Econometrics

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572 • Limited dependent variable models

a consideration of the probability that a firm would seek external financing or
not. But suppose that we wish to examine not only whether a firm decides to
issue external funds but also which method of funding it chooses when there are a
number of alternatives available. As discussed above, the pecking order hypothesis
suggests that the least costly methods, which, everything else equal, will arise
where there is least information asymmetry, will be used first, and the method
used will also depend on the riskiness of the firm. Returning to Helwege and
Liang’s study, they argue that if the pecking order is followed, low-risk firms will
issue public debt first, while moderately risky firms will issue private debt and
the most risky companies will issue equity. Since there is more than one possible
choice, this is a multiple choice problem and consequently, a binary logit model
is inappropriate and instead, a multinomial logit is used. There are three possible
choices here: bond issue, equity issue and private debt issue. As is always the case
for multinomial models, we estimate equations for one fewer than the number
of possibilities, and so equations are estimated for equities and bonds, but not for
private debt. This choice then becomes the reference point, so that the coefficients
measure the probability of issuing equity or bonds rather than private debt, and a
positive parameter estimate in, say, the equities equation implies that an increase
in the value of the variable leads to an increase in the probability that the firm will
choose to issue equity rather than private debt.

The set of explanatory variables is slightly different now given the different
nature of the problem at hand. The key variable measuring risk is now the ‘unlev-
ered Z score’, which is Altman’s Z score constructed as a weighted average of
operating earnings before interest and taxes, sales, retained earnings and work-
ing capital. All other variable names are largely self-explanatory and so are not
discussed in detail, but they are divided into two categories – those measuring
the firm’s level of risk (unlevered Z-score, debt, interest expense and variance
of earnings) and those measuring the degree of information asymmetry (R&D
expenditure, venture-backed, age, age over fifty, plant property and equipment,
industry growth, non-financial equity issuance, and assets). Firms with heavy R&D
expenditure, those receiving venture capital financing, younger firms, firms with
less property, plant and equipment, and smaller firms are argued to suffer from
greater information asymmetry. The parameter estimates for the multinomial logit
are presented in table 12.2, with equity issuance as a (0,1) dependent variable in
the second column and bond issuance as a (0,1) dependent variable in the third
column.

Overall, the results paint a very mixed picture about whether the pecking order
hypothesis is validated or not. The positive (significant) and negative (insignificant)
estimates on the unlevered Z-score and interest expense variables respectively
suggest that firms in good financial health (i.e. less risky firms) are more likely
to issue equities or bonds rather than private debt. Yet the positive sign of the
parameter on the debt variable is suggestive that riskier firms are more likely to
issue equities or bonds; the variance of earnings variable has the wrong sign but
is not statistically significant. Almost all of the asymmetric information variables
have statistically insignificant parameters. The only exceptions are that firms having
venture backing are more likely to seek capital market financing of either type,

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12.10 The pecking order hypothesis revisited • 573

Table 12.2 Multinomial logit estimation of the type of external
financing

Variable Equity equation Bonds equation

Intercept −4.67 −4.68
Unlevered Z-score (−6.17) (−5.48)
Debt
Interest expense 0.14 0.26
Variance of earnings (1.84) (2.86)
R&D
Venture-backed 1.72 3.28
Age (1.60) (2.88)
Age over fifty −9.41 −4.54
Plant, property and equipment (−0.93) (−0.42)
Industry growth −0.04 −0.14
Non-financial equity issuance (−0.55) (−1.56)
Assets 0.61 0.89
(1.28) (1.59)

0.70 0.86
(2.32) (2.50)
−0.01 −0.03
(−1.10) (−1.85)
1.58 1.93
(1.44) (1.70)

(0.62) 0.34
(0.94) (0.50)
0.003
0.005 (0.70)
(1.14)
0.005
0.008 (2.65)
(3.89)
−0.001 0.002
(−0.59) (4.11)

Notes: t-ratios in parentheses; only figures for all years in the sample are presented.
Source: Helwege and Liang (1996). Reprinted with the permission of Elsevier.

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574 • Limited dependent variable models

as are non-financial firms. Finally, larger firms are more likely to issue bonds (but
not equity). Thus the authors conclude that the results ‘do not indicate that firms
strongly avoid external financing as the pecking order predicts’ and ‘equity is not
the least desirable source of financing since it appears to dominate bank loans’
(Helwege and Liang (1996), p. 458).

• • • • • • • • • • • • 12.11 Ordered response linear dependent variables models

Some limited dependent variables can be assigned numerical values that have a
natural ordering. The most common example in finance is that of credit ratings, as
discussed previously, but a further application is to modelling a security’s bid–ask
spread (see, for example, ap Gwilym et al., 1998). In such cases, it would not be
appropriate to use multinomial logit or probit since these techniques cannot take
into account any ordering in the dependent variables. Notice that ordinal variables
are still distinct from the usual type of data that were employed in the early chapters
in this book, such as stock returns, GDP, interest rates, etc. These are examples of
cardinal numbers, since additional information can be inferred from their actual
values relative to one another. To illustrate, an increase in house prices of 20%
represents twice as much growth as a 10% rise. The same is not true of ordinal
numbers, where (returning to the credit ratings example) a rating of AAA, assigned
a numerical score of 16, is not ‘twice as good’ as a rating of Baa2/BBB, assigned a
numerical score of 8. Similarly, for ordinal data, the difference between a score of,
say, 15 and of 16 cannot be assumed to be equivalent to the difference between the
scores of 8 and 9. All we can say is that as the score increases, there is a monotonic
increase in the credit quality. Since only the ordering can be interpreted with
such data and not the actual numerical values, OLS cannot be employed and a
technique based on ML is used instead. The models used are generalisations of
logit and probit, known as ordered logit and ordered probit.

Using the credit rating example again, the model is set up so that a particular
bond falls in the AA+ category (using Standard and Poor’s terminology) if its
unobserved (latent) creditworthiness falls within a certain range that is too low to
classify it as AAA and too high to classify it as AA. The boundary values between
each rating are then estimated along with the model parameters.

• • • • • • • • • • • • 12.12 Are unsolicited credit ratings biased downwards?
An ordered probit analysis

Modelling the determinants of credit ratings is one of the most important uses
of ordered probit and logit models in finance. The main credit ratings agencies
construct what may be termed solicited ratings, which are those where the issuer of
the debt contacts the agency and pays them a fee for producing the rating. Many
firms globally do not seek a rating (because, for example, the firm believes that
the ratings agencies are not well placed to evaluate the riskiness of debt in their
country or because they do not plan to issue any debt or because they believe that
they would be awarded a low rating), but the agency may produce a rating anyway.

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12.12 Are unsolicited credit ratings biased downwards? An ordered probit analysis • 575

Such ‘unwarranted and unwelcome’ ratings are known as unsolicited ratings. All of
the major ratings agencies produce unsolicited ratings as well as solicited ones, and
they argue that there is a market demand for this information even if the issuer
would prefer not to be rated.

Companies in receipt of unsolicited ratings argue that these are biased down-
wards relative to solicited ratings and that they cannot be justified without the level
of detail of information that can be provided only by the rated company itself. A
study by Poon (2003) seeks to test the conjecture that unsolicited ratings are biased
after controlling for the rated company’s characteristics that pertain to its risk.

The data employed comprise a pooled sample of all companies that appeared
on the annual ‘issuer list’ of S&P during the years 1998–2000. This list contains
both solicited and unsolicited ratings covering 295 firms over fifteen countries
and totalling 595 observations. In a preliminary exploratory analysis of the data,
Poon finds that around half of the sample ratings were unsolicited, and indeed the
unsolicited ratings in the sample are on average significantly lower than the solicited
ratings.9 As expected, the financial characteristics of the firms with unsolicited
ratings are significantly weaker than those for firms that requested ratings. The
core methodology employs an ordered probit model with explanatory variables
comprising firm characteristics and a dummy variable for whether the firm’s credit
rating was solicited or not

Ri∗ = Xi β + i (12.16)

with

⎧ 1 if Ri∗ ≤ μ0
⎨⎪⎪⎪⎪ 2 if μ0 < Ri∗ ≤ μ1
if μ1 < Ri∗ ≤ μ2
Ri = ⎪⎪⎪⎪⎩ 3 if μ2 < Ri∗ ≤ μ3
4 if Ri∗ > μ3

5

where Ri are the observed ratings scores that are given numerical values as follows:
AA or above = 6, A = 5, BBB = 4, BB = 3, B = 2 and CCC or below =
1; Ri∗ is the unobservable ‘true rating’ (or ‘an unobserved continuous variable
representing S&P’s assessment of the creditworthiness of issuer i ’), Xi is a vector
of variables that explains the variation in ratings; β is a vector of coefficients; μi
are the threshold parameters to be estimated along with β; and i is a disturbance
term that is assumed normally distributed.

The explanatory variables attempt to capture the creditworthiness using pub-

licly available information. Two specifications are estimated: the first includes the

variables listed below, while the second additionally incorporates an interaction of

the main financial variables with a dummy variable for whether the firm’s rating

was solicited (SOL) and separately with a dummy for whether the firm is based in

9 We are assuming here that the broader credit rating categories, of which there are six, (AAA, AA,
A, BBB, BB, B) are being used rather than the finer categories used by Cantor and Packer (1996).

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576 • Limited dependent variable models

Japan.10 The financial variables are ICOV – interest coverage (i.e. earnings inter-
est), ROA – return on assets, DTC – total debt to capital, and SDTD – short-term
debt to total debt. Three variables – SOVAA, SOVA and SOVBBB – are dummy
variables that capture the debt issuer’s sovereign credit rating.11 Table 12.3 presents
the results from the ordered probit estimation.

The key finding is that the SOL variable is positive and statistically significant
in Model 1 (and it is positive but insignificant in Model 2), indicating that even
after accounting for the financial characteristics of the firms, unsolicited firms
receive ratings on average 0.359 units lower than an otherwise identical firm that
had requested a rating. The parameter estimate for the interaction term between
the solicitation and Japanese dummies (SOL∗JP) is positive and significant in
both specifications, indicating strong evidence that Japanese firms soliciting ratings
receive higher scores. On average, firms with stronger financial characteristics
(higher interest coverage, higher return on assets, lower debt to total capital, or a
lower ratio of short-term debt to long-term debt) have higher ratings.

A major flaw that potentially exists within the above analysis is the self-selection
bias or sample selection bias that may have arisen if firms that would have received
lower credit ratings (because they have weak financials) elect not to solicit a rating.
If the probit equation for the determinants of ratings is estimated ignoring this
potential problem and it exists, the coefficients will be inconsistent. To get around
this problem and to control for the sample selection bias, Heckman (1979) proposed
a two-step procedure that in this case would involve first estimating a 0–1 probit
model for whether the firm chooses to solicit a rating and second estimating the
ordered probit model for the determinants of the rating. The first-stage probit
model is

Yi∗ = Zi γ + ξi (12.17)

where Yi = 1 if the firm has solicited a rating and 0 otherwise, and Yi∗ denotes the
latent propensity of issuer i to solicit a rating, Zi are the variables that explain the

choice to be rated or not, and γ are the parameters to be estimated. When this

equation has been estimated, the rating Ri as defined above in equation (12.16)
will be observed only if Yi = 1. The error terms from the two equations, i and
ξi , follow a bivariate standard normal distribution with correlation ρ ξ . Table 12.4

shows the results from the two-step estimation procedure, with the estimates from

the binary probit model for the decision concerning whether to solicit a rating in

panel A and the determinants of ratings for rated firms in panel B.

A positive parameter value in panel A indicates that higher values of the

associated variable increases the probability that a firm will elect to be rated. Of

the four financial variables, only the return on assets and the short-term debt as a

10 The Japanese dummy is used since a disproportionate number of firms in the sample are from
this country.

11 So SOVAA = 1 if the sovereign (i.e. the government of that country) has debt with a rating of
AA or above and 0 otherwise; SOVA has a value 1 if the sovereign has a rating of A; and SOVBBB
has a value 1 if the sovereign has a rating of BBB; any firm in a country with a sovereign whose
rating is below BBB is assigned a zero value for all three sovereign rating dummies.

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12.12 Are unsolicited credit ratings biased downwards? An ordered probit analysis • 577

Table 12.3 Ordered probit model results for the determinants of credit ratings

Model 1 Model 2

Explanatory Coefficient Test statistic Coefficient Test statistic
variables
2.324 8.960∗∗∗ 1.492 3.155∗∗∗
Intercept 0.359 2.105∗∗ 0.391 0.647
SOL −0.548 −2.949∗∗∗ 1.296 2.441∗∗
JP 1.614 7.027∗∗∗ 1.487 5.183∗∗∗
JP∗SOL 2.135 8.768∗∗∗ 2.470 8.975∗∗∗
SOVAA 0.554 2.552∗∗ 0.925 3.968∗∗∗
SOVA −0.416 −1.480 −0.181 −0.601
SOVBBB 0.023 3.466∗∗∗ −0.005 −0.172
ICOV 0.104 10.306∗∗∗ 0.194 2.503∗∗
ROA −1.393 −5.736∗∗∗ −0.522 −1.130
DTC −1.212 −5.228∗∗∗ 0.111 0.171
SDTD 0.005 0.163
SOL∗ICOV – – −0.116 −1.476
SOL∗ROA – – 0.756 1.136
SOL∗DTC – – −0.887 −1.290
SOL∗SDTD – – 0.009 0.275
JP∗ICOV – – 0.183 2.200∗∗
JP∗ROA – – −1.865 −3.214∗∗∗
JP∗DTC – – −2.443 −3.437∗∗∗
JP∗SDTD – –

AA or above >5.095 25.278∗∗∗ >5.578 23.294∗∗∗
A >3.788 and ≤5.095 19.671∗∗∗ >4.147 and ≤5.578 19.204∗∗∗
BBB >2.550 and ≤3.788 14.342∗∗∗ >2.803 and ≤4.147 14.324∗∗∗
BB >1.287 and ≤2.550 7.927∗∗∗ >1.432 and ≤2.803
B >0 and ≤1.287 >0 and ≤1.432 7.910∗∗∗
CCC or below ≤0 ≤0

Note: ∗, ∗∗ and ∗∗∗ denote significance at the 10%, 5% and 1% levels respectively.
Source: Poon (2003). Reprinted with the permission of Elsevier.

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578 • Limited dependent variable models

Table 12.4 Two-step ordered probit model allowing for selectivity
bias in the determinants of credit ratings

Explanatory variable Coefficient Test statistic

Panel A: Decision to be rated 1.624 3.935∗∗∗
Intercept −0.776 −4.951∗∗∗
JP −0.959 −2.706∗∗∗
SOVAA −0.614 −1.794∗
SOVA −1.130 −2.899∗∗∗
SOVBBB −0.005 −0.922
ICOV
ROA 0.051 6.537∗∗∗
DTC 0.272
SDTD −1.651 1.019
−5.320∗∗∗

Panel B: Rating determinant equation 1.368 2.890∗∗∗
Intercept 2.456 3.141∗∗∗
JP 2.315 6.121∗∗∗
SOVAA 0.875 2.755∗∗∗
SOVA 0.306 0.768
SOVBBB 0.002 0.118
ICOV 0.038 2.408∗∗
ROA −0.330 −0.512
DTC 0.105 0.303
SDTD 0.038 1.129
JP∗ICOV 0.188 2.104∗∗
JP∗ROA −0.808 −0.924
JP∗DTC −2.823 −2.430∗∗
JP∗SDTD
−0.836 −5.723∗∗∗
Estimated correlation >4.275
AA or above >2.841 and ≤4.275 8.235∗∗∗
A >1.748 and ≤2.841 9.164∗∗∗
BBB >0.704 and ≤1.748 6.788∗∗∗
BB >0 and ≤0.704 3.316∗∗∗
B ≤0
CCC or below

Note: ∗, ∗∗ and ∗∗∗ denote significance at the 10%, 5% and 1% levels respectively.
Source: Poon (2003). Reprinted with the permission of Elsevier.

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12.13 Censored and truncated dependent variables • 579

proportion of total debt have correctly signed and significant (positive and negative
respectively) impacts on the decision to be rated. The parameters on the sovereign
credit rating dummy variables (SOVAA, SOVA and SOVB) are all significant and
negative in sign, indicating that any debt issuer in a country with a high sovereign
rating is less likely to solicit its own rating from S&P, other things equal.

These sovereign rating dummy variables have the opposite sign in the ratings
determinant equation (panel B) as expected, so that firms in countries where gov-
ernment debt is highly rated are themselves more likely to receive a higher rating.
Of the four financial variables, only ROA has a significant (and positive) effect on
the rating awarded. The dummy for Japanese firms is also positive and significant,
and so are three of the four financial variables when interacted with the Japan
dummy, indicating that S&P appears to attach different weights to the financial
variables when assigning ratings to Japanese firms compared with comparable firms
in other countries.

Finally, the estimated correlation between the error terms in the decision to
be rated equation and the ratings determinant equation, ρ ξ , is significant and
negative (−0.836), indicating that the results in table 12.3 above would have been
subject to self-selection bias and hence the results of the two-stage model are to be
preferred. The only disadvantage of this approach, however, is that by construction
it cannot answer the core question of whether unsolicited ratings are on average
lower after allowing for the debt issuer’s financial characteristics, because only firms
with solicited ratings are included in the sample at the second stage!

• • • • • • • • • • • • 12.13 Censored and truncated dependent variables

Censored or truncated variables occur when the range of values observable for
the dependent variables is limited for some reason. Unlike the types of limited
dependent variables examined so far in this chapter, censored or truncated variables
may not necessarily be dummies. A standard example is that of charitable donations
by individuals. It is likely that some people would actually prefer to make negative
donations (that is, to receive from the charity rather than to donate to it), but since
this is not possible, there will be many observations at exactly zero. So suppose, for
example, that we wished to model the relationship between donations to charity
and people’s annual income, in pounds. The situation we might face is illustrated
in figure 12.3.

Given the observed data, with many observations on the dependent variable
stuck at zero, OLS would yield biased and inconsistent parameter estimates. An
obvious but flawed way to get around this would be just to remove all of the
zero observations altogether, since we do not know whether they should be truly
zero or negative. However, as well as being inefficient (since information would
be discarded), this would still yield biased and inconsistent estimates. This arises
because the error term, ui , in such a regression would not have an expected value
of zero, and it would also be correlated with the explanatory variable(s), violating
the assumption that Cov (ui , xki ) = 0 ∀ k.

The key differences between censored and truncated data are highlighted in
box 12.2. For both censored and truncated data, OLS will not be appropriate, and

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580 • Limited dependent variable models

True (unobservable)
Fitted line

Probability of making a donation Income

Figure 12.3 Modelling charitable donations as a function of income

an approach based on maximum likelihood must be used, although the model in
each case would be slightly different. In both cases, we can work out the marginal
effects given the estimated parameters, but these are now more complex than in
the logit or probit cases.

12.13.1 Censored dependent variable models

The approach usually used to estimate models with censored dependent variables
is known as tobit analysis, named after Tobin (1958). To illustrate, suppose that we
wanted to model the demand for privatisation IPO shares, as discussed above, as a
function of income (x2i ), age (x3i ), education (x4i ) and region of residence (x5i ).
The model would be

yi∗ = β1 + β2x2i + β3x3i + β4x4i + β5x5i + ui

yi = yi∗ for yi∗ < 250 (12.18)

yi = 250 for yi∗ ≥ 250

yi∗ represents the true demand for shares (i.e. the number of shares requested) and
this will be observable only for demand less than 250. It is important to note in this

model that β2, β3, etc. represent the impact on the number of shares demanded (of
a unit change in x2i , x3i , etc.) and not the impact on the actual number of shares

that will be bought (allocated).

An interesting financial application of the tobit approach is due to Haushalter

(2000), who employs it to model the determinants of the extent of hedging by oil

and gas producers using futures or options over the 1992–4 period. The dependent

variable used in the regression models, the proportion of production hedged, is

clearly censored because around half of all of the observations are exactly zero

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12.13 Censored and truncated dependent variables • 581

Box 12.2 The differences between censored and truncated
dependent variables

Although at first sight the two words might appear interchangeable, when
the terms are used in econometrics, censored and truncated data are
different.

● Censored data occur when the dependent variable has been ‘censored’ at
a certain point so that values above (or below) this cannot be observed.
Even though the dependent variable is censored, the corresponding
values of the independent variables are still observable.

● As an example, suppose that a privatisation IPO is heavily
oversubscribed, and you were trying to model the demand for the shares
using household income, age, education and region of residence as
explanatory variables. The number of shares allocated to each investor
may have been capped at, say, 250, resulting in a truncated distribution.

● In this example, even though we are likely to have many share
allocations at 250 and none above this figure, all of the observations on
the independent variables are present and hence the dependent variable
is censored, not truncated.

● A truncated dependent variable, meanwhile, occurs when the
observations for both the dependent and the independent variables are
missing when the dependent variable is above (or below) a certain
threshold. Thus the key difference from censored data is that we cannot
observe the xi s either, and so some observations are completely cut out
or truncated from the sample. For example, suppose that a bank were
interested in determining the factors (such as age, occupation and
income) that affected a customer’s decision as to whether to undertake
a transaction in a branch or online. Suppose also that the bank tried to
achieve this by encouraging clients to fill in an online questionnaire
when they log on. There would be no data at all for those who opted to
transact in person since they probably would not have even logged on
to the bank’s web-based system and so would not have the opportunity
to complete the questionnaire. Thus, dealing with truncated data is
really a sample selection problem because the sample of data that can be
observed is not representative of the population of interest – the sample
is biased, very likely resulting in biased and inconsistent parameter
estimates. This is a common problem, which will result whenever data
for buyers or users only can be observed while data for non-buyers or
non-users cannot. Of course, it is possible, although unlikely, that the
population of interest is focused only on those who use the internet for
banking transactions, in which case there would be no problem.

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582 • Limited dependent variable models

(i.e. the firm does not hedge at all).12 The censoring of the proportion of produc-
tion hedged may arise because of high fixed costs that prevent many firms from
being able to hedge even if they wished to. Moreover, if companies expect the
price of oil or gas to rise in the future, they may wish to increase rather than reduce
their exposure to price changes (i.e. ‘negative hedging’), but this would not be
observable given the way that the data are constructed in the study.

The main results from the study are that the proportion of exposure hedged
is negatively related to creditworthiness, positively related to indebtedness, to the
firm’s marginal tax rate, and to the location of the firm’s production facility. The
extent of hedging is not, however, affected by the size of the firm as measured by
its total assets.

Before moving on, two important limitations of tobit modelling should be
noted. First, such models are much more seriously affected by non-normality and
heteroscedasticity than are standard regression models (see Amemiya, 1984), and
biased and inconsistent estimation will result. Second, as Kennedy (2003, p. 283)
argues, the tobit model requires it to be plausible that the dependent variable can
have values close to the limit. There is no problem with the privatisation IPO
example discussed above since the demand could be for 249 shares. However, it
would not be appropriate to use the tobit model in situations where this is not the
case, such as the number of shares issued by each firm in a particular month. For
most companies, this figure will be exactly zero, but for those where it is not, the
number will be much higher and thus it would not be feasible to issue, say, one or
three or fifteen shares. In this case, an alternative approach should be used.

12.13.2 Truncated dependent variable models

For truncated data, a more general model is employed that contains two equations –
one for whether a particular data point will fall into the observed or constrained
categories and another for modelling the resulting variable. The second equation
is equivalent to the tobit approach. This two-equation methodology allows for a
different set of factors to affect the sample selection (for example, the decision to
set up internet access to a bank account) from the equation to be estimated (for
example, to model the factors that affect whether a particular transaction will be
conducted online or in a branch). If it is thought that the two sets of factors will be
the same, then a single equation can be used and the tobit approach is sufficient.
In many cases, however, the researcher may believe that the variables in the sample
selection and estimation equations should be different. Thus the equations could
be

a ∗ = α1 + α2z2i + α3z3i +··· + αm zmi + εi (12.19)
i (12.20)

yi∗ = β1 + β2x2i + β3x3i + · · · + βk xki + ui

12 Note that this is an example of a censored rather than a truncated dependent variable because the
values of all of the explanatory variables are still available from the annual accounts even if a firm
does not hedge at all.

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12.14 Limited dependent variable models in EViews • 583

where yi = yi∗ for a ∗ > 0 and, yi is unobserved for a ∗ ≤ 0. a ∗ denotes the rela-
i i i

tive ‘advantage’ of being in the observed sample relative to the unobserved sample.

The first equation determines whether the particular data point i will be

observed or not, by regressing a proxy for the latent (unobserved) variable a ∗ on
i

a set of factors, zi . The second equation is similar to the tobit model. Ideally, the

two equations (12.19) and (12.20) will be fitted jointly by maximum likelihood.

This is usually based on the assumption that the error terms, εi and ui , are mul-
tivariate normally distributed and allowing for any possible correlations between

them. However, while joint estimation of the equations is more efficient, it is

computationally more complex and hence a two-stage procedure popularised by

Heckman (1976) is often used. The Heckman procedure allows for possible cor-

relations between εi and ui while estimating the equations separately in a clever

way – see Maddala (1983).

• • • • • • • • • • • • 12.14 Limited dependent variable models in EViews

Estimating limited dependent variable models in EViews is very simple. The
example that will be considered here concerns whether it is possible to determine
the factors that affect the likelihood that a student will fail his/her MSc. The data
comprise a sample from the actual records of failure rates for five years of MSc
students in finance at the ICMA Centre, University of Reading contained in the
spreadsheet ‘MSc fail.xls’. While the values in the spreadsheet are all genuine, only
a sample of 100 students is included for each of five years who completed (or not as
the case may be!) their degrees in the years 2003 to 2007 inclusive. Therefore, the
data should not be used to infer actual failure rates on these programmes. The idea
for this example is taken from a study by Heslop and Varotto (2007) which seeks
to propose an approach to preventing systematic biases in admissions decisions.13

The objective here is to analyse the factors that affect the probability of failure
of the MSc. The dependent variable (‘fail’) is binary and takes the value 1 if that
particular candidate failed at first attempt in terms of his/her overall grade and 0
elsewhere. Therefore, a model that is suitable for limited dependent variables is
required, such as a logit or probit.

The other information in the spreadsheet that will be used includes the age
of the student, a dummy variable taking the value 1 if the student is female, a
dummy variable taking the value 1 if the student has work experience, a dummy
variable taking the value 1 if the student’s first language is English, a country
code variable that takes values from 1 to 10,14 a dummy variable that takes the
value 1 if the student already has a postgraduate degree, a dummy variable that
takes the value 1 if the student achieved an A-grade at the undergraduate level

13 Note that since this book uses only a sub-set of their sample and variables in the analysis, the
results presented below may differ from theirs. Since the number of fails is relatively small, I
deliberately retained as many fail observations in the sample as possible, which will bias the
estimated failure rate upwards relative to the true rate.

14 The exact identities of the countries involved are not revealed in order to avoid any embarrassment
for students from countries with high relative failure rates, except that Country 8 is the UK!

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584 • Limited dependent variable models

Screenshot 12.1 Equation estimation window for limited dependent variables

(i.e. a first-class honours degree or equivalent), and a dummy variable that takes
the value 1 if the undergraduate grade was less than a B-grade (i.e. the student
received the equivalent of a lower second-class degree). The B-grade (or upper
second-class degree) is the omitted dummy variable and this will then become the
reference point against which the other grades are compared – see chapter 9. The
reason why these variables ought to be useful predictors of the probability of failure
should be fairly obvious and is therefore not discussed. To allow for differences
in examination rules and in average student quality across the five-year period,
year dummies for 2004, 2005, 2006 and 2007 are created and thus the year 2003
dummy will be omitted from the regression model.

First, open a new workfile that can accept ‘unstructured/undated’ series
of length 500 observations and then import the 13 variables. The data are
organised by observation and start in cell A2. The country code variable will
require further processing before it can be used but the others are already in
the appropriate format, so to begin, suppose that we estimate an LPM of fail on a
constant, age, English, female and work experience. This would be achieved simply
by running a linear regression in the usual way. While this model has a number
of very undesirable features as discussed above, it would nonetheless provide a
useful benchmark with which to compare the more appropriate models estimated
below.

Next, estimate a probit model and a logit model using the same depen-
dent and independent variables as above. Choose Quick and then Equation

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12.14 Limited dependent variable models in EViews • 585

Screenshot 12.2 Equation estimation options for limited dependent variables

Estimation. Then type the dependent variable followed by the explanatory
variables

FAIL C AGE ENGLISH FEMALE WORK EXPERIENCE AGRADE
BELOWBGRADE PG DEGREE YEAR2004 YEAR2005 YEAR2006
YEAR2007

and then in the second window, marked ‘Estimation settings’, select BINARY –
Binary Choice (Logit, Probit, Extreme Value) with the whole sample 1 500.
The screen will appear as in screenshot 12.1.

You can then choose either the probit or logit approach. Note that EViews
also provides support for truncated and censored variable models and for multiple
choice models, and these can be selected from the drop-down menu by choosing
the appropriate method under ‘estimation settings’. Suppose that here we wish to
choose a probit model (the default). Click on the Options tab at the top of the
window and this enables you to select Robust Covariances and Huber/White.
This option will ensure that the standard error estimates are robust to heteroscedas-
ticity (see screenshot 12.2).

There are other options to change the optimisation method and convergence
criterion, as discussed in chapter 8. We do not need to make any modifications
from the default here, so click OK and the results will appear. Freeze and name
this table and then, for completeness, estimate a logit model. The results that
you should obtain for the probit model are in the following table.

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586 • Limited dependent variable models

Dependent Variable: FAIL
Method: ML – Binary Probit (Quadratic hill climbing)
Date: 08/04/07 Time: 19:10
Sample: 1 500
Included observations: 500
Convergence achieved after 5 iterations
QML (Huber/White) standard errors & covariance

Coefficient Std. Error z-Statistic Prob.

C −1.287210 0.609503 −2.111901 0.0347
AGE 0.005677 0.022559 0.251648 0.8013
ENGLISH 0.156226 0.5483
FEMALE −0.093792 0.186201 −0.600362 0.2972
WORK EXPERIENCE −0.194107 0.151333 −1.042460 0.0355
AGRADE −0.318247 0.231148 −2.102956 0.0198
BELOWBGRADE −0.538814 0.219301 −2.331038 0.1191
PG DEGREE 0.225925 0.5562
YEAR2004 0.341803 0.241450 1.558601 0.1476
YEAR2005 0.132957 0.268527 0.588502 0.6866
YEAR2006 0.349663 0.238536 1.448181 0.0047
YEAR2007 −0.108330 −0.403422 0.0802
0.673612 0.24793 2.823944
0.433785 1.749630 0.134000
0.333221
McFadden R-squared 0.088870 Mean dependent var 54.18582
S.D. dependent var 0.340993 S.E. of regression −179.4563
Akaike info criterion 0.765825 Sum squared resid −196.9602
Schwarz criterion 0.866976 Log likelihood −0.358913
Hannan-Quinn criter. 0.805517 Restr. log likelihood
LR statistic 35.00773 Avg. log likelihood 500
Prob(LR statistic) 0.000247

Obs with Dep=0 433 Total obs
Obs with Dep=1 67

As can be seen, the pseudo-R2 values are quite small at just below 9%, although this
is often the case for limited dependent variable models. Only the work experience
and A-grade variables and two of the year dummies have parameters that are
statistically significant, and the Below B-grade dummy is almost significant at the
10% level in the probit specification (although less so in the logit). As the final two
rows of the tables note, the proportion of fails in this sample is quite small, which
makes it harder to fit a good model than if the proportions of passes and fails had
been more evenly balanced. Various goodness of fit statistics can be examined by
(from the logit or probit estimation output window) clicking View/Goodness-
of-fit Test (Hosmer-Lemeshow). A further check on model adequacy is to
produce a set of ‘in-sample forecasts’ – in other words, to construct the fitted
values. To do this, click on the Forecast tab after estimating the probit model

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12.14 Limited dependent variable models in EViews • 587

.6

.5

.4

.3

.2

.1

.0 500
50 100 150 200 250 300 350 400 450

Figure 12.4 Fitted values from the failure probit regression

and then uncheck the forecast evaluation box in the ‘Output’ window as the
evaluation is not relevant in this case. All other options can be left as the default
settings and then the plot of the fitted values shown on figure 12.4 results.

The unconditional probability of failure for the sample of students we have is
only 13.4% (i.e. only 67 out of 500 failed), so an observation should be classified as
correctly fitted if either yi = 1 and yˆi > 0.134 or yi = 0 and yˆi < 0.134. The easi-
est way to evaluate the model in EViews is to click View/Actual,Fitted,Residual
Table from the logit or probit output screen. Then from this information we can
identify that of the 67 students that failed, the model correctly predicted 46 of
them to fail (and it also incorrectly predicted that 21 would pass). Of the 433
students who passed, the model incorrectly predicted 155 to fail and correctly pre-
dicted the remaining 278 to pass. Eviews can construct an ‘expectation-prediction
classification table’ automatically by clicking on View/Expectation-Prediction
Evaluation and then entering the unconditional probability of failure as the cutoff
when prompted (0.134). Overall, we could consider this a reasonable set of (in
sample) predictions with 64.8% of the total predictions correct, comprising 64.2%
of the passes correctly predicted as passes and 68.66% of the fails correctly predicted
as fails.

It is important to note that, as discussed above, we cannot interpret the param-
eter estimates in the usual way. In order to be able to do this, we need to calculate
the marginal effects. Unfortunately, EViews does not do this automatically, so the
procedure is probably best achieved in a spreadsheet using the approach described
in box 12.1 for the logit model and analogously for the probit model. If we did this,
we would end up with the statistics displayed in table 12.5, which are interestingly
quite similar in value to those obtained from the linear probability model.

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588 • Limited dependent variable models

Table 12.5 Marginal effects for logit and probit models for
probability of MSc failure

Parameter Logit Probit

C −0.2433 −0.1646
AGE 0.0012 0.0007
ENGLISH
FEMALE −0.0178 −0.0120
WORK EXPERIENCE −0.0360 −0.0248
AGRADE −0.0613 −0.0407
BELOWBGRADE −0.1170 −0.0689
PG DEGREE
YEAR2004 0.0606 0.0437
YEAR2005 0.0229 0.0170
YEAR2006 0.0704 0.0447
YEAR2007 −0.0198 −0.0139
0.1344 0.0862
0.0917 0.0555

This table presents us with values that can be intuitively interpreted in terms of
how the variables affect the probability of failure. For example, an age parameter
value of 0.0012 implies that an increase in the age of the student by one year would
increase the probability of failure by 0.12%, holding everything else equal, while a
female student is around 2.5–3% (depending on the model) less likely than a male
student with otherwise identical characteristics to fail. Having an A-grade (first
class) in the bachelors degree makes a candidate either 6.89% or 12.7% (depending
on the model) less likely to fail than an otherwise identical student with a B-grade
(upper second-class degree). Finally, since the year 2003 dummy has been omitted
from the equations, this becomes the reference point. So students were more likely
in 2004, 2006 and 2007, but less likely in 2005, to fail the MSc than in 2003.

Key concepts

The key terms to be able to define and explain from this chapter are

• limited dependent variables • logit
• probit • censored variables
• truncated variables • ordered response
• multinomial logit • marginal effects
• pseudo-R2

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Appendix: The maximum likelihood estimator • 589

Self-study questions

1. Explain why the linear probability model is inadequate as a specification for
limited dependent variable estimation.

2. Compare and contrast the probit and logit specifications for binary choice
variables.

3. (a) Describe the intuition behind the maximum likelihood estimation
technique used for limited dependent variable models.

(b) Why do we need to exercise caution when interpreting the coefficients of
a probit or logit model?

(c) How can we measure whether a logit model that we have estimated fits
the data well or not?

(d) What is the difference, in terms of the model setup, in binary choice
versus multiple choice problems?

4. (a) Explain the difference between a censored variable and a truncated
variable as the terms are used in econometrics.

(b) Give examples from finance (other than those already described in this
book) of situations where you might meet each of the types of variable
described in part (a) of this question.

(c) With reference to your examples in part (b), how would you go about
specifying such models and estimating them?

5. Re-open the ‘fail xls’ spreadsheet for modelling the probability of MSc
failure and do the following:
(a) Take the country code series and construct separate dummy variables for
each country. Re-run the probit and logit regression above with all of the
other variables plus the country dummy variables. Set up the regression so
that the UK becomes the reference point against which the effect on
failure rate in other countries is measured. Is there evidence that any
countries have significantly higher or lower probabilities of failure than
the UK, holding all other factors in the model constant? In the case of the
logit model, use the approach given in box 12.1 to evaluate the
differences in failure rates between the UK and each other country.
(b) Suppose that a fellow researcher suggests that there may be a non-linear
relationship between the probability of failure and the age of the student.
Estimate a probit model with all of the same variables as above plus an
additional one to test this. Is there indeed any evidence of such a
nonlinear relationship?

Appendix The maximum likelihood estimator for logit and probit models

Recall that under the logit formulation, the estimate of the probability that yi = 1
will be given from equation (12.4), which was

1 (12A.1)
Pi = 1 + e −(β1 +β2x2i +...+βk xki +ui )

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590 • Limited dependent variable models

Set the error term, ui , to its expected value for simplicity and again, let zi =
β1 + β2x2i + · · · + βk xki , so that we have

1 (12A.2)
Pi = 1 + e −zi

We will also need the probability that yi = 1 or equivalently the probability
that yi = 0. This will be given by 1 minus the probability in (12A.2).15 Given
that we can have actual zeros and ones only for yi rather than probabilities, the
likelihood function for each observation yi will be

1 yi 1 (1−yi )
Li = 1 + e −zi × (12A.3)
1 + e zi

The likelihood function that we need will be based on the joint probability

for all N observations rather than an individual observation i . Assuming that

each observation on yi is independent, the joint likelihood will be the product
of all N marginal likelihoods. Let L (θ |x2i , x3i , . . . , xki ; i = 1, N ) denote the
likelihood function of the set of parameters (β1, β2, . . . , βk) given the data. Then
the likelihood function will be given by

N 1 yi 1 (1−yi )
1 + e −zi 1 + e zi
L (θ ) = × (12A.4)

i =1

As for maximum likelihood estimator of GARCH models, it is computation-
ally much simpler to maximise an additive function of a set of variables than a
multiplicative function, so long as we can ensure that the parameters required to
achieve this will be the same. We thus take the natural logarithm of equation
(12A.4) and this log-likelihood function is maximised

N (12A.5)

LLF = − [yi ln(1 + e −zi ) + (1 − yi ) ln(1 + e zi )]

i =1

Estimation for the probit model will proceed in exactly the same way, except
that the form for the likelihood function in (12A.4) will be slightly different. It
will instead be based on the familiar normal distribution function described in the
appendix to chapter 9.

15 We can use the rule that

1 − 1 = 1 + e −zi − 1 = e −zi = e −zi = e −zi × e zi = 1 .
+ e −zi 1 + e −zi 1 + e −zi 1 + e zi 1 + e zi
1 1 + 1
e zi

13 Simulation methods

Learning outcomes

In this chapter, you will learn how to
• Design simulation frameworks to solve a variety of problems in finance
• Explain the difference between pure simulation and bootstrapping
• Describe the various techniques available for reducing Monte Carlo sampling

variability
• Implement a simulation analysis in EViews

• • • • • • • • • • • • 13.1 Motivations

There are numerous situations, in finance and in econometrics, where the
researcher has essentially no idea what is going to happen! To offer one illustration,
in the context of complex financial risk measurement models for portfolios con-
taining large numbers of assets whose movements are dependent on one another, it
is not always clear what will be the effect of changing circumstances. For example,
following full European Monetary Union (EMU) and the replacement of mem-
ber currencies with the euro, it is widely believed that European financial markets
have become more integrated, leading the correlation between movements in their
equity markets to rise. What would be the effect on the properties of a portfolio
containing equities of several European countries if correlations between the mar-
kets rose to 99%? Clearly, it is probably not possible to be able to answer such a
question using actual historical data alone, since the event (a correlation of 99%)
has not yet happened.

The practice of econometrics is made difficult by the behaviour of series and
inter-relationships between them that render model assumptions at best question-
able. For example, the existence of fat tails, structural breaks and bi-directional
causality between dependent and independent variables, etc. will make the pro-
cess of parameter estimation and inference less reliable. Real data is messy, and
no one really knows all of the features that lurk inside it. Clearly, it is important

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592 • Simulation methods

for researchers to have an idea of what the effects of such phenomena will be for
model estimation and inference.

By contrast, simulation is the econometrician’s chance to behave like a ‘real sci-
entist’, conducting experiments under controlled conditions. A simulations exper-
iment enables the econometrician to determine what the effect of changing one
factor or aspect of a problem will be, while leaving all other aspects unchanged.
Thus, simulations offer the possibility of complete flexibility. Simulation may be
defined as an approach to modelling that seeks to mimic a functioning system as it
evolves. The simulations model will express in mathematical equations the assumed
form of operation of the system. In econometrics, simulation is particularly useful
when models are very complex or sample sizes are small.

• • • • • • • • • • • • 13.2 Monte Carlo simulations

Simulations studies are usually used to investigate the properties and behaviour of
various statistics of interest. The technique is often used in econometrics when the
properties of a particular estimation method are not known. For example, it may
be known from asymptotic theory how a particular test behaves with an infinite
sample size, but how will the test behave if only fifty observations are available?
Will the test still have the desirable properties of being correctly sized and having
high power? In other words, if the null hypothesis is correct, will the test lead to
rejection of the null 5% of the time if a 5% rejection region is used? And if the
null is incorrect, will it be rejected a high proportion of the time?

Examples from econometrics of where simulation may be useful include:

● Quantifying the simultaneous equations bias induced by treating an endoge-
nous variable as exogenous

● Determining the appropriate critical values for a Dickey–Fuller test
● Determining what effect heteroscedasticity has upon the size and power of a

test for autocorrelation.

Simulations are also often extremely useful tools in finance, in situations such as:

● The pricing of exotic options, where an analytical pricing formula is unavail-
able

● Determining the effect on financial markets of substantial changes in the
macroeconomic environment

● ‘Stress-testing’ risk management models to determine whether they generate
capital requirements sufficient to cover losses in all situations.

In all of these instances, the basic way that such a study would be conducted (with
additional steps and modifications where necessary) is shown in box 13.1.

A brief explanation of each of these steps is in order. The first stage involves
specifying the model that will be used to generate the data. This may be a pure time
series model or a structural model. Pure time series models are usually simpler to
implement, as a full structural model would also require the researcher to specify
a data generating process for the explanatory variables as well. Assuming that a
time series model is deemed appropriate, the next choice to be made is of the

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13.3 Variance reduction techniques • 593

Box 13.1 Conducting a Monte Carlo simulation

(1) Generate the data according to the desired data generating process
(DGP), with the errors being drawn from some given distribution

(2) Do the regression and calculate the test statistic
(3) Save the test statistic or whatever parameter is of interest
(4) Go back to stage 1 and repeat N times.

probability distribution specified for the errors. Usually, standard normal draws are
used, although any other empirically plausible distribution (such as a Student’s t)
could also be used.

The second stage involves estimation of the parameter of interest in the study.
The parameter of interest might be, for example, the value of a coefficient in a
regression, or the value of an option at its expiry date. It could instead be the value
of a portfolio under a particular set of scenarios governing the way that the prices
of the component assets move over time.

The quantity N is known as the number of replications, and this should be as
large as is feasible. The central idea behind Monte Carlo is that of random sampling
from a given distribution. Therefore, if the number of replications is set too small,
the results will be sensitive to ‘odd’ combinations of random number draws. It is
also worth noting that asymptotic arguments apply in Monte Carlo studies as well
as in other areas of econometrics. That is, the results of a simulation study will be
equal to their analytical counterparts (assuming that the latter exist) asymptotically.

• • • • • • • • • • • • 13.3 Variance reduction techniques

Suppose that the value of the parameter of interest for replication i is denoted
xi . If the average value of this parameter is calculated for a set of, say, N = 1,000
replications, and another researcher conducts an otherwise identical study with
different sets of random draws, a different average value of x is almost certain
to result. This situation is akin to the problem of selecting only a sample of
observations from a given population in standard regression analysis. The sampling
variation in a Monte Carlo study is measured by the standard error estimate,
denoted Sx

Sx = var(x) (13.1)
N

where var(x) is the variance of the estimates of the quantity of interest over the
N replications. It can be seen from this equation that to reduce the Monte Carlo
standard error by a factor of 10, the number of replications must be increased
by a factor of 100. Consequently, in order to achieve acceptable accuracy, the
number of replications may have to be set at an infeasibly high level. An alter-
native way to reduce Monte Carlo sampling error is to use a variance reduction

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594 • Simulation methods

technique. There are many variance reduction techniques available. Two of the
intuitively simplest and most widely used methods are the method of antithetic
variates and the method of control variates. Both of these techniques will now be
described.

13.3.1 Antithetic variates

One reason that a lot of replications are typically required of a Monte Carlo
study is that it may take many, many repeated sets of sampling before the entire
probability space is adequately covered. By their very nature, the values of the
random draws are random, and so after a given number of replications, it may be
the case that not the whole range of possible outcomes has actually occurred.1
What is really required is for successive replications to cover different parts of the
probability space – that is, for the random draws from different replications to
generate outcomes that span the entire spectrum of possibilities. This may take a
long time to achieve naturally.

The antithetic variate technique involves taking the complement of a set of
random numbers and running a parallel simulation on those. For example, if the
driving stochastic force is a set of T N(0, 1) draws, denoted ut , for each replication,
an additional replication with errors given by −ut is also used. It can be shown
that the Monte Carlo standard error is reduced when antithetic variates are used.
For a simple illustration of this, suppose that the average value of the parameter of
interest across two sets of Monte Carlo replications is given by

x¯ = (x1 + x2)/2 (13.2)

where x1 and x2 are the average parameter values for replications sets 1 and 2,
respectively. The variance of x¯ will be given by

var(x¯ ) = 1 (var(x1) + var(x2) + 2cov(x1, x2)) (13.3)
4

If no antithetic variates are used, the two sets of Monte Carlo replications will be
independent, so that their covariance will be zero, i.e.

var(x¯ ) = 1 (var(x1) + var(x2)) (13.4)
4

However, the use of antithetic variates would lead the covariance in (13.3) to be

negative, and therefore the Monte Carlo sampling error to be reduced.

It may at first appear that the reduction in Monte Carlo sampling variation
from using antithetic variates will be huge since, by definition, corr(ut , −ut ) =
cov(ut , −ut ) = −1. However, it is important to remember that the relevant
covariance is between the simulated quantity of interest for the standard replications

1 Obviously, for a continuous random variable, there will be an infinite number of possible values.
In this context, the problem is simply that if the probability space is split into arbitrarily small
intervals, some of those intervals will not have been adequately covered by the random draws that
were actually selected.

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13.3 Variance reduction techniques • 595

and those using the antithetic variates. But the perfect negative covariance is
between the random draws (i.e. the error terms) and their antithetic variates. For
example, in the context of option pricing (discussed below), the production of
a price for the underlying security (and therefore for the option) constitutes a
non-linear transformation of ut . Therefore the covariances between the terminal
prices of the underlying assets based on the draws and based on the antithetic
variates will be negative, but not −1.

Several other variance reduction techniques that operate using similar prin-
ciples are available, including stratified sampling, moment-matching and low-
discrepancy sequencing. The latter are also known as quasi-random sequences of
draws. These involve the selection of a specific sequence of representative samples
from a given probability distribution. Successive samples are selected so that the
unselected gaps left in the probability distribution are filled by subsequent replica-
tions. The result is a set of random draws that are appropriately distributed across
all of the outcomes of interest. The use of low-discrepancy sequences leads the
Monte Carlo standard errors to be reduced in direct proportion to the number
of replications rather than in proportion to the square root of the number of
replications. Thus, for example, to reduce the Monte Carlo standard error by a
factor of 10, the number of replications would have to be increased by a factor of
100 for standard Monte Carlo random sampling, but only 10 for low-discrepancy
sequencing. Further details of low-discrepancy techniques are beyond the scope of
this text, but can be seen in Boyle (1977) or Press et al. (1992). The former offers
a detailed and relevant example in the context of options pricing.

13.3.2 Control variates

The application of control variates involves employing a variable similar to that
used in the simulation, but whose properties are known prior to the simulation.
Denote the variable whose properties are known by y, and that whose properties
are under simulation by x. The simulation is conducted on x and also on y, with
the same sets of random number draws being employed in both cases. Denoting
the simulation estimates of x and y by xˆ and yˆ, respectively, a new estimate of x
can be derived from

x∗ = y + (xˆ − yˆ) (13.5)

Again, it can be shown that the Monte Carlo sampling error of this quantity,
x∗, will be lower than that of x provided that a certain condition holds. The
control variates help to reduce the Monte Carlo variation owing to particular sets
of random draws by using the same draws on a related problem whose solution is
known. It is expected that the effects of sampling error for the problem under study
and the known problem will be similar, and hence can be reduced by calibrating
the Monte Carlo results using the analytic ones.

It is worth noting that control variates succeed in reducing the Monte Carlo
sampling error only if the control and simulation problems are very closely related.
As the correlation between the values of the control statistic and the statistic of
interest is reduced, the variance reduction is weakened. Consider again (13.5), and

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596 • Simulation methods

take the variance of both sides (13.6)
var(x∗) = var(y + (xˆ − yˆ))

var(y) = 0 since y is a quantity which is known analytically and is therefore not
subject to sampling variation, so (13.6) can be written

var(x∗) = var(xˆ) + var(yˆ) − 2cov(xˆ, yˆ) (13.7)

The condition that must hold for the Monte Carlo sampling variance to be lower
with control variates than without is that var(x∗) is less than var(xˆ). Taken from

(13.7), this condition can also be expressed as

var(yˆ) − 2cov(xˆ, yˆ) < 0

or

cov(xˆ, yˆ) > 1 var(yˆ)
2

Divide both sides of this inequality by the products of the standard deviations, i.e.
by (var(xˆ), var(yˆ))1/2, to obtain the correlation on the LHS

corr(xˆ, yˆ) > 1 var(yˆ)
2 var(xˆ)

To offer an illustration of the use of control variates, a researcher may be

interested in pricing an arithmetic Asian option using simulation. Recall that an

arithmetic Asian option is one whose payoff depends on the arithmetic average

value of the underlying asset over the lifetime of the averaging; at the time of

writing, an analytical (closed-form) model is not yet available for pricing such

options. In this context, a control variate price could be obtained by finding the

price via simulation of a similar derivative whose value is known analytically –

e.g. a vanilla European option. Thus, the Asian and vanilla options would be

priced using simulation, as shown below, with the simulated price given by PA
and PB∗S, respectively. The price of the vanilla option, PBS is also calculated using an

analytical formula, such as Black–Scholes. The new estimate of the Asian option
price, PA∗, would then be given by

P ∗ = (PA − PBS) + PB∗S (13.8)
A

13.3.3 Random number re-usage across experiments

Although of course it would not be sensible to re-use sets of random number
draws within a Monte Carlo experiment, using the same sets of draws across
experiments can greatly reduce the variability of the difference in the estimates
across those experiments. For example, it may be of interest to examine the power
of the Dickey–Fuller test for samples of size 100 observations and for different
values of φ (to use the notation of chapter 8). Thus, for each experiment involving
a different value of φ, the same set of standard normal random numbers could be

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13.4 Bootstrapping • 597

used to reduce the sampling variation across experiments. However, the accuracy
of the actual estimates in each case will not be increased, of course.

Another possibility involves taking long series of draws and then slicing them
up into several smaller sets to be used in different experiments. For example, Monte
Carlo simulation may be used to price several options of different times to maturity,
but which are identical in all other respects. Thus, if six-month, three-month and
one-month horizons were of interest, sufficient random draws to cover six months
would be made. Then the six-months’ worth of draws could be used to construct
two replications of a three-month horizon, and six replications for the one-month
horizon. Again, the variability of the simulated option prices across maturities
would be reduced, although the accuracies of the prices themselves would not be
increased for a given number of replications.

Random number re-usage is unlikely to save computational time, for making
the random draws usually takes a very small proportion of the overall time taken
to conduct the whole experiment.

• • • • • • • • • • • • 13.4 Bootstrapping

Bootstrapping is related to simulation, but with one crucial difference. With simu-
lation, the data are constructed completely artificially. Bootstrapping, on the other
hand, is used to obtain a description of the properties of empirical estimators by
using the sample data points themselves, and it involves sampling repeatedly with
replacement from the actual data. Many econometricians were initially highly
sceptical of the usefulness of the technique, which appears at first sight to be some
kind of magic trick – creating useful additional information from a given sample.
Indeed, Davison and Hinkley (1997, p. 3), state that the term ‘bootstrap’ in this
context comes from an analogy with the fictional character Baron Munchhausen,
who got out from the bottom of a lake by pulling himself up by his bootstraps.

Suppose a sample of data, y = y1, y2, . . . , yT are available and it is desired to
estimate some parameter θ . An approximation to the statistical properties of θˆT
can be obtained by studying a sample of bootstrap estimators. This is done by
taking N samples of size T with replacement from y and re-calculating θˆ with
each new sample. A series of θˆ estimates is then obtained, and their distribution
can be considered.

The advantage of bootstrapping over the use of analytical results is that it
allows the researcher to make inferences without making strong distributional
assumptions, since the distribution employed will be that of the actual data. Instead
of imposing a shape on the sampling distribution of the θˆ value, bootstrapping
involves empirically estimating the sampling distribution by looking at the variation
of the statistic within-sample.

A set of new samples is drawn with replacement from the sample and the test
statistic of interest calculated from each of these. Effectively, this involves sampling
from the sample, i.e. treating the sample as a population from which samples can
be drawn. Call the test statistics calculated from the new samples θˆ∗. The samples
are likely to be quite different from each other and from the original θˆ value,

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598 • Simulation methods

since some observations may be sampled several times and others not at all. Thus a
distribution of values of θˆ∗ is obtained, from which standard errors or some other
statistics of interest can be calculated.

Along with advances in computational speed and power, the number of boot-
strap applications in finance and in econometrics have increased rapidly in previous
years. For example, in econometrics, the bootstrap has been used in the context
of unit root testing. Scheinkman and LeBaron (1989) also suggest that the boot-
strap can be used as a ‘shuffle diagnostic’, where as usual the original data are
sampled with replacement to form new data series. Successive applications of this
procedure should generate a collection of data sets with the same distributional
properties, on average, as the original data. But any kind of dependence in the
original series (e.g. linear or non-linear autocorrelation) will, by definition, have
been removed. Applications of econometric tests to the shuffled series can then be
used as a benchmark with which to compare the results on the actual data or to
construct standard error estimates or confidence intervals.

In finance, an application of bootstrapping in the context of risk manage-
ment is discussed below. Another important recent proposed use of the boot-
strap is as a method for detecting data snooping (data mining) in the context of
tests of the profitability of technical trading rules. Data snooping occurs when
the same set of data is used to construct trading rules and also to test them. In
such cases, if a sufficient number of trading rules are examined, some of them
are bound, purely by chance alone, to generate statistically significant positive
returns. Intra-generational data snooping is said to occur when, over a long period
of time, technical trading rules that ‘worked’ in the past continue to be exam-
ined, while the ones that did not fade away. Researchers are then made aware of
only the rules that worked, and not the other, perhaps thousands, of rules that
failed.

Data snooping biases are apparent in other aspects of estimation and testing in
finance. Lo and MacKinlay (1990) find that tests of financial asset pricing models
(CAPM) may yield misleading inferences when properties of the data are used to
construct the test statistics. These properties relate to the construction of portfolios
based on some empirically motivated characteristic of the stock, such as market
capitalisation, rather than a theoretically motivated characteristic, such as dividend
yield.

Sullivan, Timmermann and White (1999) and White (2000) propose the use
of a bootstrap to test for data snooping. The technique works by placing the rule
under study in the context of a ‘universe’ of broadly similar trading rules. This
gives some empirical content to the notion that a variety of rules may have been
examined before the final rule is selected. The bootstrap is applied to each trading
rule, by sampling with replacement from the time series of observed returns for
that rule. The null hypothesis is that there does not exist a superior technical
trading rule. Sullivan, Timmermann and White show how a p-value of the ‘reality
check’ bootstrap-based test can be constructed, which evaluates the significance of
the returns (or excess returns) to the rule after allowing for the fact that the whole
universe of rules may have been examined.

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13.4 Bootstrapping • 599

Box 13.2 Re-sampling the data

(1) Generate a sample of size T from the original data by sampling with
replacement from the whole rows taken together (that is, if observation
32 is selected, take y32 and all values of the explanatory variables for
observation 32).

(2) Calculate βˆ∗, the coefficient matrix for this bootstrap sample.
(3) Go back to stage 1 and generate another sample of size T. Repeat these

stages a total of N times. A set of N coefficient vectors, βˆ∗, will thus be
obtained and in general they will all be different, so that a distribution
of estimates for each coefficient will result.

13.4.1 An example of bootstrapping in a regression context (13.9)
Consider a standard regression model
y = Xβ + u
The regression model can be bootstrapped in two ways.

Re-sample the data

This procedure involves taking the data, and sampling the entire rows correspond-
ing to observation i together. The steps would then be as shown in box 13.2.

A methodological problem with this approach is that it entails sampling from
the regressors, and yet under the CLRM, these are supposed to be fixed in repeated
samples, which would imply that they do not have a sampling distribution. Thus,
resampling from the data corresponding to the explanatory variables is not in the
spirit of the CLRM.

As an alternative, the only random influence in the regression is the errors, u,
so why not just bootstrap from those?

Re-sampling from the residuals

This procedure is ‘theoretically pure’ although harder to understand and to imple-
ment. The steps are shown in box 13.3.

13.4.2 Situations where the bootstrap will be ineffective

There are at least two situations where the bootstrap, as described above, will not
work well.

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600 • Simulation methods

Box 13.3 Re-sampling from the residuals

(1) Estimate the model on the actual data, obtain the fitted values yˆ, and
calculate the residuals, uˆ

(2) Take a sample of size T with replacement from these residuals (and call
these uˆ∗), and generate a bootstrapped-dependent variable by adding

the fitted values to the bootstrapped residuals

y∗ = yˆ + uˆ∗ (13.10)

(3) Then regress this new dependent variable on the original X data to get
a bootstrapped coefficient vector, βˆ∗

(4) Go back to stage 2, and repeat a total of N times.

Outliers in the data

If there are outliers in the data, the conclusions of the bootstrap may be affected. In
particular, the results for a given replication may depend critically on whether the
outliers appear (and how often) in the bootstrapped sample.

Non-independent data

Use of the bootstrap implicitly assumes that the data are independent of one another.
This would obviously not hold if, for example, there were autocorrelation in the
data. A potential solution to this problem is to use a ‘moving block bootstrap’.
Such a method allows for the dependence in the series by sampling whole blocks
of observations at a time. These, and many other issues relating to the theory and
practical usage of the bootstrap are given in Davison and Hinkley (1997); see also
Efron (1979, 1982).

It is also worth noting that variance reduction techniques are also available
under the bootstrap, and these work in a very similar way to those described above
in the context of pure simulation.

• • • • • • • • • • • • 13.5 Random number generation

Most econometrics computer packages include a random number generator. The
simplest class of numbers to generate are from a uniform (0,1) distribution. A
uniform (0,1) distribution is one where only values between zero and one are
drawn, and each value within the interval has an equal chance of being selected.
Uniform draws can be either discrete or continuous. An example of a discrete
uniform number generator would be a die or a roulette wheel. Computers generate
continuous uniform random number draws.

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13.6 Disadvantages of the simulation approach • 601

Numbers that are a continuous uniform (0,1) can be generated according to
the following recursion

yi+1 = (a yi + c ) modulo m , i = 0, 1, . . . , T (13.11)

then

Ri +1 = yi +1/m for i = 0, 1, . . . , T (13.12)

for T random draws, where y0 is the seed (the initial value of y), a is a multiplier and
c is an increment. All three of these are simply constants. The ‘modulo operator’
simply functions as a clock, returning to one after reaching m .

Any simulation study involving a recursion, such as that described by (13.11) to
generate the random draws, will require the user to specify an initial value, y0, to get
the process started. The choice of this value will, undesirably, affect the properties of
the generated series. This effect will be strongest for y1, y2, . . . , but will gradually
die away. For example, if a set of random draws is used to construct a time series
that follows a GARCH process, early observations on this series will behave less
like the GARCH process required than subsequent data points. Consequently,
a good simulation design will allow for this phenomenon by generating more
data than are required and then dropping the first few observations. For example,
if 1,000 observations are required, 1,200 observations might be generated, with
observations 1 to 200 subsequently deleted and 201 to 1,200 used to conduct the
analysis.

These computer-generated random number draws are known as pseudo-random
numbers, since they are in fact not random at all, but entirely deterministic, since
they have been derived from an exact formula! By carefully choosing the values
of the user-adjustable parameters, it is possible to get the pseudo-random number
generator to meet all the statistical properties of true random numbers. Eventually,
the random number sequences will start to repeat, but this should take a long time
to happen. See Press et al. (1992) for more details and Fortran code, or Greene
(2002) for an example.

The U(0,1) draws can be transformed into draws from any desired distri-
bution – for example a normal or a Student’s t. Usually, econometric software
packages with simulations facilities would do this automatically.

• • • • • • • • • • • • 13.6 Disadvantages of the simulation approach to econometric or financial
problem solving

● It might be computationally expensive
That is, the number of replications required to generate precise solutions
may be very large, depending upon the nature of the task at hand. If each
replication is relatively complex in terms of estimation issues, the problem
might be computationally infeasible, such that it could take days, weeks or
even years to run the experiment. Although CPU time is becoming ever

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602 • Simulation methods

cheaper as faster computers are brought to market, the technicality of the
problems studied seems to accelerate just as quickly!
● The results might not be precise
Even if the number of replications is made very large, the simulation experi-
ments will not give a precise answer to the problem if some unrealistic assump-
tions have been made of the data generating process. For example, in the
context of option pricing, the option valuations obtained from a simulation
will not be accurate if the data generating process assumed normally distributed
errors while the actual underlying returns series is fat-tailed.
● The results are often hard to replicate
Unless the experiment has been set up so that the sequence of random draws
is known and can be reconstructed, which is rarely done in practice, the results
of a Monte Carlo study will be somewhat specific to the given investigation.
In that case, a repeat of the experiment would involve different sets of random
draws and therefore would be likely to yield different results, particularly if the
number of replications is small.
● Simulation results are experiment-specific
The need to specify the data generating process using a single set of equations
or a single equation implies that the results could apply to only that exact type
of data. Any conclusions reached may or may not hold for other data gener-
ating processes. To give one illustration, examining the power of a statistical
test would, by definition, involve determining how frequently a wrong null
hypothesis is rejected. In the context of DF tests, for example, the power of the
test as determined by a Monte Carlo study would be given by the percentage
of times that the null of a unit root is rejected. Suppose that the following data
generating process is used for such a simulation experiment

yt = 0.99yt−1 + ut , ut ∼ N(0, 1) (13.13)

Clearly, the null of a unit root would be wrong in this case, as is necessary to
examine the power of the test. However, for modest sample sizes, the null is
likely to be rejected quite infrequently. It would not be appropriate to conclude
from such an experiment that the DF test is generally not powerful, since in
this case the null (φ = 1) is not very wrong! This is a general problem with
many Monte Carlo studies. The solution is to run simulations using as many
different and relevant data generating processes as feasible. Finally, it should
be obvious that the Monte Carlo data generating process should match the
real-world problem of interest as far as possible.

To conclude, simulation is an extremely useful tool that can be applied to an
enormous variety of problems. The technique has grown in popularity over the
past decade, and continues to do so. However, like all tools, it is dangerous in
the wrong hands. It is very easy to jump into a simulation experiment without
thinking about whether such an approach is valid or not.

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13.7 An example of Monte Carlo simulation • 603

• • • • • • • • • • • • 13.7 An example of Monte Carlo simulation in econometrics: deriving a set of
critical values for a Dickey–Fuller test

Recall, that the equation for a Dickey–Fuller (DF) test applied to some series yt is
the regression

yt = φyt−1 + ut (13.14)

so that the test is one of H0: φ = 1 against H1: φ < 1. The relevant test statistic is
given by

τ = φˆ − 1 (13.15)
S E (φˆ )

Under the null hypothesis of a unit root, the test statistic does not follow a standard
distribution, and therefore a simulation would be required to obtain the relevant
critical values. Obviously, these critical values are well documented, but it is of
interest to see how one could generate them. A very similar approach could then
potentially be adopted for situations where there has been less research and where
the results are relatively less well known.

The simulation would be conducted in the four steps shown in box 13.4.
Some EViews code for conducting such a simulation is given below. The objec-
tive is to develop a set of critical values for Dickey–Fuller test regressions. The
simulation framework considers sample sizes of 1,000, 500 and 100 observations.
For each of these sample sizes, regressions with no constant or trend, a constant
but no trend, and a constant and trend are conducted. 50,000 replications are
used in each case, and the critical values for a one-sided test at the 1%, 5% and
10% levels are determined. The code can be found pre-written in a file entitled
‘dfcv.prg’.

EViews programs are simply sets of instructions saved as plain text, so that
they can be written from within EViews, or using a word processor or text editor.
EViews program files must have a ‘.PRG’ suffix. There are several ways to run the
programs once written, but probably the simplest is to write all of the instructions
first, and to save them. Then open the EViews software and choose File, Open
and Programs. . . , and when prompted select the directory and file for the
instructions. The program containing the instructions will then appear on the
screen. To run the program, click on the Run button. EViews will then open a
dialog box with several options, including whether to run the program in ‘Verbose’
or ‘Quiet’ mode. Choose Verbose mode to see the instruction line that is being
run at each point in its execution (i.e. the screen is continually updated). This
is useful for debugging programs or for running short programs. Choose Quiet
to run the program without updating the screen display as it is running, which
will make it execute (considerably) more quickly. The screen would appear as in
screenshot 13.1.

Then click OK and off it goes! The following lists the instructions that are
contained in the program, and the discussion below explains what each line does.

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604 • Simulation methods

Box 13.4 Setting up a Monte Carlo simulation

(1) Construct the data generating process under the null hypothesis – that

is, obtain a series for y that follows a unit root process. This would be

done by:

● Drawing a series of length T, the required number of observations,

from a normal distribution. This will be the error series, so that
ut ∼ N(0,1).
● Assuming a first value for y, i.e. a value for y at time t = 1.
● Constructing the series for y recursively, starting with y2, y3, and so
on

y2 = y1 + u2 (13.16)
y3 = y2 + u3

...

yT = yT−1 + u T

(2) Calculating the test statistic, τ .
(3) Repeating steps 1 and 2 N times to obtain N replications of the

experiment. A distribution of values for τ will be obtained across the
replications.
(4) Ordering the set of N values of τ from the lowest to the highest. The
relevant 5% critical value will be the 5th percentile of this distribution.

NEW WORKFILE CREATED CALLED DF CV, UNDATED
WITH 50000 OBSERVATIONS

WORKFILE DF CV U 50000
RNDSEED 12345
SERIES T1
SERIES T2
SERIES T3
SCALAR K1
SCALAR K2
SCALAR K3
SCALAR K4
SCALAR K5
SCALAR K6
SCALAR K7
SCALAR K8
SCALAR K9
!NREPS=50000
!NOBS=1000

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13.7 An example of Monte Carlo simulation • 605

Screenshot 13.1 Running an EViews program

FOR !REPC=1 TO !NREPS
SMPL @FIRST @FIRST
SERIES Y1=0
SMPL @FIRST+1 !NOBS+200
SERIES Y1=Y1(−1)+NRND
SERIES DY1=Y1-Y1(−1)
SMPL @FIRST+200 !NOBS+200
EQUATION EQ1.LS DY1 Y1(−1)
T1(!REPC)=@TSTATS(1)
EQUATION EQ2.LS DY1 C Y1(−1)
T2(!REPC)=@TSTATS(2)
EQUATION EQ3.LS DY1 C @TREND Y1(−1)
T3(!REPC)=@TSTATS(3)
NEXT
SMPL @FIRST !NREPS
K1=@QUANTILE(T1,0.01)
K2=@QUANTILE(T1,0.05)
K3=@QUANTILE(T1,0.1)
K4=@QUANTILE(T2,0.01)
K5=@QUANTILE(T2,0.05)

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606 • Simulation methods

K6=@QUANTILE(T2,0.1)
K7=@QUANTILE(T3,0.01)
K8=@QUANTILE(T3,0.05)
K9=@QUANTILE(T3,0.1)

Although there are probably more efficient ways to structure the program than
that given above, this sample code has been written in a style to make it easy to
follow. The program would be run in the way described above. That is, it would
be opened from within EViews, and then the Run button would be pressed and
the mode of execution (Verbose or Quiet) chosen.

A first point to note is that comment lines are denoted by a symbol in EViews.
The first line of code, ‘WORKFILE DF CV U 50000’ will set up a new EViews
workfile called DF CV.WK1, which will be undated (U) and will contain series of
length 50,000. This step is required for EViews to have a place to put the output
series since no other workfile will be opened by this program! In situations where
the program requires an already existing workfile containing data to be opened,
this line would not be necessary since any new results and objects created would
be appended to the original workfile. RNDSEED 12345 sets the random number
seed that will be used to start the random draws.

‘SERIES T1’ creates a new series T1 that will be filled with NA elements. The
series T1, T2 and T3, will hold the Dickey–Fuller test statistics for each replication,
for the three cases (no constant or trend, constant but no trend, constant and trend,
respectively). ‘SCALAR K1’ sets up a scalar (single number) K1. K1, . . . , K9 will
be used to hold the 1%, 5% and 10% critical values for each of the three cases.
!NREPS=50000 and !NOBS=1000 set the number of replications that will be
used to 50,000 and the number of observations to be used in each time series to
1,000. The exclamation marks enable the scalars to be used without previously
having to define them using the SCALAR instruction. Of course, these values
can be changed as desired. Loops in EViews are defined as FOR at the start and
NEXT at the end, in a similar way to visual basic code. Thus FOR !REPC=1
TO !NREPS starts the main replications loop, which will run from 1 to NREPS.

SMPL @FIRST @FIRST
SERIES Y1=0

The two lines above set the first observation of a new series Y1 to zero (so
@FIRST is EViews method of denoting the first observation in the series, and
the final observation is denoted by, you guessed it, @LAST). Then

SMPL @FIRST+1 !NOBS+200
SERIES Y1=Y1(−1)+NRND
SERIES DY1=Y1-Y1(−1)

will set the sample to run from observation 2 to observation !NOBS+200 (1200).
This enables the program to generate 200 additional startup observations. It is very
easy in EViews to construct a series following a random walk process, and this
is done by the second of the above three lines. The current value of Y1 is set

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13.7 An example of Monte Carlo simulation • 607

to the previous value plus a standard normal random draw (NRND). In EViews,
draws can be taken from a wide array of distributions (see the User Guide).
SERIES DY1 . . . creates a new series called DY1 that contains the first difference
of Y.

SMPL @FIRST+200 !NOBS+200
EQUATION EQ1.LS DY1 Y1(−1)

The first of the two lines above sets the sample to run from observation 201 to
observation 1200, thus dropping the 200 startup observations. The following line
actually conducts an OLS estimation (‘.LS’), in the process creating an equation
object called EQ1. The dependent variable is DY1 and the independent variable
is the lagged value of Y, Y(−1).

Following the equation estimation, several new quantities will have been
created. These quantities are denoted by a ‘@’ in EViews. So the line
‘T1(!REPC)=@TSTATS(1)’ will take the t-ratio of the coefficient on the first
(and in this case only) independent variable, and will place it in the !REPC row
of the series T1. Similarly, the t-ratios on the lagged value of Y will be placed in
T2 and T3 for the regressions with constant and constant and trend respectively.
Finally, NEXT will finish the replications loop and SMPL @FIRST !NREPS will
set the sample to run from 1 to 50,000, and the 1%, 5%, and 10% critical values
for the no constant or trend case will then be found in K1, K2 and K3. The
‘@QUANTILE(T1,0.01)’ instruction will take the 1% quantile from the series
T1, which avoids sorting the series.

The critical value obtained by running the above instructions, which are
virtually identical to those found in the statistical tables at the end of this book, are
(to two decimal places)

No constant or trend 1% 5% 10%
Constant but no trend
Constant and trend −2.58 −1.95 −1.63
−3.45 −2.85 −2.56
−3.93 −3.41 −3.43

This is to be expected, for the use of 50,000 replications should ensure that
an approximation to the asymptotic behaviour is obtained. For example, the 5%
critical value for a test regression with no constant or trend and 500 observations is
−1.945 in this simulation, and −1.95 in Fuller (1976). Although the Dickey–Fuller
simulation was unnecessary in the sense that the critical values for the resulting test
statistics are already well known and documented, a very similar procedure could
be adopted for a variety of problems. For example, a similar approach could be
used for constructing critical values or for evaluating the performance of statistical
tests in various situations.

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608 • Simulation methods

Box 13.5 Simulating the price of an Asian option

(1) Specify a data generating process for the underlying asset. A random walk
with drift model is usually assumed. Specify also the assumed size of the
drift component and the assumed size of the volatility parameter.
Specify also a strike price K, and a time to maturity, T.

(2) Draw a series of length T, the required number of observations for the
life of the option, from a normal distribution. This will be the error
series, so that εt ∼ N(0, 1).

(3) Form a series of observations of length T on the underlying asset.
(4) Observe the price of the underlying asset at maturity observation T. For a call

option, if the value of the underlying asset on maturity date, PT ≤ K,
the option expires worthless for this replication. If the value of the
underlying asset on maturity date, PT > K, the option expires in the
money, and has value on that date equal to PT − K, which should be
discounted back to the present day using the risk-free rate. Use of the
risk-free rate relies upon risk-neutrality arguments (see Duffie, 1996).
(5) Repeat steps 1 to 4 a total of N times, and take the average value of the
option over the N replications. This average will be the price of the
option.

• • • • • • • • • • • • 13.8 An example of how to simulate the price of a financial option

A simple example of how to use a Monte Carlo study for obtaining a price for
a financial option is shown below. Although the option used for illustration here
is just a plain vanilla European call option which could be valued analytically
using the standard Black–Scholes (1973) formula, again, the method is sufficiently
general that only relatively minor modifications would be required to value more
complex options. Boyle (1977) gives an excellent and highly readable introduction
to the pricing of financial options using Monte Carlo.

The steps involved are shown in box 13.5.

13.8.1 Simulating the price of a financial option using a fat-tailed
underlying process

A fairly limiting and unrealistic assumption in the above methodology for pricing
options is that the underlying asset returns are normally distributed, whereas in
practice, it is well know that asset returns are fat-tailed. There are several ways
to remove this assumption. First, one could employ draws from a fat-tailed dis-
tribution, such as a Student’s t , in step 2 above. Another method, which would
generate a distribution of returns with fat tails, would be to assume that the errors
and therefore the returns follow a GARCH process. To generate draws from a
GARCH process, do the steps shown in box 13.6.

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13.8 An example of how to simulate a financial option • 609

Box 13.6 Generating draws from a GARCH process

(1) Draw a series of length T, the required number of observations for the

life of the option, from a normal distribution. This will be the error
series, so that εt ∼ N(0, 1).
(2) Recall that one way of expressing a GARCH model is

r t = μ + ut ut = εt σt εt ∼ N(0, 1) (13.17)

σt2 = α0 + α1u 2 + β σt2−1 (13.18)
t −1

A series of εt , have been constructed and it is necessary to specify
initialising values y1 and σ12 and plausible parameter values for α0, α1,
β. Assume that y1 and σ12 are set to μ and one, respectively, and
the parameters are given by α0 = 0.01, α1 = 0.15, β = 0.80. The
equations above can then be used to generate the model for r t as
described above.

13.8.2 Simulating the price of an Asian option

An Asian option is one whose payoff depends upon the average value of the
underlying asset over the averaging horizon specified in the contract. Most Asian
options contracts specify that arithmetic rather than geometric averaging should
be employed. Unfortunately, the arithmetic average of a unit root process with a
drift is not well defined. Additionally, even if the asset prices are assumed to be log-
normally distributed, the arithmetic average of them will not be. Consequently,
a closed-form analytical expression for the value of an Asian option has yet to
be developed. Thus, the pricing of Asian options represents a natural application
for simulations methods. Determining the value of an Asian option is achieved in
almost exactly the same way as for a vanilla call or put. The simulation is conducted
identically, and the only difference occurs in the very last step where the value of
the payoff at the date of expiry is determined.

13.8.3 Pricing Asian options using EViews

A sample of EViews code for determining the value of an Asian option is given
below. The example is in the context of an arithmetic Asian option on the FTSE
100, and two simulations will be undertaken with different strike prices (one that
is out of the money forward and one that is in the money forward). In each case,
the life of the option is six months, with daily averaging commencing immediately,
and the option value is given for both calls and puts in terms of index points. The
parameters are given as follows, with dividend yield and risk-free rates expressed
as percentages:

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610 • Simulation methods

Simulation 1: strike=6500, risk-free=6.24, dividend yield=2.42, ‘today’s’
FTSE=6289.70, forward price=6405.35, implied volatility=26.52

Simulation 2: strike=5500, risk-free=6.24, dividend yield=2.42, ‘today’s’
FTSE=6289.70, forward price=6405.35, implied volatility=34.33

Any other programming language or statistical package would be equally appli-
cable, since all that is required is a Gaussian random number generator, the ability
to store in arrays and to loop. Since no actual estimation is performed, differences
between packages are likely to be negligible. All experiments are based on 25,000
replications and their antithetic variates (total: 50,000 sets of draws) to reduce
Monte Carlo sampling error.

Some sample code for pricing an ASIAN option for Normally distributed
errors using EViews is given as follows:

UNDATED WORKFILE CREATED CALLED ASIAN P
WITH 50000 OBSERVATIONS
WORKFILE ASIAN P U 50000
RNDSEED 12345
!N=125
!TTM=0.5
!NREPS=50000
!IV=0.28
!RF=0.0624
!DY=0.0242
!DT=!TTM / !N
!DRIFT=(!RF-!DY-(!IVˆ2/2.0))∗!DT
!VSQRDT=!IV∗(!DTˆ0.5)
!K=5500
!S0=6289.7
SERIES APVAL
SERIES ACVAL
SERIES SPOT
SCALAR AV
SCALAR CALLPRICE
SCALAR PUTPRICE
SERIES RANDS
GENERATES THE DATA
FOR !REPC=1 TO !NREPS STEP 2
RANDS=NRND
SERIES SPOT=0
SMPL @FIRST @FIRST
SPOT(1)=!S0∗EXP(!DRIFT+!VSQRDT∗RANDS(1))
SMPL 2 !N
SPOT=SPOT(−1)∗EXP(!DRIFT+!VSQRDT∗RANDS(!N))
COMPUTE THE DAILY AVERAGE
SMPL @FIRST !N

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13.8 An example of how to simulate a financial option • 611

AV=@MEAN(SPOT)
IF AV>!K THEN

ACVAL(!REPC)=(AV-!K)∗EXP(-!RF∗!TTM)

ELSE
ACVAL(!REPC)=0

ENDIF
IF AV<!K THEN

APVAL(!REPC)=(!K-AV)∗EXP(-!RF∗!TTM)

ELSE
APVAL(!REPC)=0

ENDIF
RANDS=-RANDS
SERIES SPOT=0

SMPL @FIRST @FIRST
SPOT(1)=!S0∗EXP(!DRIFT+!VSQRDT∗RANDS(1))

SMPL 2 !N
SPOT=SPOT(−1)∗EXP(!DRIFT+!VSQRDT∗RANDS(!N))

COMPUTE THE DAILY AVERAGE

SMPL @FIRST !N
AV=@MEAN(SPOT)
IF AV>!K THEN

ACVAL(!REPC+1)=(AV-!K)∗EXP(-!RF∗!TTM)

ELSE
ACVAL(!REPC+1)=0

ENDIF
IF AV<!K THEN

APVAL(!REPC+1)=(!K-AV)∗EXP(-!RF∗!TTM)

ELSE
APVAL(!REPC+1)=0

ENDIF

NEXT

SMPL @FIRST !NREPS
CALLPRICE=@MEAN(ACVAL)
PUTPRICE=@MEAN(APVAL)

Many parts of the program above use identical instructions to those given for
the DF critical value simulation, and so annotation will now focus on the con-
struction of the program and on previously unseen commands. The first block of
commands set up a new workfile called ‘ASIAN P’ that will hold all of the objects
and output. Then the following lines specify the parameters for the simulation
of the path of the price of the underlying asset (the drift, the implied volatility,
etc.).

‘!=DT=!TTM/!N’ splits the time to maturity (0.5 years) into N discrete
time periods. Since daily averaging is required, it is easiest to set N = 125 (the
approximate number of trading days in half a year), so that each time period

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612 • Simulation methods

DT represents one day. The model assumes under a risk-neutral measure that the
underlying asset price follows a geometric Brownian motion, which is given by

d S = (r f − d y) Sd t + σ Sd z (13.19)

where dz is the increment of a Brownian motion. Further details of this continuous
time representation of the movement of the underlying asset over time are beyond
the scope of this book. A treatment of this and many other useful option pricing
formulae and computer code are given in Haug (1998), and an accessible discussion
is given in Hull (2011). The discrete time approximation to this for a time step of
one can be written

St = St−1 exp r f − dy − 1σ2 √ (13.20)
2 dt + σ dt ut

where ut is a white noise error process. The following instructions set up the arrays
for the underlying spot price (called ‘SPOT’), and for the discounted values of the
put (‘APVAL’) and call (‘ACVAL’). Note that by default, arrays of the length given
by the ‘workfile’ definition statement (50000) will be created.

The command ‘FOR !REPC=1 TO !NREPS DO REPC=1, NREPS,2’
starts the main do loop for the simulation, looping up to the number of replications,
in steps of 2. The loop ends at ‘END DO REPC’. Steps of 2 are used because
antithetic variates are also used for each replication, which will create another
simulated path for the underlying asset prices and option value.

The random N(0,1) draws are made, which are then constructed into a series of
future prices of the underlying asset for the next 125 days. ‘AV=@MEAN(SPOT)’
will compute the average price of the underlying over the lifetime of the option
(125 days). The following two statements construct the terminal payoffs for the
call and the put options respectively. For the call, ‘ACVAL’ is set to the average
underlying price less the strike price if the average is greater than the strike (i.e.
if the option expires in the money), and zero otherwise. Vice versa for the put.
The payoff at expiry is discounted back to the present using the risk-free rate, and
placed in the REPC row of the ‘ACVAL’ or ‘APVAL’ array for the calls and puts,
respectively.

The process then repeats using the antithetic variates, constructed using
‘RANDS = -RANDS’. The call and put present values for these paths are put
in the even rows of ‘ACVAL’ and ‘APVAL’.

This completes one cycle of the REPC loop, which starts again with REPC=3,
then 5, 7, 9, . . . , 49999. The result will be two arrays ‘ACVAL’ and ‘APVAL’,
which will contain 50,000 rows comprising the present value of the call and put
option for each simulated path. The option prices would then simply be given by
the averages over the 50,000 replications.

Note that both call values and put values can be calculated easily from a given
simulation, since the most computationally expensive step is in deriving the path
of simulated prices for the underlying asset. The results are given in table 10.1,
along with the values derived from an analytical approximation to the option price,
derived by Levy, and estimated using VBA code in Haug (1998, pp. 97–100).

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13.9 An example of bootstrapping • 613

The main difference between the way that the simulation is conducted here
and the method used for EViews simulation of the Dickey–Fuller critical values
is that here, the random numbers are generated by opening a new series called
‘RANDS’ and filling it with the random number draws. The reason that this must
be done is so that the negatives of the elements of RANDS can later be taken
to form the antithetic variates. Finally, for each replication, the IF clause will set
out of the money call prices (where K>AV) and out of the money put prices
(K<AV) to zero. Then the call and put prices for each replication are discounted
back to the present using the risk-free rate, and outside the replications loop,
the options prices are the averages of these discounted prices across the 50,000
replications.

The workfile ‘ASIAN P’ will contain quite a few objects by the end of
the simulation, including the scalars CALLPRICE and PUTPRICE, which will
be the call and put prices. Also, the series ACVAL and APVAL will contain
the current value of the option for each of the 50,000 simulated paths. Hav-
ing the whole series across all replications can be useful for constructing stan-
dard errors, and for checking that the program appears to have been working
correctly.

Applying the instructions above (with K = 5500, and implied volatility at 28%)
gives simulated call and put prices as given in the following table.

Strike = 6500, IV = 26.52 Price Strike = 5500, IV = 34.33 Price

CALL 203.45 CALL 888.55
204.22 885.29
Analytical Approximation Analytical Approximation
Monte Carlo Normal Price Monte Carlo Normal Price

PUT 348.7 PUT 64.52
349.43 61.52
Analytical Approximation Analytical Approximation
Monte Carlo Normal Monte Carlo Normal

In both cases, the simulated options prices are quite close to the analytical
approximations, although the Monte Carlo seems to overvalue the out-of-the-
money call and to undervalue the out-of-the-money put. Some of the errors in
the simulated prices relative to the analytical approximation may result from the
use of a discrete-time averaging process using only 125 points.

• • • • • • • • • • • • 13.9 An example of bootstrapping to calculate capital risk requirements

13.9.1 Financial motivation

Risk management modelling has, in this author’s opinion, been one of the most
rapidly developing areas of application of econometric techniques over the past

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614 • Simulation methods

decade or so. One of the most popular approaches to risk measurement is by
calculating what is known as an institution’s ‘value-at-risk’, denoted VaR. Broadly
speaking, value-at-risk is an estimation of the probability of likely losses which could
arise from changes in market prices. More precisely, it is defined as the money-loss of
a portfolio that is expected to occur over a pre-determined horizon and with a
pre-determined degree of confidence. The roots of VaR’s popularity stem from
the simplicity of its calculation, its ease of interpretation and from the fact that VaR
can be suitably aggregated across an entire firm to produce a single number which
broadly encompasses the risk of the positions of the firm as a whole. The value-
at-risk estimate is also often known as the position risk requirement or minimum
capital risk requirement (MCRR); the three terms will be used interchangeably
in the exposition below. There are various methods available for calculating value
at risk, including the ‘delta-normal’ method; historical simulation, involving the
estimation of the quantile of returns of the portfolio; and structured Monte Carlo
simulation; see Dowd (1998) or Jorion (2006) for thorough introductions to value-
at-risk.

The Monte Carlo approach involves two steps. First, a data generating process
is specified for the underlying assets in the portfolio. Second, possible future paths
are simulated for those assets over given horizons, and the value of the portfolio
at the end of the period is examined. Thus the returns for each simulated path are
obtained, and from this distribution across the Monte Carlo replications, the VaR
as a percentage of the initial value of the portfolio can be measured as the first or
fifth percentile.

The Monte Carlo method is clearly a very powerful and flexible method for
generating VaR estimates, since any stochastic process for the underlying assets can
be specified. The effect of increasing variances or correlations, etc. can easily be
incorporated into the simulation design. However, there are at least two drawbacks
with the use of Monte Carlo simulation for estimating VaR. First, for a large
portfolio, the computational time required to compute the VaR may be excessively
great. Second, and more fundamentally, the calculated VaR may be inaccurate if the
stochastic process that has been assumed for the underlying asset is inappropriate.
In particular, asset prices are often assumed to follow a random walk or a random
walk with drift, where the driving disturbances are random draws from a normal
distribution. Since it is well known that asset returns are fat-tailed, the use of
Gaussian draws in the simulation is likely to lead to a systematic underestimate of
the VaR, as extremely large positive or negative returns are more likely in practice
than would arise under a normal distribution. Of course, the normal random draws
could be replaced by draws from a t-distribution, or the returns could be assumed
to follow a GARCH process, both of which would generate an unconditional
distribution of returns with fat tails. However, there is still some concern as to
whether the distribution assumed in designing the simulations framework is really
appropriate.

An alternative approach, that could potentially overcome this criticism, would
be to use bootstrapping rather than Monte Carlo simulation. In this context, the
future simulated prices are generated using random draws with replacement from
the actual returns themselves, rather than artificially generating the disturbances

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13.9 An example of bootstrapping • 615

from an assumed distribution. Such an approach is used in calculating MCRRs
by Hsieh (1993) and by Brooks, Clare and Persand (2000). The methodology
proposed by Hsieh will now be examined.

Hsieh (1993) employs daily log returns on foreign currency (against the US
dollar) futures series from 22 February 1985 until 9 March 1990 (1,275 observa-
tions) for the British pound (denoted BP), the German mark (GM), the Japanese
yen (JY) and the Swiss franc (SF). The first stage in setting up the bootstrapping
framework is to form a model that fits the data and adequately describes its fea-
tures. Hsieh employs the BDS test (discussed briefly in chapter 8) to determine
an appropriate class of models. An application of the test to the raw returns data
shows that the data are not random, and that there is some structure in the data.
The dependence in the series, shown in the rejection of randomness by the test
implies that there is either:

● a linear relationship between yt and yt−1, yt−2, . . . or
● a non-linear relationship between yt and yt−1, yt−2, . . .

The Box–Pierce Q test is applied to test for both, on the returns for the former,
and on the squared or absolute values of the returns for the latter. The results of
this test are not shown but effectively rule out the possibility of linear dependence
(so that, for example, an ARMA model would not be appropriate for the returns),
but there appears to be evidence of non-linear dependence in the series. There-
fore, a second question, is whether the non-linearity is in-mean or in-variance
(see chapter 8 for elucidation). Hsieh uses a bicorrelation test to show that there
is no evidence for non-linearity in-mean. Therefore, the most appropriate class of
models for the returns series is a model which has time-varying (conditional) vari-
ances. Hsieh employs two types of model: EGARCH and autoregressive volatility
(ARV) models. The coefficient estimates for the EGARCH model are reported in
table 13.1.

Several features of the EGARCH estimates are worth noting. First, as one
may anticipate for a set of currency futures returns, the asymmetry terms (i.e.
the estimated values of γ ) are not significant for any of the four series. The
high estimated values of β suggest a high degree of persistence in volatility in
all cases except the Japanese yen. Brooks, Clare and Persand (2000) suggest that
such persistence may be excessive in the sense that the volatility implied by the
estimated conditional variance is too persistent to reproduce the profile of the
volatility of the actual returns series. Such excessive volatility persistence could
lead to an overestimate of the VaR. Leaving this issue aside, Hsieh continues to
evaluate the effectiveness of the EGARCH models in capturing all of the non-
linear dependence in the data. This is achieved by reapplying the BDS test to
the standardised residuals, constructed by taking the residuals from the estimated
models, and dividing them by their respective conditional standard deviations. If
the model has captured all of the important features of the data, the standardised
residual series should be completely random. It is observed that the EGARCH
model cannot capture all of the non-linear dependence in the mark or franc series.

A second approach to modelling volatility is derived from a high/low volatility
estimator. A daily volatility series is thus constructed using a re-scaled estimate of

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616 • Simulation methods

Table 13.1 EGARCH estimates for currency futures returns

Coefficient xt = μ + σtηt SF
μ ηt ∼ N(0, 1)
α log σt2 = α + β log σt2−1 + φ(|ηt−1| − (2/π )1/2) + γ ηt−1
β BP DM JY
φ
γ 0.000319 0.000377 0.000232 0.000239
(0.000208) (0.000214) (0.000189) (0.000235)
−0.688127 −1.072229 −4.438289 −0.993241
(0.030088) (0.041828) (0.756704) (0.032479)
0.889511 0.550707 0.895527
0.928780 (0.004386) (0.075851) (0.003508)
(0.002995) 0.187005 0.282167 0.157669
(0.028388) (0.093357) (0.024013)
0.135854 0.084173 0.313274 0.129035
(0.019961) (0.147279) (0.201531) (0.166507)
−0.110718
(0.177458)

Notes: Standard errors in parentheses.
Source: Hsieh (1993). Reprinted with the permission of School of Business Administration,
University of Washington.

the range over the trading day

σP,t = (0.361 × 1440/M)1/2log(Hight /Lowt ) (13.21)

where Hight and Lowt are the highest and lowest transacted prices on day t and M
is the number of trading minutes during the day. The volatility series, σP,t can now
be modelled as any other series. A natural model to propose, given the dependence

(or persistence) in volatility over time, is an autoregressive model in the volatility.

The formulation used for the price series is known as an autoregressive volatility

(ARV) model

xt = σP,t u t (13.22)

ln σP,t = α + βi ln σP,t−i + νt (13.23)

i

where νt is an error term. The appropriate lag length for the ARV model is
determined using Schwarz’s information criterion, which suggests that 8, 8, 5 and
8 lags should be used for the pound, mark, yen and franc series, respectively. The
coefficient estimates for the ARV models are given in table 13.2.

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13.9 An example of bootstrapping • 617

Table 13.2 Autoregressive volatility estimates for currency
futures returns

Coefficient xt = σP,t ut JY SF
α
β1 ln σP,t = α + βi ln σP,t−i + νt −1.219
β2 (0.193)
β3 i 0.115
β4 (0.028)
β5 BP DM 0.106
β6 (0.028)
β7 −1.037 −1.139 −1.874 0.068
β8 (0.171) (0.187) (0.199) (0.028)
R¯ 2 0.192 0.153 0.208 0.091
(0.028) (0.028) (0.028) (0.028)
0.134 0.111 0.137 0.118
(0.029) (0.028) (0.028) (0.028)
0.062 0.052 0.058 0.074
(0.029) (0.028) (0.029) (0.028)
0.069 0.092 0.109 0.086
(0.029) (0.028) (0.028) (0.028)
0.137 0.091 0.112 0.078
(0.028) (0.028) (0.028) (0.028)
0.027 0.072 0.193
(0.029) (0.028) 0.170
0.073 0.110
(0.028) (0.028)
0.088 0.079
(0.028) (0.028)
0.274 0.227

Source: Hsieh (1993). Reprinted with the permission of School of Business Administration,
University of Washington.

The degrees of persistence for each exchange rate series implied by the ARV
estimates is given by the sums of the β coefficients, which are 0.78, 0.76, 0.62, 0.74,

respectively. These figures are high, although less so than under the EGARCH
formulation. The standardised residuals from this model are given by xt /σˆ P,t ,
where σˆ P,t are the fitted values of volatility. An application of the BDS test to
these standardised residuals shows no evidence of further structure apart from

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618 • Simulation methods

in the Swiss franc case, where the test statistics are marginally significant. Thus,

since these standardised residuals are iid, it is valid to sample from them using the

bootstrap technique.

To summarise, it is concluded that both the EGARCH and ARV mod-

els present reasonable descriptions of the futures returns series, which are then

employed in conjunction with the bootstrap to estimate the value at risk estimates.

This is achieved by simulating the future values of the futures price series, using the

parameter estimates from the two models, and using disturbances obtained by sam-
pling with replacement from the standardised residuals (ηˆt /hˆt1/2) for the EGARCH
model and from ut and νt for ARV models. In this way, 10,000 possible future
paths of the series are simulated (i.e. 10,000 replications are used), and in each case,

the maximum drawdown (loss) can be calculated over a given holding period by

Q = (P0 − P1) ×number of contracts (13.24)

where P0 is the initial value of the position, and P1 is the lowest simulated price
(for a long position) or highest simulated price (for a short position) over the
holding period. The maximum loss is calculated assuming holding periods of 1, 5,
10, 15, 20, 25, 30, 60, 90 and 180 days. It is assumed that the futures position is
opened on the final day of the sample used to estimate the models, 9 March 1990.

The ninetieth percentile of these 10,000 maximum losses can be taken to
obtain a figure for the amount of capital required to cover losses on 90% of days.
It is important for firms to consider the maximum daily losses arising from their
futures positions, since firms will be required to post additional funds to their
margin accounts to cover such losses. If funds are not made available to the margin
account, the firm is likely to have to liquidate its futures position, thus destroying
any hedging effects that the firm required from the futures contracts in the first
place.

However, Hsieh (1993) uses a slightly different approach to the final stage,
which is as follows. Assuming (without loss of generality) that the number of
contracts held is 1, the following can be written for a long position

Q = 1 − x1 (13.25)
x0 x0
or

Q = x1 − 1 (13.26)
x0 x0

for a short position. x1 is defined as the minimum price for a long position (or the
maximum price for a short position) over the horizon that the position is held.

In either case, since x0 is a constant, the distribution of Q will depend on the
distribution of x1. Hsieh (1993) assumes that prices are lognormally distributed,
i.e. that the logs of the ratios of the prices,

ln x1
x0

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13.9 An example of bootstrapping • 619

are normally distributed. This being the case, an alternative estimate of the fifth
percentile of the distribution of returns can be obtained by taking the relevant
critical value from the normal statistical tables, multiplying it by the standard
deviation and adding it to the mean of the distribution.

The MCRRs estimated using the ARV and EGARCH models are com-
pared with those estimated by bootstrapping from the price changes themselves,
termed the ‘unconditional density model’. The estimated MCRRs are given in
table 13.3.

The entries in table 13.3 refer to the amount of capital required to cover 90%
of expected losses, as percentages of the initial values of the positions. For example,
according to the EGARCH model, approximately 14% of the initial value of a
long position should be held in the case of the yen to cover 90% of expected losses
for a 180-day horizon. The results contain several interesting features. First, the
MCRRs derived from bootstrapping the price changes themselves (the ‘uncondi-
tional approach’) are in most cases higher than those generated from the other two
methods, especially at short investment horizons. This is argued to have occurred
owing to the fact that the level of volatility at the start of the MCRR calculation
period was low relative to its historical level. Therefore, the conditional estimation
methods (EGARCH and ARV) will initially forecast volatility to be lower than the
historical average. As the holding period increases from 1 towards 180 days, the
MCRR estimates from the ARV model converge upon those of the unconditional
densities. On the other hand, those of the EGARCH model do not converge,
even after 180 days (in fact, in some cases, the EGARCH MCRR seems oddly
to diverge from the unconditionally estimated MCRR as the horizon increases).
It is thus argued that the EGARCH model may be inappropriate for MCRR
estimation in this application.

It can also be observed that the MCRRs for short positions are larger than
those of comparative long positions. This could be attributed to an upward drift in
the futures returns over the sample period, suggesting that on average an upwards
move in the futures price was slightly more likely than a fall.

A further step in the analysis, which Hsieh did not conduct, but which is
shown in Brooks, Clare and Persand (2000), is to evaluate the performance of the
MCRR estimates in an out-of-sample period. Such an exercise would evaluate
the models by assuming that the MCRR estimated from the model had been
employed, and by tracking the change in the value of the position over time.
If the MCRR is adequate, the 90% nominal estimate should be sufficient to
cover losses on 90% of out-of-sample testing days. Any day where the MCRR is
insufficient to cover losses is termed an ‘exceedence’ or an ‘exception’. A model
that leads to more than 10% exceptions for a nominal 90% coverage is deemed
unacceptable on the grounds that on average, the MCRR was insufficient. Equally,
a model that leads to considerably less than the expected 10% exceptions would
also be deemed unacceptable on the grounds that the MCRR has been set at
an inappropriately high level, leading capital to be unnecessarily tied up in a
liquid and unprofitable form. Brooks, Clare and Persand (2000) observe, as Hsieh’s
results forewarn, that the MCRR estimates from GARCH-type models are too
high, leading to considerably fewer exceedences than the nominal proportion.

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620 • Simulation methods

Table 13.3 Minimum capital risk requirements for currency futures as
a percentage of the initial value of the position

Long position Short position

No. of days AR Unconditional EGARCH AR Unconditional EGARCH
density density

BP 1 0.73 0.91 0.93 0.80 0.98 1.05
5 1.90 2.30 2.61 2.18 2.76 3.00
2.83 3.27 4.19 3.38 4.22 4.88
10 3.54 3.94 5.72 4.45 5.48 6.67
15 4.10 4.61 6.96 5.24 6.33 8.43
20 4.59 5.15 8.25 6.20 7.36 10.46
25 5.02 5.58 9.08 7.11 8.33 12.06
30 7.24 7.44 14.50 11.64 12.87 20.71
60 8.74 8.70 17.91 15.45 16.90 28.03
90 11.38 10.67 24.25 25.81 27.36 48.02
180
0.72 0.87 0.83 0.89 1.00 0.95
DM 1 1.89 2.18 2.34 2.23 2.70 2.91
5 2.77 3.14 3.93 3.40 4.12 5.03
3.52 3.86 5.37 4.36 5.30 6.92
10 4.05 4.45 6.54 5.19 6.14 8.91
15 4.55 4.90 7.86 6.14 7.21 10.69
20 4.93 5.37 8.75 7.02 7.88 12.36
25 7.16 7.24 13.14 11.36 12.38 20.86
30 8.87 8.39 16.06 14.68 16.16 27.75
60 11.38 10.35 21.69 24.25 26.25 45.68
90
180 0.56 0.74 0.72 0.68 0.87 0.86
1.61 1.99 2.22 1.92 2.36 2.73
JY 1 2.59 2.82 3.46 3.06 3.53 4.41
5 3.30 3.46 4.37 4.11 4.60 5.79
3.95 4.10 5.09 5.13 5.45 6.77
10 4.42 4.58 5.78 5.91 6.30 7.98
15 4.95 4.92 6.34 6.58 6.85 8.81
20 6.99 6.84 8.72 10.53 10.74 13.58
25 8.43 8.00 10.51 13.61 14.00 17.63
30 10.97 10.27 13.99 21.86 22.21 27.39
60
90
180

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•
•
13.9 An example of bootstrapping • 621

Table 13.3 (cont.)

Long position Short position

No. of days AR Unconditional EGARCH AR Unconditional EGARCH
density density

SF 1 0.82 0.97 0.89 0.93 1.12 0.98
5 1.99 2.51 2.48 2.23 2.93 2.98
2.87 3.60 4.12 3.37 4.53 5.09
10 3.67 4.35 5.60 4.22 5.67 7.03
15 4.24 5.10 6.82 5.09 6.69 8.86
20 4.81 5.65 8.12 5.90 7.77 10.93
25 5.23 6.20 9.12 6.70 8.47 12.50
30 7.69 8.41 13.73 10.55 13.10 21.27
60 9.23 9.93 16.89 13.60 17.06 27.80
90 12.18 12.57 22.92 21.72 27.45 45.47
180

Source: Hsieh (1993). Reprinted with the permission of School of Business Administration, University of
Washington.

13.9.2 VaR estimation using bootstrapping in EViews

Following the discussion above concerning the Hsieh (1993) and Brooks, Clare and
Persand (2000) approaches to calculating minimum capital risk requirements, the
following EViews code can be used to calculate the MCRR for a ten-day holding
period (the length that regulators require banks to employ) using daily S&P500
data, which is found in the file ‘sp500.wf1’. The code is presented, followed by an
annotated copy of some of the key lines.

THIS PROGRAM APPLIES THE BOOTSTRAP TO THE
CALCULATION OF
MCRR FOR A 10-DAY HORIZON PERIOD
LOAD WORKFILE
LOAD “D:\CHRIS\BOOK\SP500.WF1”
RNDSEED 12345
!NREPS=10000
SERIES RT
SERIES U
SERIES H
SERIES MIN
SERIES MAX
SERIES L1