Two main approaches are used for calculating VaR and ETL [2].
First, there is historical simulation, a non-parametric approach that
employs historical data. Consider a sample of 100 P/L values as a
simple example for calculating VaR and ETL. Let us choose the
confidence level of 95%. Then VaR is the sixth smallest number in
the sample while ETL is the average of the five smallest numbers
within the sample. In the general case of N observations, VaR at the
confidence level a is the [N(1 a) þ 1] lowest observation and ETL is
the average of N(1 a) smallest observations.
The well-known problem with the historical simulation is handling
of old data. First, ‘‘too old’’ data may lose their relevance. Therefore,
moving data windows (i.e., fixed number of observations prior to
every new period) are often used. Another subject of concern is
outliers. Different data weighting schemes are used to address this
problem. In a simple approach, the historical data X(t k) are multi-
plied by the factor l
where 0 < l < 1. Another interesting idea is
weighting the historical data with their volatility [4]. Namely, the asset
returns R(t) at time t used in forecasting VaR for time T are scaled
with the volatility ratio
(t) ¼ R(t)s(T)=s(t) (11:2:1)
where s(t) is the historical forecast of the asset volatility.
As a result,
the actual return at day t is increased if the volatility forecast at day T
is higher than that of day t, and vice versa. The scaled forecasts R
are further used in calculating VaR in the same way as the forecasts
R(t) are used in equal-weight historical simulation. Other more so-
phisticated non-parametric techniques are discussed in [2] and refer-
ences therein.
An obvious advantage of the non-parametric approaches is their
relative conceptual and implementation simplicity. The main disad-
vantage of the non-parametric approaches is their absolute depend-
ence on the historical data: Collecting and filtering empirical data
always comes at a price.
The parametric approach is a plausible alternative to historical
simulation. This approach is based on fitting the P/L probability
distribution to some analytic function. The (log)normal, Student
Market Risk Measurement 125
and extreme value distributions are commonly used in modeling P/L
[2, 5]. The parametric approach is easy to implement since the analytic
expressions can often be used. In particular, the assumption of the
normal distribution reduces calculating VaR to (11.1.2). Also, VaR
for time interval T can be easily expressed via VaR for unit time (e.g.,
via daily VaR (DVaR) providing T is the number of days)
VaR(T) ¼ DVaR
VaR for a portfolio of N assets is calculated using the variance of the
multivariate normal distribution
, j¼1
If the P/L distribution is normal, ETL can also be calculated analyt-
ETL(a) ¼ sP
)=(1 a) m (11:2:4)
The value z
in (11.2.4) is determined with (11.1.3). Obviously, the
parametric approach is as good and accurate as the choice of the
analytic probability distribution.
Calculating VaR has become a part of the regulatory environment
in the financial industry [6]. As a result, several methodologies have
been developed for testing the accuracy of VaR models. The most
widely used method is the Kupiec test. This test is based on the
assumption that if the VaR(a) model is accurate, the number of the
tail losses n in a sample N is determined with the binomial distribu-
(n; N, 1 a) ¼
n!(N n)!
(1 a)
The null hypothesis is that n/N equals 1 a, which can be tested with
the relevant likelihood ratio statistic. The Kupiec test has clear mean-
ing but may be inaccurate for not very large data samples. Other
approaches for testing the VaR models are described in [2, 6] and
references therein.
126 Market Risk Measurement

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