# 10.4 Monte Carlo Transformation Procedures

If a portfolio mapping is neither linear nor quadratic, we may apply a function remapping to make it such. If doing so would entail too large an approximation error, we resort instead to transformations that employ numerical methods of integration. Because of the high dimensionality of many value-at-risk analyses, we focus on the Monte Carlo method. Numerical transformations based upon the Monte Carlo method were applied to PMMRs as early as Lietaer (1971).

Consider a portfolio (^{0}*p*,^{1}*P*) with mapping ^{1}*P* = θ(^{1}** R**). Based upon a sample {

^{1}

*R*^{[1]},

^{1}

*R*^{[2]}, … ,

^{1}

*R*^{[m]}} for

^{1}

**, we define a sample {**

*R*^{1}

*P*

^{[1]},

^{1}

*P*

^{[2]}, … ,

^{1}

*P*

^{[m]}} for

^{1}

*P*with

^{1}

*P*

^{[k]}= θ(

^{1}

*R*^{[k]}). Value-at-risk—or any reasonable PMMR—may be estimated by applying a suitable sample estimator to {

^{1}

*P*

^{[1]},

^{1}

*P*

^{[2]}, … ,

^{1}

*P*

^{[m]}}. The result is a crude Monte Carlo estimator for the portfolio’s value-at-risk.

To apply the estimator, we need a realization {^{1}*r*^{[1]}, ^{1}*r*^{[2]}, … , ^{1}*r*^{[m]}}. The ^{1}*r*^{[k]} may be pseudorandom vectors constructed as described in Section 5.8. They may also be constructed directly from historical data for key factors, as described in Section 11.2. In the former case, the transformation is called a **Monte Carlo transformation**. In the latter case, it is called an **historical transformation**. The following discussion applies to both. We address issues specific to historical transformations in Section 10.6.

###### 10.4.1 Monte Carlo Standard Error

In implementing Monte Carlo transformations, an important issue is how large a sample size *m* to use. The standard error of the Monte Carlo analysis depends upon several factors:

- sample size
*m*; - the value-at-risk metric—or more generally, the PMMR—being estimated;
- the conditional distribution of
^{1}*P*, which depends upon both the conditional distribution of^{1}and the portfolio’s composition.*R*

As with all Monte Carlo analyses, standard error is inversely proportional to the square root of sample size *m* but is unrelated to the dimensionality *n* of the problem. Below, we explore the effects of chosen PMMR and portfolio composition on standard error.

A simple way to estimate the standard error of a Monte Carlo analysis is to perform the same analysis multiple times—using different pseudorandom vectors each time—and take the standard deviation of the results. For a given portfolio and PMMR, employ a Monte Carlo transformation procedure with sample size *m* = 1000 to approximate a value for the PMMR. Repeat the analysis 50,000 times, each time employing different pseudorandom vectors, to yield 50,000 independent approximations of the PMMR. Taking their sample standard deviation provides an estimate of the standard deviation of a single Monte Carlo analysis for that portfolio/PMMR pair based upon a sample size of *m* = 1000.

###### 10.4.2 Empirical Analysis of Standard Error for Value-at-Risk

By performing the aboves analysis for various portfolio/PMMR pairs, we may obtain a sense of how standard error varies for different portfolios and PMMRs. Exhibit 10.5 indicates the results of such an analysis based upon five portfolios and three PMMRs—for a total of 15 portfolio/PMMR pairs.

We have given the five portfolios descriptive names. Their actual compositions are unimportant. What matters is their conditional PDFs for ^{1}*P*.4 These are graphed in Exhibit 10.6. The first three portfolios are most typical of what is encountered in applications. Portfolio (a) might correspond to diversified or undiversified linear exposures. It might also correspond to highly diversified nonlinear exposures. Portfolios (b) and (c) might correspond to a handful of positive or negative gamma positions. They might also correspond to delta and gamma exposure to a single underlier. Portfolios (d) and (e) might correspond to delta-hedged gamma exposure to a single underlier. These two portfolios are somewhat stylized. We include them in our analysis to illustrate how extreme standard errors might arrise. For all portfolios, ^{0}*E*(^{1}*P*) = ^{0}*p*.

^{1}

*P*are indicated for the five portfolios considered in the analysis of Exhibit 10.5.

Another way to look at our results is to determine, based upon [5.38], for each portfolio/PMMR pair the sample size that would achieve a 1% standard error. Results of this analysis are presented in Exhibit 10.7.

To estimate standard deviation of portfolio value, a sample size of 8000 should ensure a standard error less than 1% for most diversified portfolios. Value-at-risk metrics require larger samples. For 90% VaR or 99% VaR, consider sample sizes of 30,000 or 45,000, respectively.

Even if a portfolio mapping function θ is simple, performing such large numbers of valuations can be computationally expensive. If θ is more complicated, run times may become prohibitive. The worst situation occurs if a portfolio holds exotic derivatives, mortgage-backed securities, or other instruments that must be valued using numerical techniques such as binomial trees or the Monte Carlo method. If θ is a primary mapping, a single portfolio valuation ^{1}*p*^{[k]} = θ(^{1}*r*^{[k]}) might require minutes of processing time. The thousands of valuations required to estimate value-at-risk would take days.

###### Exercises

A Monte Carlo transformation employs a sample size *m* = 5000. For a particular portfolio and PMMR it has a 2.6% standard error. What sample size should be used to achieve a standard error of 1.0%?

Solution

Consider the portfolio of Exercise 10.3. Use a Monte Carlo transformation to evaluate the same metrics:

- standard deviation of 1-day EUR loss,
^{0}*std*(^{1}*L*); and - 90% EUR VaR.

Do your calculations three times, using sample sizes *m* of 100, 1000, and 10,000. Compare your results for the different sample sizes, and compare them with the corresponding results you obtained for Exercise 10.3.

Solution