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Portfolio value-at-risk with heavytailed risk factors (2000)
| Content Provider | CiteSeerX |
|---|---|
| Author | Glasserman, Paul Heidelberger, Philip Shahabuddin, Perwez |
| Abstract | This paper develops efficient methods for computing portfolio value-at-risk (VAR) when the underlying risk factors have a heavy-tailed distribution. In modeling heavy tails, we focus on multivariate t distributions and some extensions thereof. We develop two methods for VAR calculation that exploit a quadratic approximation to the portfolio loss, such as the delta-gamma approximation. In the first method, we derive the characteristic function of the quadratic approximation and then use numerical transform inversion to approximate the portfolio loss distribution. Because the quadratic approximation may not always yield accurate VAR estimates, we also develop a low variance Monte Carlo method. This method uses the quadratic approximation to guide the selection of an effective importance sampling distribution that samples risk factors so that large losses occur more often. Variance is further reduced by combining the importance sampling with stratified sampling. Numerical results on a variety of test portfolios indicate that large variance reductions are typically obtained. Both methods developed in this paper overcome difficulties associated with VAR calculation with heavy-tailed risk factors. The Monte Carlo method also extends to the problem of estimating the conditional excess, sometimes known as the conditional VAR. |
| File Format | |
| Volume Number | 12 |
| Journal | Mathematical Finance |
| Language | English |
| Publisher Date | 2000-01-01 |
| Access Restriction | Open |
| Subject Keyword | Portfolio Value-at-risk Quadratic Approximation Heavytailed Risk Factor Var Calculation Risk Factor Underlying Risk Factor Characteristic Function Monte Carlo Method Efficient Method Stratified Sampling Portfolio Loss Heavy-tailed Risk Factor Heavy-tailed Distribution First Method Low Variance Monte Carlo Method Large Loss Multivariate Distribution Test Portfolio Conditional Var Paper Overcome Difficulty Conditional Excess Effective Importance Large Variance Reduction Accurate Var Estimate Delta-gamma Approximation Portfolio Loss Distribution Heavy Tail Numerical Result Numerical Transform Inversion |
| Content Type | Text |
| Resource Type | Article |
