Variance Reduction Techniques for Estimating Value-at-Risk

By Paul Glasserman, Philip Heidelberger, Perwez Shahabuddin. 2000

238 citations recorded by the source corpus.

Subfields: Demography, Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/b8b0294b9eafd02102365778b9887bada30951a4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Demography

1. Christiane Lemieux
2. Lennart F. Hoogerheide
3. H. V. Dijk

In Finance

1. Christiane Lemieux
2. Lennart F. Hoogerheide
3. H. V. Dijk
4. Csaba Szepesvari
5. Zhonghua Han
6. Stefan Görtz
7. Josef Dick
8. Erich Novak

In Management Science and Operations Research

1. Christiane Lemieux
2. Lennart F. Hoogerheide
3. H. V. Dijk

Citations in the corpus

• Bayesian Forecasting of Value at Risk and Expected Shortfall Using Adaptive Importance Sampling (2010, methodology): “which is also the relative gain for the VaR estimator’s precision (from formulas (12)-(13)).”
• Portfolio Value‐at‐Risk with Heavy‐Tailed Risk Factors (2002, unknown): “In Glasserman et al. (2000) we showed that if L ¼ a0 þ Q holds exactly, then this estimator (with suitable h) is asymptotically optimal in the sense that its second moment decreases exponentially (as x !”
• Monte Carlo and Quasi-Monte Carlo Sampling (2009, result): “Although this idea has been studied by other authors, here we restrict our attention to the approach proposed by Glasserman, Heidelberger and Shahabuddin [132, 133], which is also discussed in [136].”