Improving grid-based methods for estimating value at risk of fixed-income portfolios
AbstractJamshidian and Zhu (1997) propose a discrete grid method for simplifying the computation of Value at Risk (VaR) for fixed-income portfolios. Their method relies on two simplifications. First, the value of fixed income instruments is modeled as depending on a small number of risk factors chosen using principal components analysis. Second, they use a discrete approximation to the distribution of the portfolio's value. We show that their method has two serious shortcomings which imply it cannot accurately estimate VaR for some fixed-income portfolios. First, risk factors chosen using principal components analysis will explain the variation in the yield curve, but they may not explain the variation in the portfolio's value. This will be especially problematic for portfolios that are hedged. Second, their discrete distribution of portfolio value can be a poor approximation to the true continuous distribution. We propose two refinements to their method to correct these two shortcomings. First, we propose choosing risk factors according to their ability to explain the portfolio's value. To do this, instead of generating risk factors with principal components analysis, we generate them with a statistical technique called partial least squares. Second, we compute VaR with a ``Grid Monte Carlo'' method that uses continuous risk factor distributions while maintaining the computational simplicity of a grid method for pricing. We illustrate our points with several example portfolios where the Jamshidian-Zhu method fails to accurately estimate VaR, while our refinements succeed.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Board of Governors of the Federal Reserve System (U.S.) in its series Finance and Economics Discussion Series with number 2000-25.
Date of creation: 2000
Date of revision:
This paper has been announced in the following NEP Reports:
- NEP-ALL-2000-07-11 (All new papers)
- NEP-CFN-2000-07-11 (Corporate Finance)
- NEP-FIN-2000-07-11 (Finance)
- NEP-FMK-2000-07-11 (Financial Markets)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Matthew Pritsker, 1997. "Evaluating Value at Risk Methodologies: Accuracy versus Computational Time," Journal of Financial Services Research, Springer, vol. 12(2), pages 201-242, October.
- Farshid Jamshidian & Yu Zhu, 1996. "Scenario Simulation: Theory and methodology (*)," Finance and Stochastics, Springer, vol. 1(1), pages 43-67.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Kris Vajs) The email address of this maintainer does not seem to be valid anymore. Please ask Kris Vajs to update the entry or send us the correct address.
If references are entirely missing, you can add them using this form.