Evaluating Portfolio Value-At-Risk Using Semi-Parametric GARCH Models
2009-01-28
Research Paper
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In this paper we examine the usefulness of multivariate semi-parametric GARCH models for evaluating the Value-at-Risk (VaR) of a portfolio with arbitrary weights. We specify and estimate several alternative multivariate GARCH models for daily returns on the S&P 500 and Nasdaq indexes. Examining the within sample VaRs of a set of given portfolios shows that the semi-parametric model performs uniformly well, while parametric models in several cases have unacceptable failure rates. Interestingly, distributional assumptions appear to have a much larger impact on the performance of the VaR estimates than the particular parametric specification chosen for the GARCH equations.
Keywords
Classifications using
Journal of Economic Literature (JEL) Classification System
- G11 : Portfolio Choice; Investment Decisions
- C53 : Forecasting and Other Model Applications
- G3 : Corporate Finance and Governance
- C14 : Semiparametric and Nonparametric Methods
- M : Business Administration and Business Economics; Marketing; Accounting
- C22 : Time-Series Models; Dynamic Quantile Regressions
Automatically Extracted Terms
- model
- distribution
- portfolio
- return
- multivariate
- semi-parametric
- garch
- value-at-risk
- density
- estimate
- failure rates
- innovation
- level
- table
- multivariate garch models
- matrix
- management
- estimation
- asset
- result
