Range-Based Models in Estimating Value-at-Risk (VaR)

Dennis Mapa () and Nikkin Beronilla

MPRA Paper from University Library of Munich, Germany

Abstract: This paper introduces new methods of estimating Value-at-Risk (VaR) using Range-Based GARCH (General Autoregressive Conditional Heteroskedasticity) models. These models, which could be either based on the Parkinson Range or Garman-Klasss Range, are applied to 10 stock market indices of selected countries in the Asia-Pacific Region. The results are compared using the traditional methods such as the econometric method based on the ARMA-GARCH models and RiskMetricsTM. The performance of the different models is assessed using the out-of-sample VaR forecasts. Series of likelihood ratio (LR) tests namely: LR of unconditional coverage (LRuc), LR of independence (LRind), and LR of conditional coverage (LRcc) are performed for comparison. The result of the assessment shows that the model based on the Parkinson Range GARCH (1,1) with Student’s t distribution is the best performing model on the 10 stock market indices. It has a failure rate, defined as the percentage of actual return that is smaller than the one-step-ahead VaR forecast, of zero in 9 out 10 stock market indices. The finding of this paper is that Range-Based GARCH Models are good alternatives in modeling volatility and in estimating VaR.

Keywords: Value-at-Risk (VaR); Parkinson Range; Garman-Klasss Range; Range-Based GARCH (General Autoregressive Conditional Heteroskedasticity) (search for similar items in EconPapers)
JEL-codes: C22 C51 C52 (search for similar items in EconPapers)
Date: 2008
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Published in The Philippine Review of Economics 2.XLV(2008): pp. 87-100

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Journal Article: Range-based models in estimating value-at-risk (VaR) (2008)
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