Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intraday Range

Cathy W. S. Chen (), Richard Gerlach, Bruce Hwang and Michael McAleer

No EI 2011-17, Econometric Institute Research Papers from Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute

Abstract: Value-at-Risk (VaR) is commonly used for financial risk measurement. It has recently become even more important, especially during the 2008-09 global financial crisis. We propose some novel nonlinear threshold conditional autoregressive VaR (CAViar) models that incorporate intra-day price ranges. Model estimation and inference are performed using the Bayesian approach via the link with the Skewed-Laplace distribution. We examine how a range of risk models perform during the 2008-09 financial crisis, and evaluate how the crisis affects the performance of risk models via forecasting VaR. Empirical analysis is conducted on five Asia-Pacific Economic Cooperation stock market indices and two exchange rates????. We examine violation rates, back-testing criteria, market risk charges and quantile loss function to measure the forecasting performance of a variety of risk models. The proposed threshold CAViaR model, incorporating range information, is shown to forecast VaR more efficiently than other models, which should be useful for financial practitioners.

Keywords: CAViaR model; Markov chain Monte Carlo; Skewed-Laplace distribution; Value-at-Risk; backtesting; intra-day range (search for similar items in EconPapers)
Date: 2011-06-30
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Related works:
Journal Article: Forecasting Value-at-Risk using nonlinear regression quantiles and the intra-day range (2012)
Working Paper: Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intra-day Range (2011)
Working Paper: Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intra-day Range (2011)
Working Paper: Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intra-day Range (2011)
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