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



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Chen, Cathy W. S. and Gerlach, Richard and Hwang, Bruce B. K. and McAleer, Michael (2011) Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intra-day Range. [ Documentos de Trabajo del Instituto Complutense de Análisis Económico; nº 16, 2011, ] (Unpublished)

Creative Commons Attribution Non-commercial.


Official URL: http://eprints.ucm.es/12755/



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 pro-pose 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 fnancial crisis, and evaluate how the crisis afects the performance of risk models via forecasting VaR. Empirical analysis is conducted
on five Asia-Pacific Economic Cooperation stock market indices as well as two exchange rate series. We examine violation rates, back-testing criteria, market risk charges and quantile loss function values to measure and assess the forecasting performance of a variety of risk models. The proposed threshold CAViaR model, incorporating range information, is shown to forecast VaR more eficiently than other models, across the series considered, which should
be useful for financial practitioners.

Item Type:Working Paper or Technical Report
Uncontrolled Keywords:Value-at-Risk, CAViaR model, Skewed-Laplace distribution, Intra-day range, Backtesting, Markov chain Monte Carlo.
Subjects:Social sciences > Economics > Econometrics
Social sciences > Economics > Economic indicators
Series Name:Documentos de Trabajo del Instituto Complutense de Análisis Económico
ID Code:12755
Deposited On:01 Jun 2011 06:51
Last Modified:06 Feb 2014 09:31

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