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.

CAViaR model, Markov chain Monte Carlo, Skewed-Laplace distribution, Value-at-Risk, backtesting, intra-day range
Erasmus School of Economics
Econometric Institute Research Papers
Report / Econometric Institute, Erasmus University Rotterdam
Erasmus School of Economics

Chen, C.W.S, Gerlach, R, Hwang, B.B.K, & McAleer, M.J. (2011). Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intraday Range (No. EI 2011-17). Report / Econometric Institute, Erasmus University Rotterdam (pp. 1–38). Erasmus School of Economics. Retrieved from