Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intraday Range
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|
|Organisation||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 http://hdl.handle.net/1765/23795