Template-Type: ReDIF-Paper 1.0
Author-Name: Chen, C.W.S.
Author-Name-Last: Chen
Author-Name-First: Cathy
Author-Name: Gerlach, R.
Author-Name-Last: Gerlach
Author-Name-First: Richard
Author-Name: Hwang, B.B.K.
Author-Name-Last: Hwang
Author-Name-First: Bruce
Author-Name: McAleer, M.J.
Author-Name-Last: McAleer
Author-Name-First: Michael
Author-Person: pmc90
Title: Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intraday Range
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.
Creation-Date: 2011-06-30
File-URL: https://repub.eur.nl/pub/23795/EI2011-17.pdf
File-Format: application/pdf
Series: RePEc:ems:eureir
Number: EI 2011-17
Keywords: CAViaR model, Markov chain Monte Carlo, Skewed-Laplace distribution, Value-at-Risk, backtesting, intra-day range
Handle: RePEc:ems:eureir:23795