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

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  • Chen, C.W.S.
  • Gerlach, R.
  • Hwang, B.B.K.
  • McAleer, M.J.

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.

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Bibliographic Info

Paper provided by Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute in its series Econometric Institute Research Papers with number EI 2011-17.

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Date of creation: 30 Jun 2011
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Handle: RePEc:ems:eureir:23795

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Keywords: CAViaR model; Markov chain Monte Carlo; Skewed-Laplace distribution; Value-at-Risk; backtesting; intra-day range;

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Cited by:
  1. Fuertes, Ana-Maria & Olmo, Jose, 2013. "Optimally harnessing inter-day and intra-day information for daily value-at-risk prediction," International Journal of Forecasting, Elsevier, vol. 29(1), pages 28-42.

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