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

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Author Info

  • Cathy W. S. Chen

    (Graduate Institute of Statistics and Actuarial Science, Feng Chia University)

  • Richard Gerlach

    (University of Sydney Business School, Australia.)

  • Bruce B. K. Hwang

    (Graduate Institute of Statistics and Actuarial Science, Feng Chia University)

  • Michael McAleer

    (Econometrisch Instituut (Econometric Institute), Faculteit der Economische Wetenschappen (Erasmus School of Economics) Erasmus Universiteit, Tinbergen Instituut (Tinbergen Institute).)

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 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 financial crisis, and evaluate how the crisis a ects 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.

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Bibliographic Info

Paper provided by Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico in its series Documentos de Trabajo del ICAE with number 2011-16.

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Length: 40 pages
Date of creation: 2011
Date of revision:
Handle: RePEc:ucm:doicae:1116

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Related research

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

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References

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  1. Michael McAleer & Juan-Ángel Jiménez-Martín & Teodosio Pérez-Amaral, 2011. "Has the Basel II Accord Encouraged Risk Management During the 2008-09 Financial Crisis?," KIER Working Papers 767, Kyoto University, Institute of Economic Research.
  2. McAleer, M.J. & Jimenez-Martin, J-A. & Perez-Amaral, T., 2012. "Has the Basel Accord Improved Risk Management During the Global Financial Crisis?," Econometric Institute Research Papers EI 2012-34, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  3. Chan, Nancy Y. C. & Chen, Cathy W.S. & Gerlach, Richard, 2009. "Bayesian time-varying quantile forecasting for Value-at-Risk in financial markets," Working Papers 01/2009, University of Sydney Business School, Discipline of Business Analytics.
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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, Elsevier, vol. 29(1), pages 28-42.

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