The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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Value at Risk Forecasting Based on Quantile Regression for GARCH Models

  • Lee, Sang-Yeol (Department of Statistics, Seoul National University) ;
  • Noh, Jung-Sik (Department of Statistics, Seoul National University)
  • Received : 20100500
  • Accepted : 20100700
  • Published : 2010.08.31
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Abstract

Value-at-Risk(VaR) is an important part of risk management in the financial industry. This paper present a VaR forecasting for financial time series based on the quantile regression for GARCH models recently developed by Lee and Noh (2009). The proposed VaR forecasting features the direct conditional quantile estimation for GARCH models that is well connected with the model parameters. Empirical performance is measured by several backtesting procedures, and is reported in comparison with existing methods using sample quantiles.

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References

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Cited by

  1. Forecasting value-at-risk by encompassing CAViaR models via information criteria vol.24, pp.6, 2013, https://doi.org/10.7465/jkdi.2013.24.6.1531
  2. Quantile Regression Estimator for GARCH Models vol.40, pp.1, 2013, https://doi.org/10.1111/j.1467-9469.2011.00759.x