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A Quantile Regression Approach to the Multiple Period Value at Risk Estimation

Author

Listed:
  • Chi Ming Wong

    (School of Mathematical and Physical Sciences, University of Technology Sydney, Australia)

  • Lei Lam Olivia Ting

    (DBS Bank, Hong Kong SAR)

Abstract

This research focuses on methods for multiple period Value at Risk (VaR) estimation by utilizing some common approaches like RiskMetrics and empirical distribution and examining quantile regression. In a simulation study we compare the least square and quantile regression percentiles with the actual percentiles for different error distributions. We also discuss the method of selecting response and explanatory variables for the quantile regression approach. In an empirical study, we apply the three VaR estimation approaches to the aggregate returns of four major market indices. The results indicate that the quantile regression approach is better than the other two approaches.

Suggested Citation

  • Chi Ming Wong & Lei Lam Olivia Ting, 2016. "A Quantile Regression Approach to the Multiple Period Value at Risk Estimation," Journal of Economics and Management, College of Business, Feng Chia University, Taiwan, vol. 12(1), pages 1-35, February.
    • Handle: RePEc:jec:journl:v:12:y:2016:i:1:p:1-35
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    References listed on IDEAS

    as
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    Cited by:

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    2. Petrella, Lea & Raponi, Valentina, 2019. "Joint estimation of conditional quantiles in multivariate linear regression models with an application to financial distress," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 70-84.

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    More about this item

    Keywords

    quantile regression; value at risk; risk measures;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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