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Range-Based Models in Estimating Value-at-Risk (VaR)

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  • Mapa, Dennis
  • Beronilla, Nikkin

Abstract

This paper introduces new methods of estimating Value-at-Risk (VaR) using Range-Based GARCH (General Autoregressive Conditional Heteroskedasticity) models. These models, which could be either based on the Parkinson Range or Garman-Klasss Range, are applied to 10 stock market indices of selected countries in the Asia-Pacific Region. The results are compared using the traditional methods such as the econometric method based on the ARMA-GARCH models and RiskMetricsTM. The performance of the different models is assessed using the out-of-sample VaR forecasts. Series of likelihood ratio (LR) tests namely: LR of unconditional coverage (LRuc), LR of independence (LRind), and LR of conditional coverage (LRcc) are performed for comparison. The result of the assessment shows that the model based on the Parkinson Range GARCH (1,1) with Student’s t distribution is the best performing model on the 10 stock market indices. It has a failure rate, defined as the percentage of actual return that is smaller than the one-step-ahead VaR forecast, of zero in 9 out 10 stock market indices. The finding of this paper is that Range-Based GARCH Models are good alternatives in modeling volatility and in estimating VaR.

Suggested Citation

  • Mapa, Dennis & Beronilla, Nikkin, 2008. "Range-Based Models in Estimating Value-at-Risk (VaR)," MPRA Paper 21223, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:21223
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    References listed on IDEAS

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    7. Mapa, Dennis S., 2003. "A Range-Based GARCH Model for Forecasting Volatility," MPRA Paper 21323, University Library of Munich, Germany.
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    Cited by:

    1. Dilip Kumar, 2020. "Value-at-Risk in the Presence of Structural Breaks Using Unbiased Extreme Value Volatility Estimator," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 18(3), pages 587-610, September.
    2. Edward P. Santos & Dennis S. Mapa & Eloisa T. Glindro, 2010. "Estimating inflation-at-risk (IaR) using extreme value theory (EVT)," Philippine Review of Economics, University of the Philippines School of Economics and Philippine Economic Society, vol. 47(2), pages 21-40, December.
    3. Dilip Kumar, 2016. "Estimating and forecasting value-at-risk using the unbiased extreme value volatility estimator," Proceedings of Economics and Finance Conferences 3205528, International Institute of Social and Economic Sciences.

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    More about this item

    Keywords

    Value-at-Risk (VaR); Parkinson Range; Garman-Klasss Range; Range-Based GARCH (General Autoregressive Conditional Heteroskedasticity);
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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