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Range-based models in estimating value-at-risk (VaR)

Author

Listed:
  • Nikkin L. Beronilla

    (Institute for Popular Democracy)

  • Dennis S. Mapa

    (University of the Philippines School of Statistics)

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 based on either the Parkinson range or the Garman-Klass range, are applied to ten 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 autoregressive moving average (ARMA)-GARCH models and RiskMetricsTM. The performance of the different models is assessed using the out-ofsample 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 ten 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 nine out of ten stock market indices. This paper finds that range-based GARCH models are good alternatives in modeling volatility and in estimating VaR.

Suggested Citation

  • Nikkin L. Beronilla & Dennis S. Mapa, 2008. "Range-based models in estimating value-at-risk (VaR)," Philippine Review of Economics, University of the Philippines School of Economics and Philippine Economic Society, vol. 45(2), pages 87-99, December.
    • Handle: RePEc:phs:prejrn:v:45:y:2008:i:2:p:87-99
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    File URL: http://pre.econ.upd.edu.ph/index.php/pre/article/view/178/643
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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.

    More about this item

    Keywords

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    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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