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Estimating the Value-at-Risk from High-frequency Data

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  • Pavol Krasnovský

Abstract

We present two alternative approaches for estimating VaR. Both approaches are based on the observation that each trading day is very diverse and we can observe K different phases of the trading day. We can not observe from which of the K phases our observations rt are. Therefore, we apply Gibbs sampler to estimate parameters from our data. In the latter approach, we apply Dubins and Schwarz theorem (Kallenberg, 2000), which allows us to re-scale our portfolio returns rt and to get normal distributed returns rJt~N(0,Jt). To verify our approaches, we make an empirical application.

Suggested Citation

  • Pavol Krasnovský, 2015. "Estimating the Value-at-Risk from High-frequency Data," European Financial and Accounting Journal, Prague University of Economics and Business, vol. 2015(2), pages 5-11.
    • Handle: RePEc:prg:jnlefa:v:2015:y:2015:i:2:id:138:p:5-11
    DOI: 10.18267/j.efaj.138
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    References listed on IDEAS

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    1. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
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    More about this item

    Keywords

    Data augmentation; Gibbs sampler; Quadratic variation; Time changed Brownian motion;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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