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Irregularly Spaced Intraday Value-at-Risk (ISIVaR) Models: Forecasting and Predictive Abilities

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  • Christophe Hurlin

    (LEO - Laboratoire d'économie d'Orleans [2008-2011] - UO - Université d'Orléans - CNRS - Centre National de la Recherche Scientifique)

Abstract

The objective of this paper is to propose a market risk measure defined in price event time and a suitable backtesting procedure for irregularly spaced data. Firstly, we combine Autoregressive Conditional Duration models for price movements and a non parametric quantile estimation to derive a semi-parametric Irregularly Spaced Intraday Value at Risk (ISIVaR) model. This ISIVaR measure gives two information: the expected duration for the next price event and the related VaR. Secondly, we use a GMM approach to develop a backtest and investigate its finite sample properties through numerical Monte Carlo simulations. Finally, we propose an application to two NYSE stocks.
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Suggested Citation

  • Christophe Hurlin, 2007. "Irregularly Spaced Intraday Value-at-Risk (ISIVaR) Models: Forecasting and Predictive Abilities," Post-Print halshs-00257452, HAL.
    • Handle: RePEc:hal:journl:halshs-00257452
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    Cited by:

    1. Karmakar, Madhusudan & Paul, Samit, 2016. "Intraday risk management in International stock markets: A conditional EVT approach," International Review of Financial Analysis, Elsevier, vol. 44(C), pages 34-55.

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