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

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Author Info

  • Christophe Hurlin

    ()
    (LEO - Laboratoire d'économie d'Orleans - CNRS : UMR6221 - Université d'Orléans)

  • Gilbert Colletaz

    (LEO - Laboratoire d'économie d'Orleans - CNRS : UMR6221 - Université d'Orléans)

  • Sessi Tokpavi

    ()
    (LEO - Laboratoire d'économie d'Orleans - CNRS : UMR6221 - Université d'Orléans)

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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File URL: http://halshs.archives-ouvertes.fr/docs/00/16/24/40/PDF/ISIVAR.pdf
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Bibliographic Info

Paper provided by HAL in its series Working Papers with number halshs-00162440.

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Date of creation: 13 Jul 2007
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Handle: RePEc:hal:wpaper:halshs-00162440

Note: View the original document on HAL open archive server: http://halshs.archives-ouvertes.fr/halshs-00162440/en/
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Related research

Keywords: Value at Risk; High-frequency data; ACD models; Irregularly spaced market risk models; Backtesting;

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