Irregularly Spaced Intraday Value at Risk (ISIVaR) Models : Forecasting and Predictive Abilities
AbstractThe 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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Bibliographic InfoPaper provided by HAL in its series Working Papers with number halshs-00162440.
Date of creation: 13 Jul 2007
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Value at Risk; High-frequency data; ACD models; Irregularly spaced market risk models; Backtesting;
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