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A simple two-component model for the distribution of intraday returns

Research output: Contribution to journalArticle



Publication details

JournalEuropean Journal of Finance
DateE-pub ahead of print - 9 Sep 2011
DatePublished (current) - Oct 2012
Issue number9
Number of pages23
Pages (from-to)775–797
Early online date9/09/11
Original languageEnglish


We model the conditional probability law of high frequency financial returns by means of quantile regression. Using three years of 30 minutes sampled returns for a set of stocks traded at the Spanish Stock Exchange, a pure limit order book electronic platform, we show that the conditional probability density depends on past returns and on the time of the day. Two practical applications illustrate the usefulness of the methodology. First, we provide quantile-based measures of conditional volatility, asymmetry and kurtosis that do not depend on the existence of moments. We find seasonal patterns and time dependencies beyond volatility. Second, we estimate and forecast intraday Value at Risk. A battery of tests show that our methodology delivers good risk assessments for intraday returns, and it clearly outperforms GARCH-based Value at Risk assessments.

    Research areas

  • intraday returns, quantile regression, intraday VaR

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