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On downside risk predictability through liquidity and trading activity: a quantile regression approach

Author

Listed:
  • Lidia Sanchis-Marco

    (Dpto. Análisis Económico y Finanzas)

  • Antonio Rubia Serrano

    (Universidad de Alicante)

Abstract

Most downside risk models implicitly assume that returns are a sufficient statistic with which to forecast the daily conditional distribution of a portfolio. In this paper, we address this question empirically and analyze if the variables that proxy for market liquidity and trading conditions convey valid information to forecast the quantiles of the conditional distribution of several representative market portfolios. Using quantile regression techniques, we report evidence of predictability that can be exploited to improve Value at Risk forecasts. Including trading- and spread-related variables improves considerably the forecasting performance.

Suggested Citation

  • Lidia Sanchis-Marco & Antonio Rubia Serrano, 2011. "On downside risk predictability through liquidity and trading activity: a quantile regression approach," Working Papers. Serie AD 2011-14, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    • Handle: RePEc:ivi:wpasad:2011-14
    as

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    References listed on IDEAS

    as
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    Keywords

    Value at Risk; Basel; Liquidity; Trading Activity.;
    All these keywords.

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