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On downside risk predictability through liquidity and trading activity: A dynamic quantile approach

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  • Rubia, Antonio
  • Sanchis-Marco, Lidia
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    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 analyze whether the variables that proxy for market-wide liquidity and trading conditions convey valid information for forecasting the quantiles of the conditional distribution of several representative market portfolios, including volume- and value-weighted market portfolios, and several Book-to-Market- and Size-sorted portfolios. Using dynamic quantile regression techniques, we report evidence of conditional tail predictability in terms of these variables. A comprehensive backtesting analysis shows that this link can be exploited in dynamic quantile modelling, in order to considerably improve the performances of day-ahead Value at Risk forecasts.

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    Bibliographic Info

    Article provided by Elsevier in its journal International Journal of Forecasting.

    Volume (Year): 29 (2013)
    Issue (Month): 1 ()
    Pages: 202-219

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    Handle: RePEc:eee:intfor:v:29:y:2013:i:1:p:202-219

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    Web page: http://www.elsevier.com/locate/ijforecast

    Related research

    Keywords: Value at risk; Liquidity; Trading activity; Non-linear quantile regression; CAViaR;

    References

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    Cited by:
    1. López-Espinosa, Germán & Rubia, Antonio & Valderrama, Laura & Antón, Miguel, 2013. "Good for one, bad for all: Determinants of individual versus systemic risk," Journal of Financial Stability, Elsevier, vol. 9(3), pages 287-299.

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