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

  • Rubia, Antonio
  • Sanchis-Marco, Lidia
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    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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    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
    DOI: 10.1016/j.ijforecast.2012.09.001
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