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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

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.

Suggested Citation

  • Rubia, Antonio & Sanchis-Marco, Lidia, 2013. "On downside risk predictability through liquidity and trading activity: A dynamic quantile approach," International Journal of Forecasting, Elsevier, vol. 29(1), pages 202-219.
  • 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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    11. Sousa, Ricardo M. & Vivian, Andrew & Wohar, Mark E., 2016. "Predicting asset returns in the BRICS: The role of macroeconomic and fundamental predictors," International Review of Economics & Finance, Elsevier, vol. 41(C), pages 122-143.
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