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

Antonio Rubia and Lidia Sanchis-Marco

International Journal of Forecasting, 2013, vol. 29, issue 1, 202-219

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.

Keywords: Value at risk; Liquidity; Trading activity; Non-linear quantile regression; CAViaR (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (30)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:29:y:2013:i:1:p:202-219

DOI: 10.1016/j.ijforecast.2012.09.001

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