On downside risk predictability through liquidity and trading activity: A dynamic quantile approach |
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| Institution: | 1. National School of Development, Peking University, Beijing, China;2. School of Banking and Finance, University of International Business and Economics, Beijing, China;1. Research Laboratory for Economy, Management and Quantitative Finance (LaREMFiQ), IHEC, University of Sousse, Tunisia;2. LaREMFiQ, IHEC, University of Sousse, Tunisia |
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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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