Forecasting Dynamic Return Distributions Based on Ordered Binary Choice
30 Pages Posted: 17 Nov 2017 Last revised: 9 Jan 2019
Date Written: November 15, 2017
We present a simple approach to forecasting conditional probability distributions of asset returns. We work with a parsimonious specification of ordered binary choice regression that imposes a connection on sign predictability across different quantiles. The model forecasts the future conditional probability distributions of returns quite precisely when using a past indicator and past volatility proxy as predictors. Direct benefits of the model are revealed in an empirical application to the 29 most liquid U.S. stocks. The forecast probability distribution is translated to significant economic gains in a simple trading strategy. Our approach can also be useful in many other applications where conditional distribution forecasts are desired.
Keywords: asset returns, predictive distribution, conditional probability, probability forecasting, ordered binary choice
JEL Classification: C22, C58, G17
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