Forecasting Dynamic Return Distributions Based on Ordered Binary Choice

Abstract

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