Predictable Return Distributions

Forthcoming in Journal of Forecasting

38 Pages Posted: 15 Aug 2010 Last revised: 19 Dec 2014

Date Written: November 31, 2014


Using quantile regression this paper explores predictability of the stock and bond return distributions as a function of economic state variables. The use of quantile regression allows us to examine specific parts of the return distribution such as the tails and the center, and for a sufficiently fine grid of quantiles we can trace out the entire distribution. A univariate quantile regression model is used to examine the marginal stock and bond return distributions, while a multivariate model is used to capture their joint distribution. An empirical analysis on US data shows that economic state variables predict the stock and bond return distributions in quite different ways in terms of, for example, location-shifts, volatility, and skewness. Comparing the different economic state variables in terms of their out-of-sample forecasting performance, the empirical analysis also shows that the relative accuracy of the state variables varies across the return distribution. Density forecasts based on an assumed normal distribution with forecasted mean and variance is compared to forecasts based on quantiles estimates, and in general the latter yields the best performance.

Keywords: Return predictability, return distribution, quantile regression, multivariate model, out-of-sample forecast

JEL Classification: C21, C31, G12, G17

Suggested Citation

Pedersen, Thomas Quistgaard, Predictable Return Distributions (November 31, 2014). Forthcoming in Journal of Forecasting, Available at SSRN: or

Thomas Quistgaard Pedersen (Contact Author)

Aarhus University - CREATES ( email )

School of Economics and Management
Building 1322, Bartholins Alle 10
DK-8000 Aarhus C

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