Composite Absolute Value and Sign Forecasts

47 Pages Posted: 27 Jan 2021

Date Written: October 16, 2020

Abstract

This paper introduces composite absolute value and sign (CAVS) forecasts, a nonlinear framework that combines forecasts of the sign and absolute value of a time series into conditional mean forecasts. In contrast to linear models, the proposed framework allows different predictors to separately impact the sign and absolute value of the target series. Among other results, I show that the conditional mean can be accurately approximated by the product of mean squared error optimal sign and absolute value forecasts. An empirical application using the FRED-MD dataset shows that CAVS forecasts substantially outperform linear forecasts for series that exhibit persistent volatility dynamics, such as output and interest rates.

Keywords: Forecasting, Directional Predictability, Machine Learning

JEL Classification: C53, C22, C52, C58

Suggested Citation

Souza, André B.M., Composite Absolute Value and Sign Forecasts (October 16, 2020). Available at SSRN: https://ssrn.com/abstract=3712897 or http://dx.doi.org/10.2139/ssrn.3712897

André B.M. Souza (Contact Author)

ESADE Business School ( email )

Av. de Pedralbes, 60-62
Barcelona, 08034
Spain

HOME PAGE: http://www.andrebmsouza.com

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