Equity Premium Prediction by Sparse Pooling of Parsimonious State-Dependent Models

Posted: 16 Aug 2018

See all articles by Daniel de Almeida

Daniel de Almeida

Universidad Carlos III de Madrid

Ana-Maria Fuertes

Cass Business School, City University of London

Luiz Koodi Hotta

University of Campinas (UNICAMP) - Department of Statistics

Date Written: August 1, 2018

Abstract

A large set of macroeconomic variables have been suggested as equity risk premium predictors in the literature. This paper proposes a forecasting approach for the equity risk premium with two novel features. First, individual month-ahead forecasts are obtained from parsimonious threshold regression models that exploit from one to at most three macroeconomic predictors jointly, and a binary technical indicator as an observable state variable to accommodate expansion versus recession predictability states. Second, a sparse combination of those forecasts is carried out through a robust predictability test. A comprehensive out-of-sample forecast evaluation exercise based on statistical criteria and asset-allocation criteria demonstrates that both features of the proposed approach enable gains versus existing forecasting techniques. However, the state-dependent aspect of the forecasts delivers larger improvements in forecast accuracy that the sparse combination aspect. The results are robust to sub-period analyses, expanding versus rolling estimation windows, and different investors' risk aversion levels.

Keywords: Equity Risk Premium, Forecast Combination, Technical Indicators, Out-of-Sample, Business Cycles

JEL Classification: C22, C38, C53, C58, E32, G11, G12, G14, G17

Suggested Citation

de Almeida, Daniel and Fuertes, Ana-Maria and Hotta, Luiz Koodi, Equity Premium Prediction by Sparse Pooling of Parsimonious State-Dependent Models (August 1, 2018). Available at SSRN: https://ssrn.com/abstract=3224415

Daniel De Almeida

Universidad Carlos III de Madrid ( email )

CL. de Madrid 126
Madrid, Madrid 28903
Spain

Ana-Maria Fuertes (Contact Author)

Cass Business School, City University of London ( email )

Faculty of Finance
106 Bunhill Row
London, EC1Y 8TZ
United Kingdom
+44 207 477 0186 (Phone)
+44 207 477 8881 (Fax)

HOME PAGE: http://www.city.ac.uk/people/academics/ana-maria-fuertes

Luiz Koodi Hotta

University of Campinas (UNICAMP) - Department of Statistics ( email )

Campinas, São Paulo 13083-859
Brazil

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