Distilling Large Information Sets to Forecast Commodity Returns: Automatic Variable Selection or Hidden Markov Models?

55 Pages Posted: 26 May 2020 Last revised: 5 Jun 2020

See all articles by Massimo Guidolin

Massimo Guidolin

Bocconi University - Department of Finance

Manuela Pedio

University of Bristol; Bocconi University - CAREFIN - Centre for Applied Research in Finance

Date Written: May 1, 2020

Abstract

We investigate the out-of-sample, recursive predictive accuracy for (fully hedged) commodity future returns of two sets of forecasting models, i.e., hidden Markov chain models in which the coefficients of predictive regressions follow a regime switching process and stepwise variable selection algorithms in which the coefficients of predictors not selected are set to zero. We perform the analysis under four alternative loss functions, i.e., squared and the absolute value, and the realized, portfolio Sharpe ratio and MV utility when the portfolio is built upon optimal weights computed solving a standard MV portfolio problem. We find that neither HMM or stepwise regressions manage to systematically (or even just frequently) outperform a plain vanilla AR benchmark according to RMSFE or MAFE statistical loss functions. However, in particular stepwise variable selection methods create economic value in out-of-sample meanvariance portfolio tests. Because we impose transaction costs not only ex post but also ex ante, so that an investor uses the forecasts of a model only when they increase expected utility,

Keywords: Backward and forward stepwise regressions; hidden Markov models, out-of-sample forecasting; commodity futures returns; mean-variance portfolios.

Suggested Citation

Guidolin, Massimo and Pedio, Manuela, Distilling Large Information Sets to Forecast Commodity Returns: Automatic Variable Selection or Hidden Markov Models? (May 1, 2020). BAFFI CAREFIN Centre Research Paper No. 2020-140, Available at SSRN: https://ssrn.com/abstract=3606933 or http://dx.doi.org/10.2139/ssrn.3606933

Massimo Guidolin (Contact Author)

Bocconi University - Department of Finance ( email )

Via Roentgen 1
Milano, MI 20136
Italy

Manuela Pedio

University of Bristol ( email )

University of Bristol,
Senate House, Tyndall Avenue
Bristol, BS8 ITH
United Kingdom

Bocconi University - CAREFIN - Centre for Applied Research in Finance ( email )

Via Sarfatti, 25
Milan, 20136
Italy

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