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Estimating stochastic discount factor models with hidden regimes: Applications to commodity pricing

Marta Giampietro, Massimo Guidolin () and Manuela Pedio

European Journal of Operational Research, 2018, vol. 265, issue 2, 685-702

Abstract: We develop new likelihood-based methods to estimate factor-based Stochastic Discount Factors (SDF) that may accommodate Hidden Markov dynamics in the factor loadings. We use these methods to investigate whether it is possible to find a SDF that jointly prices the cross-section of eight U.S. portfolios of stocks, Treasuries, corporate bonds, and commodities. In particular, we test a range of possible different specification of the SDF, including single-state and Hidden Markov models and compare their statistical and pricing performances. In addition, we assess whether and to which extent a selection of these models replicates the observed moments of the return series, and especially correlations. We report that regime-switching models clearly outperform single-state ones both in term of statistical and pricing accuracy. However, while a four-state model is selected by the information criteria, a two-state three-factor full Vector Autoregression model outperforms the others as far as the pricing accuracy is concerned.

Keywords: Finance; Commodities; Stochastic discount factor; Hidden Markov model (search for similar items in EconPapers)
Date: 2018
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Working Paper: Estimating Stochastic Discount Factor Models with Hidden Regimes: Applications to Commodity Pricing (2017) Downloads
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