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Estimating Stochastic Discount Factor Models with Hidden Regimes: Applications to Commodity Pricing

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  • Marta Giampietro
  • Massimo Guidolin
  • Manuela Pedio

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. Key words: Finance, Commodities, Stochastic Discount Factor, Hidden Markov model.

Suggested Citation

  • Marta Giampietro & Massimo Guidolin & Manuela Pedio, 2017. "Estimating Stochastic Discount Factor Models with Hidden Regimes: Applications to Commodity Pricing," Working Papers 614, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    • Handle: RePEc:igi:igierp:614
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    Cited by:

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    4. Nguyen, Quynh Nga & Aboura, Sofiane & Chevallier, Julien & Zhang, Lyuyuan & Zhu, Bangzhu, 2020. "Local Gaussian correlations in financial and commodity markets," European Journal of Operational Research, Elsevier, vol. 285(1), pages 306-323.
    5. Shirui Wang & Tianyang Zhang, 2024. "Predictability of commodity futures returns with machine learning models," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(2), pages 302-322, February.
    6. Massimo Guidolin & Manuela Pedio, 2020. "Distilling Large Information Sets to Forecast Commodity Returns: Automatic Variable Selection or HiddenMarkov Models?," BAFFI CAREFIN Working Papers 20140, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    7. Massimo Guidolin & Manuela Pedio, 2018. "Forecasting Commodity Futures Returns: An Economic Value Analysis of Macroeconomic vs. Specific Factors," BAFFI CAREFIN Working Papers 1886, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    8. P. Zhukov E. & П. Жуков Е., 2019. "Новые модели анализа изменений стоимости компании, основанные на стохастических ставках дисконтирования // New Models for Analyzing Changes in Company Value Based on Stochastic Discount Rates," Финансы: теория и практика/Finance: Theory and Practice // Finance: Theory and Practice, ФГОБУВО Финансовый университет при Правительстве Российской Федерации // Financial University under The Government of Russian Federation, vol. 23(3), pages 35-48.
    9. Schücking, Maximilian & Jochem, Patrick, 2020. "Two-stage stochastic program optimizing the total cost of ownership of electric vehicles in commercial fleets," Working Paper Series in Production and Energy 50, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    10. Schücking, Maximilian & Jochem, Patrick, 2021. "Two-stage stochastic program optimizing the cost of electric vehicles in commercial fleets," Applied Energy, Elsevier, vol. 293(C).
    11. Liu, Chunbo & Zhang, Xuan & Zhou, Zhiping, 2023. "Are commodity futures a hedge against inflation? A Markov-switching approach," International Review of Financial Analysis, Elsevier, vol. 86(C).

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