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

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  • Giampietro, Marta
  • Guidolin, Massimo
  • Pedio, Manuela

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.

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  • Giampietro, Marta & Guidolin, Massimo & Pedio, Manuela, 2018. "Estimating stochastic discount factor models with hidden regimes: Applications to commodity pricing," European Journal of Operational Research, Elsevier, vol. 265(2), pages 685-702.
    • Handle: RePEc:eee:ejores:v:265:y:2018:i:2:p:685-702
    DOI: 10.1016/j.ejor.2017.07.045
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    as
    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. Hansen, Lars Peter & Jagannathan, Ravi, 1997. "Assessing Specification Errors in Stochastic Discount Factor Models," Journal of Finance, American Finance Association, vol. 52(2), pages 557-590, June.
    3. Jagannathan, Ravi & Wang, Zhenyu, 1996. "The Conditional CAPM and the Cross-Section of Expected Returns," Journal of Finance, American Finance Association, vol. 51(1), pages 3-53, March.
    4. Büyükşahin, Bahattin & Robe, Michel A., 2014. "Speculators, commodities and cross-market linkages," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 38-70.
    5. Lombardi, Marco J. & Ravazzolo, Francesco, 2016. "On the correlation between commodity and equity returns: Implications for portfolio allocation," Journal of Commodity Markets, Elsevier, vol. 2(1), pages 45-57.
    6. Scott R. Baker & Nick Bloom & Steven J. Davis & John Van Reenen, 2012. "Economic Recovery and Policy Uncertainty," CEP US Election Analysis Papers 002, Centre for Economic Performance, LSE.
    7. Frans A. De Roon & Theo E. Nijman & Chris Veld, 2000. "Hedging Pressure Effects in Futures Markets," Journal of Finance, American Finance Association, vol. 55(3), pages 1437-1456, June.
    8. Xiaohong Chen & Sydney C. Ludvigson, 2009. "Land of addicts? an empirical investigation of habit‐based asset pricing models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(7), pages 1057-1093, November.
    9. Yang, Fan, 2013. "Investment shocks and the commodity basis spread," Journal of Financial Economics, Elsevier, vol. 110(1), pages 164-184.
    10. Massimo Guidolin & Allan Timmermann, 2006. "An econometric model of nonlinear dynamics in the joint distribution of stock and bond returns," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 1-22, January.
    11. Pastor, Lubos & Stambaugh, Robert F., 2003. "Liquidity Risk and Expected Stock Returns," Journal of Political Economy, University of Chicago Press, vol. 111(3), pages 642-685, June.
    12. Buckley, Ian & Saunders, David & Seco, Luis, 2008. "Portfolio optimization when asset returns have the Gaussian mixture distribution," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1434-1461, March.
    13. Massimo Guidolin, 2011. "Markov Switching Models in Empirical Finance," Working Papers 415, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    14. Daskalaki, Charoula & Skiadopoulos, George, 2011. "Should investors include commodities in their portfolios after all? New evidence," Journal of Banking & Finance, Elsevier, vol. 35(10), pages 2606-2626, October.
    15. Bae, Geum Il & Kim, Woo Chang & Mulvey, John M., 2014. "Dynamic asset allocation for varied financial markets under regime switching framework," European Journal of Operational Research, Elsevier, vol. 234(2), pages 450-458.
    16. Daskalaki, Charoula & Kostakis, Alexandros & Skiadopoulos, George, 2014. "Are there common factors in individual commodity futures returns?," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 346-363.
    17. Sydney C. Ludvigson & Serena Ng, 2009. "Macro Factors in Bond Risk Premia," The Review of Financial Studies, Society for Financial Studies, vol. 22(12), pages 5027-5067, December.
    18. Shiller, Robert J, 1979. "The Volatility of Long-Term Interest Rates and Expectations Models of the Term Structure," Journal of Political Economy, University of Chicago Press, vol. 87(6), pages 1190-1219, December.
    19. Massimo Guidolin, 2011. "Markov Switching Models in Empirical Finance," Advances in Econometrics, in: Missing Data Methods: Time-Series Methods and Applications, pages 1-86, Emerald Group Publishing Limited.
    20. Suleyman Basak & Anna Pavlova, 2013. "Asset Prices and Institutional Investors," American Economic Review, American Economic Association, vol. 103(5), pages 1728-1758, August.
    21. Alizadeh, Amir H. & Nomikos, Nikos K. & Pouliasis, Panos K., 2008. "A Markov regime switching approach for hedging energy commodities," Journal of Banking & Finance, Elsevier, vol. 32(9), pages 1970-1983, September.
    22. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2012. "Has Economic Policy Uncertainty Hampered the Recovery?," Book Chapters, in: Lee E. Ohanian & John B. Taylor & Ian J. Wright (ed.), Government Policies and the Delayed Economic Recovery, chapter 3, Hoover Institution, Stanford University.
    23. Clifford S. Asness & Tobias J. Moskowitz & Lasse Heje Pedersen, 2013. "Value and Momentum Everywhere," Journal of Finance, American Finance Association, vol. 68(3), pages 929-985, June.
    24. Hsiang-Tai Lee & Jonathan Yoder, 2007. "A bivariate Markov regime switching GARCH approach to estimate time varying minimum variance hedge ratios," Applied Economics, Taylor & Francis Journals, vol. 39(10), pages 1253-1265.
    25. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2012. "Has Economic Policy Uncertainty Hampered the Recovery?," Book Chapters, in: Lee E. Ohanian & John B. Taylor & Ian J. Wright (ed.), Government Policies and the Delayed Economic Recovery, chapter 3, Hoover Institution, Stanford University.
    26. Marta Szymanowska & Frans Roon & Theo Nijman & Rob Goorbergh, 2014. "An Anatomy of Commodity Futures Risk Premia," Journal of Finance, American Finance Association, vol. 69(1), pages 453-482, February.
    27. Dias, José G. & Vermunt, Jeroen K. & Ramos, Sofia, 2015. "Clustering financial time series: New insights from an extended hidden Markov model," European Journal of Operational Research, Elsevier, vol. 243(3), pages 852-864.
    28. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
    29. Marroquı´n-Martı´nez, Naroa & Moreno, Manuel, 2013. "Optimizing bounds on security prices in incomplete markets. Does stochastic volatility specification matter?," European Journal of Operational Research, Elsevier, vol. 225(3), pages 429-442.
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    5. 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.
    6. 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.
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    9. 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.
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