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Markov Switching Models in Empirical Finance

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  • Massimo Guidolin

Abstract

I review the burgeoning literature on applications of Markov regime switching models in empirical finance. In particular, distinct attention is devoted to the ability of Markov Switching models to fit the data, filter unknown regimes and states on the basis of the data, to allow a powerful tool to test hypothesesformulated in the light of financial theories, and to their forecasting performance with reference to both point and density predictions. The review covers papers concerning a multiplicity of sub-fields in financial economics, ranging from empirical analyses of stock returns, the term structure of default-free interest rates, the dynamics of exchange rates, as well as the joint process of stock and bond returns.

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  • Massimo Guidolin, 2011. "Markov Switching Models in Empirical Finance," Working Papers 415, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  • Handle: RePEc:igi:igierp:415
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    3. Guidolin, Massimo & Orlov, Alexei G. & Pedio, Manuela, 2017. "The impact of monetary policy on corporate bonds under regime shifts," Journal of Banking & Finance, Elsevier, vol. 80(C), pages 176-202.
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    5. Andrzej Geise & Mariola Pilatowska, 2013. "Synchronization of Crude Oil Prices Cycle and Business Cycle for the Central Eastern European Economies," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 13, pages 175-194.
    6. Xiaochun Liu, 2016. "Markov switching quantile autoregression," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 70(4), pages 356-395, November.
    7. Fernando F. Ferreira & A. Christian Silva & Ju-Yi Yen, 2014. "Information ratio analysis of momentum strategies," Papers 1402.3030, arXiv.org, revised Jul 2014.
    8. Erik Kole & Dick Dijk, 2017. "How to Identify and Forecast Bull and Bear Markets?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(1), pages 120-139, January.
    9. Billio, Monica & Casarin, Roberto & Ravazzolo, Francesco & van Dijk, Herman K., 2012. "Combination schemes for turning point predictions," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(4), pages 402-412.
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    12. 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.
    13. Marta Giampietro & Massimo Guidolin & Manuela Pedio, 2015. "Can No-Arbitrage SDF Models with Regime Shifts Explain the Correlations Between Commodity, Stock, and Bond Returns?," BAFFI CAREFIN Working Papers 1619, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    14. Haas, Markus, 2016. "A note on optimal portfolios under regime–switching," Finance Research Letters, Elsevier, vol. 19(C), pages 209-216.
    15. repec:pal:assmgt:v:17:y:2016:i:5:d:10.1057_jam.2016.12 is not listed on IDEAS
    16. Gębka, Bartosz & Serwa, Dobromił, 2015. "The elusive nature of motives to trade: Evidence from international stock markets," International Review of Financial Analysis, Elsevier, vol. 39(C), pages 147-157.
    17. Aslanidis, Nektarios & Christiansen, Charlotte & Savva, Christos S., 2016. "Risk-return trade-off for European stock markets," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 84-103.
    18. Tom Boot & Andreas Pick, 2014. "Optimal forecasts from Markov switching models," DNB Working Papers 452, Netherlands Central Bank, Research Department.
    19. repec:eee:finana:v:55:y:2018:i:c:p:93-110 is not listed on IDEAS

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