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Markov switching GARCH models of currency turmoil in southeast Asia

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This paper analyzes exchange rate turmoil with a Markov Switching GARCH model. We distinguish between two different regimes in both the conditional mean and the conditional variance: \"ordinary\" regime, characterized by low exchange rate changes and low volatility, and \"turbulent\" regime, characterized by high exchange rate movements and high volatility. We also allow the transition probabilities to vary over time as functions of economic and financial indicators. We find that real effective exchange rates, money supply relative to reserves, stock index returns, and bank stock index returns and volatility contain valuable information for identifying turbulence and ordinary periods.

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  • Celso Brunetti & Roberto S. Mariano & Chiara Scotti & Augustine H. H. Tan, 2007. "Markov switching GARCH models of currency turmoil in southeast Asia," International Finance Discussion Papers 889, Board of Governors of the Federal Reserve System (U.S.).
  • Handle: RePEc:fip:fedgif:889
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    Cited by:

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    2. Walid, Chkili & Chaker, Aloui & Masood, Omar & Fry, John, 2011. "Stock market volatility and exchange rates in emerging countries: A Markov-state switching approach," Emerging Markets Review, Elsevier, vol. 12(3), pages 272-292, September.
    3. repec:ipg:wpaper:2013-032 is not listed on IDEAS
    4. Houda Rharrabti Zaid, 2015. "Transmission du stress financier de la zone euro aux Pays de l’Europe Centrale et Orientale," EconomiX Working Papers 2015-37, University of Paris Nanterre, EconomiX.
    5. Demiris, Nikolaos & Kypraios, Theodore & Smith, L. Vanessa, 2012. "On the epidemic of financial crises," MPRA Paper 46693, University Library of Munich, Germany.
    6. repec:ipg:wpaper:32 is not listed on IDEAS
    7. Aloui, Chaker & Hammoudeh, Shawkat & Hamida, Hela Ben, 2015. "Price discovery and regime shift behavior in the relationship between sharia stocks and sukuk: A two-state Markov switching analysis," Pacific-Basin Finance Journal, Elsevier, vol. 34(C), pages 121-135.
    8. Thomas Chuffart, 2015. "Selection Criteria in Regime Switching Conditional Volatility Models," Econometrics, MDPI, Open Access Journal, vol. 3(2), pages 1-28, May.
    9. Cicih Ratnasih, 2018. "Institutional Bureaucracy and Real Sector Movement," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 31-39.
    10. Khallouli, Wajih & Sandretto, René, 2012. "Testing for “Contagion” of the Subprime Crisis on the Middle East and North African Stock Markets: A Markov Switching EGARCH Approach," Journal of Economic Integration, Center for Economic Integration, Sejong University, vol. 27, pages 134-166.
    11. Flavin, Thomas J. & Sheenan, Lisa, 2015. "The role of U.S. subprime mortgage-backed assets in propagating the crisis: Contagion or interdependence?," The North American Journal of Economics and Finance, Elsevier, vol. 34(C), pages 167-186.
    12. Duprey, Thibaut & Klaus, Benjamin, 2017. "How to predict financial stress? An assessment of Markov switching models," Working Paper Series 2057, European Central Bank.
    13. Michael Frömmel, 2010. "Volatility Regimes in Central and Eastern European Countries’ Exchange Rates," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 60(1), pages 2-21, February.
    14. Chkili, Walid, 2017. "Is gold a hedge or safe haven for Islamic stock market movements? A Markov switching approach," Journal of Multinational Financial Management, Elsevier, vol. 42, pages 152-163.
    15. Martín Sola & Zacharias Psaradakis, 2017. "Markov-Switching Models with State-Dependent Time-Varying Transition Probabilities," Department of Economics Working Papers 2017_01, Universidad Torcuato Di Tella.
    16. Xiaoping Zhan & Tiefeng Ma & Shuangzhe Liu & Kunio Shimizu, 2018. "Markov-Switching Linked Autoregressive Model for Non-continuous Wind Direction Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(3), pages 410-425, September.
    17. T. G. Saji, 2019. "Can BRICS Form a Currency Union? An Analysis under Markov Regime-Switching Framework," Global Business Review, International Management Institute, vol. 20(1), pages 151-165, February.
    18. Giampiero M. Gallo & Edoardo Otranto, 2007. "Volatility transmission across markets: a Multichain Markov Switching model," Applied Financial Economics, Taylor & Francis Journals, vol. 17(8), pages 659-670.
    19. repec:udt:wpecon:2017_1 is not listed on IDEAS
    20. Diteboho Xaba & Ntebogang Dinah Moroke & Ishmael Rapoo, 2019. "Modeling Stock Market Returns of BRICS with a Markov-Switching Dynamic Regression Model," Journal of Economics and Behavioral Studies, AMH International, vol. 11(3), pages 10-22.
    21. Khaled Guesmi & Frédéric Teulon & Zied Ftiti, 2013. "Sudden Changes in Volatility in European Stock Markets," Working Papers 2013-32, Department of Research, Ipag Business School.
    22. Kim Liow & Zhiwei Chen & Jingran Liu, 2011. "Multiple Regimes and Volatility Transmission in Securitized Real Estate Markets," The Journal of Real Estate Finance and Economics, Springer, vol. 42(3), pages 295-328, April.
    23. Martín Solá & Zacharias Psaradakis & Fabio Spagnolo & Nicola Spagnolo, 2010. "Some Cautionary Results Concerning Markov-Switching Models with Time-Varying Transition Probabilities," Department of Economics Working Papers 2010-12, Universidad Torcuato Di Tella.
    24. Alberto Humala & Gabriel Rodriguez, 2010. "Foreign exchange intervention and exchange rate volatility in Peru," Applied Economics Letters, Taylor & Francis Journals, vol. 17(15), pages 1485-1491.
    25. Ivana Marjanović & Milan Marković, 2019. "Determinants of currency crises in the Republic of Serbia," Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, University of Rijeka, Faculty of Economics, vol. 37(1), pages 191-212.

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