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The multi-chain Markov switching model

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  • Edoardo Otranto

    (Universit� di Sassari, Italy)

Abstract

In many real phenomena the behaviour of a certain variable, subject to different regimes, depends on the state of other variables or the same variable observed in other subjects, so the knowledge of the state of the latter could be important to forecast the state of the former. In this paper a particular multivariate Markov switching model is developed to represent this case. The transition probabilities of this model are characterized by the dependence on the regime of the other variables. The estimation of the transition probabilities provides useful information for the researcher to forecast the regime of the variables analysed. Theoretical background and an application are shown. Copyright © 2005 John Wiley & Sons, Ltd.

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File URL: http://hdl.handle.net/10.1002/for.965
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Bibliographic Info

Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 24 (2005)
Issue (Month): 7 ()
Pages: 523-537

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Handle: RePEc:jof:jforec:v:24:y:2005:i:7:p:523-537

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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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Cited by:
  1. Marcel Aloy & Gilles de Truchis & Gilles Dufrénot & Benjamin Keddad, 2014. "Shift-Volatility Transmission in East Asian Equity Markets," AMSE Working Papers 1402, Aix-Marseille School of Economics, Marseille, France, revised Mar 2014.
  2. Gbaguidi S. DAVID, 2011. "Expectations Impact On The Effectiveness Of The Inflation-Real Activity Trade-Off," Theoretical and Practical Research in Economic Fields, ASERS Publishing, vol. 0(2), pages 141-182, December.
  3. 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.
  4. Gbaguidi, David Sedo, 2011. "Regime Switching in a New Keynesian Phillips Curve with Non-zero Steady-state Inflation Rate," MPRA Paper 35481, University Library of Munich, Germany.
  5. L. Bauwens & E. Otranto, 2013. "Modeling the Dependence of Conditional Correlations on Volatility," Working Paper CRENoS 201304, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
  6. Gallo, Giampiero M. & Otranto, Edoardo, 2008. "Volatility spillovers, interdependence and comovements: A Markov Switching approach," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3011-3026, February.
  7. Otranto, Edoardo & Calzolari, Giorgio & Di Iorio, Francesca, 2005. "Indirect estimation of Markov switching models with endogenous switching," MPRA Paper 22983, University Library of Munich, Germany, revised 2005.
  8. E. Otranto, 2012. "Spillover Effects in the Volatility of Financial Markets," Working Paper CRENoS 201217, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
  9. M. Bigeco & E. Grosso & E. Otranto, 2008. "Recognizing and Forecasting the Sign of Financial Local Trends using Hidden Markov Models," Working Paper CRENoS 200803, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
  10. Verny, Jérôme & Gbaguidi, David Sedo, 2014. "Performance measure of a port-valley system: Data availability and their limits in freight transport and logistics," MPRA Paper 55480, University Library of Munich, Germany.
  11. Khalifa, Ahmed A.A. & Hammoudeh, Shawkat & Otranto, Edoardo, 2014. "Patterns of volatility transmissions within regime switching across GCC and global markets," International Review of Economics & Finance, Elsevier, vol. 29(C), pages 512-524.
  12. A. Khalifa & S. Hammoudeh & E. Otranto & S. Ramchander, 2012. "Volatility Transmission across Currency, Commodity and Equity Markets under Multi-Chain Regime Switching: Implications for Hedging and Portfolio Allocation," Working Paper CRENoS 201214, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
  13. Lai, Jing-yi, 2012. "Shock-dependent conditional skewness in international aggregate stock markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(1), pages 72-83.

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