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Regime Changes and Financial Markets

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  • Andrew Ang

    ()
    (Finance and Economics Department, Business School, Columbia University, New York, NY 10027
    National Bureau of Economic Research, Cambridge, Massachusetts 02138)

  • Allan Timmermann

    ()
    (Rady School of Management and Department of Economics, University of California, San Diego, La Jolla, California 92093)

Abstract

Regime-switching models can match the tendency of financial markets to often change their behavior abruptly and the phenomenon that the new behavior of financial variables often persists for several periods after such a change. Although the regimes captured by regime-switching models are identified by an econometric procedure, they often correspond to different periods in regulation, policy, and other secular changes. In empirical estimates, the means, volatilities, autocorrelations, and cross-covariances of asset returns often differ across regimes in a manner that allows regime-switching models to capture the stylized behavior of many financial series including fat tails, heteroskedasticity, skewness, and time-varying correlations. In equilibrium models, regimes in fundamental processes, such as consumption or dividend growth, strongly affect the dynamic properties of equilibrium asset prices and can induce nonlinear risk-return trade-offs. Regime switches also lead to potentially large consequences for investors' optimal portfolio choice.

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Bibliographic Info

Article provided by Annual Reviews in its journal Annual Review of Financial Economics.

Volume (Year): 4 (2012)
Issue (Month): 1 ()
Pages: 313-337

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Handle: RePEc:anr:refeco:v:4:y:2012:p:313-337

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Keywords: regime switching; nonlinear equilibrium asset pricing models; mixture distributions; rare events; jumps;

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References

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Cited by:
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  3. Nyberg, Henri, 2013. "Predicting bear and bull stock markets with dynamic binary time series models," Journal of Banking & Finance, Elsevier, vol. 37(9), pages 3351-3363.
  4. Massimo Guidolin & Stuart Hyde, 2010. "Can VAR models capture regime shifts in asset returns? a long-horizon strategic asset allocation perspective," Working Papers 2010-002, Federal Reserve Bank of St. Louis.
  5. Chavez-Demoulin, V. & Embrechts, P. & Sardy, S., 2014. "Extreme-quantile tracking for financial time series," Journal of Econometrics, Elsevier, vol. 181(1), pages 44-52.
  6. Acharya, Viral V. & Amihud, Yakov & Bharath, Sreedhar T., 2013. "Liquidity risk of corporate bond returns: conditional approach," Journal of Financial Economics, Elsevier, vol. 110(2), pages 358-386.
  7. Amélie Charles & Olivier Darné & Zakaria Moussa, 2014. "The sensitivity of Fama-French factors to economic uncertainty," Working Papers hal-01015702, HAL.
  8. Massimo Guidolin, 2011. "Markov Switching Models in Empirical Finance," Working Papers 415, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  9. Ho, Kin-Yip & Shi, Yanlin & Zhang, Zhaoyong, 2013. "How does news sentiment impact asset volatility? Evidence from long memory and regime-switching approaches," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 436-456.
  10. repec:hal:journl:halshs-00658540 is not listed on IDEAS
  11. Mathieu Gatumel & Florian Ielpo, 2011. "The Number of Regimes Across Asset Returns: Identification and Economic Value," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00658540, HAL.

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