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Regime Changes and Financial Markets

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  • Ang, Andrew
  • Timmermann, Allan G

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. While 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 regime switching means, volatilities, autocorrelations, and cross-covariances of asset returns often differ across regimes, which allow 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, like consumption or dividend growth, strongly affect the dynamic properties of equilibrium asset prices and can induce non-linear risk-return trade-offs. Regime switches also lead to potentially large consequences for investors' optimal portfolio choice.

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Paper provided by C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number 8480.

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Date of creation: Jul 2011
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Handle: RePEc:cpr:ceprdp:8480

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Keywords: jumps; mixture distributions; non-linear equilibrium asset pricing models; rare events; regime switching;

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Cited by:
  1. Massimo Guidolin & Stuart Hyde, 2011. "Can VAR Models Capture Regime Shifts in Asset Returns? A Long-Horizon Strategic Asset Allocation Perspective," Working Papers 414, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  2. 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.
  3. 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.
  4. repec:hal:journl:halshs-00658540 is not listed on IDEAS
  5. Yang, Lu & Hamori, Shigeyuki, 2014. "Spillover effect of US monetary policy to ASEAN stock markets: Evidence from Indonesia, Singapore, and Thailand," Pacific-Basin Finance Journal, Elsevier, vol. 26(C), pages 145-155.
  6. repec:hal:cesptp:halshs-00658540 is not listed on IDEAS
  7. Zhou, Yinggang, 2014. "Modeling the joint dynamics of risk-neutral stock index and bond yield volatilities," Journal of Banking & Finance, Elsevier, vol. 38(C), pages 216-228.
  8. 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.
  9. Amélie Charles & Olivier Darné & Zakaria Moussa, 2014. "The sensitivity of Fama-French factors to economic uncertainty," Working Papers hal-01015702, HAL.
  10. 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.
  11. Massimo Guidolin, 2011. "Markov Switching Models in Empirical Finance," Working Papers 415, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

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