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Regime Changes and Financial Markets

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  • Andrew Ang
  • Allan Timmermann

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 National Bureau of Economic Research, Inc in its series NBER Working Papers with number 17182.

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Date of creation: Jun 2011
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Publication status: published as Andrew Ang & Allan Timmermann, 2012. "Regime Changes and Financial Markets," Annual Review of Financial Economics, Annual Reviews, vol. 4(1), pages 313-337.
Handle: RePEc:nbr:nberwo:17182

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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. 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.
  3. Nyberg, Henri, 2013. "Predicting bear and bull stock markets with dynamic binary time series models," Journal of Banking & Finance, Elsevier, Elsevier, vol. 37(9), pages 3351-3363.
  4. Acharya, Viral V. & Amihud, Yakov & Bharath, Sreedhar T., 2013. "Liquidity risk of corporate bond returns: conditional approach," Journal of Financial Economics, Elsevier, Elsevier, vol. 110(2), pages 358-386.
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  6. 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, Elsevier, vol. 26(C), pages 436-456.
  7. Amélie Charles & Olivier Darné & Zakaria Moussa, 2014. "The sensitivity of Fama-French factors to economic uncertainty," Working Papers hal-01015702, HAL.
  8. Zhou, Yinggang, 2014. "Modeling the joint dynamics of risk-neutral stock index and bond yield volatilities," Journal of Banking & Finance, Elsevier, Elsevier, vol. 38(C), pages 216-228.
  9. Chavez-Demoulin, V. & Embrechts, P. & Sardy, S., 2014. "Extreme-quantile tracking for financial time series," Journal of Econometrics, Elsevier, Elsevier, vol. 181(1), pages 44-52.
  10. 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, Elsevier, vol. 26(C), pages 145-155.
  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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