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Level shifts in stock returns driven by large shocks

Author

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  • Dendramis, Yiannis
  • Kapetanios, George
  • Tzavalis, Elias

Abstract

This paper employs a parametric model of persistent (level) shifts in the conditional mean of stock market returns which are endogenously driven by large positive or negative return shocks. These shocks can be taken to reflect important market announcements, monetary policy regime changes and/or changes in business conditions affecting stock market. The model assumes that both the timing and size of breaks are stochastic. The last property of the model distinguishes it from other nonlinear models of the literature employed to capture level shifts in stock returns. Implementation of the model to the US stock market indicates that it can successfully capture level shifts in the mean of the aggregate return of this market which follow a cyclical pattern. Most of these shifts are triggered by negative large return shocks. The latter can be of smaller magnitude than that of the positive ones. Finally, the paper shows that the model can be employed to successfully forecast future expected stock returns.

Suggested Citation

  • Dendramis, Yiannis & Kapetanios, George & Tzavalis, Elias, 2014. "Level shifts in stock returns driven by large shocks," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 41-51.
  • Handle: RePEc:eee:empfin:v:29:y:2014:i:c:p:41-51
    DOI: 10.1016/j.jempfin.2014.04.001
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    2. Mihailo Jovanović & Vladica Stojanović & Kristijan Kuk & Brankica Popović & Petar Čisar, 2022. "Asymptotic Properties and Application of GSB Process: A Case Study of the COVID-19 Dynamics in Serbia," Mathematics, MDPI, vol. 10(20), pages 1-28, October.
    3. Dendramis, Yiannis & Kapetanios, George & Tzavalis, Elias, 2015. "Shifts in volatility driven by large stock market shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 55(C), pages 130-147.
    4. Costantini, Mauro & Kunst, Robert M., 2021. "On using predictive-ability tests in the selection of time-series prediction models: A Monte Carlo evaluation," International Journal of Forecasting, Elsevier, vol. 37(2), pages 445-460.
    5. Martin, Vance L. & Tang, Chrismin & Yao, Wenying, 2018. "News and expected returns in East Asian equity markets: The RV-GARCHM model," Journal of Asian Economics, Elsevier, vol. 57(C), pages 36-52.
    6. Chourdakis, Kyriakos & Dendramis, Yiannis & Tzavalis, Elias, 2014. "Are regime-shift sources of risk priced in the market?," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 151-170.
    7. Dendramis, Y. & Tzavalis, E. & Adraktas, G., 2018. "Credit risk modelling under recessionary and financially distressed conditions," Journal of Banking & Finance, Elsevier, vol. 91(C), pages 160-175.

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    More about this item

    Keywords

    Expected returns; Level shifts; Large shocks; State space model; Regime-shifts; Threshold models;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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