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Structural instability and predictability

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  • Devpura, Neluka
  • Narayan, Paresh Kumar
  • Sharma, Susan Sunila

Abstract

We propose a structural break predictive regression model that accounts for predictor persistency, endogeneity, heteroscedasticity, and a structural break. Monte Carlo (MC) simulations indicate that this test performs satisfactorily compared to competitor estimators. We employ a popular U.S. data set (the period January 1927 to December 2016) that includes stock market returns and multiple predictors. We show, consistent with the MC results, evidence of a structural break. Our analysis reveals that a structural break–based predictive regression model fits the data reasonably well in predicting stock price returns.

Suggested Citation

  • Devpura, Neluka & Narayan, Paresh Kumar & Sharma, Susan Sunila, 2019. "Structural instability and predictability," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 63(C).
    • Handle: RePEc:eee:intfin:v:63:y:2019:i:c:s1042443119300150
    DOI: 10.1016/j.intfin.2019.101145
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