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Evaluating the Forecast Accuracy of Emerging Market Stock Returns

Author

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  • Andre Carvalhal
  • Beatriz Vaz de Melo Mendes

Abstract

This paper analyzes the forecast performance of emerging market stock returns using standard autoregressive moving average (ARMA) and more elaborated autoregressive conditional heteroskedasticity (ARCH) models. Our results indicate that the ARMA and ARCH specifications generally outperform random walk models. Models that allow for asymmetric shocks to volatility are better for in-sample estimation (threshold autoregressive conditional heteroskedasticity for daily returns and exponential generalized autoregressive conditional heteroskedasticity for longer periods), and ARMA models are better for out-of-sample forecasts. The results are valid using both U. S. dollar and domestic currencies. Overall, the forecast errors of each Latin American market can be explained by the forecasts of other Latin American markets and Asian markets. The forecast errors of each Asian market can be explained by the forecasts of other Asian markets, but not by Latin American markets. Our predictability results are economically significant and may be useful for portfolio managers to enter or leave the market.

Suggested Citation

  • Andre Carvalhal & Beatriz Vaz de Melo Mendes, 2008. "Evaluating the Forecast Accuracy of Emerging Market Stock Returns," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 44(1), pages 21-40, January.
    • Handle: RePEc:mes:emfitr:v:44:y:2008:i:1:p:21-40
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    Cited by:

    1. Joanna Olbrys, 2013. "Price and Volatility Spillovers in the Case of Stock Markets Located in Different Time Zones," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 49(S2), pages 145-157, March.
    2. Li, Matthew C., 2016. "US term structure and international stock market volatility: The role of the expectations factor and the maturity premium," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 41(C), pages 1-15.

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