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Forecasting volatility using range data: analysis for emerging equity markets in Latin America

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  • Manabu Asai
  • Iván Brugal

Abstract

The article suggests a simple but effective approach for estimating value-at-risk thresholds using range data, working with the filtered historical simulation. For this purpose, we consider asymmetric heterogeneous Autoregressive Moving Average (ARMA) model for log-range, which captures the leverage effects and the effects from daily, weekly and monthly horizons. The empirical analysis on stock market indices on the US, Mexico, Brazil and Argentina shows that 1% and 5% Value at Risk (VaR) thresholds based on one-step-ahead forecasts of log-range are satisfactory for the period includes the global financial crisis.

Suggested Citation

  • Manabu Asai & Iván Brugal, 2012. "Forecasting volatility using range data: analysis for emerging equity markets in Latin America," Applied Financial Economics, Taylor & Francis Journals, vol. 22(6), pages 461-470, March.
    • Handle: RePEc:taf:apfiec:v:22:y:2012:i:6:p:461-470
    DOI: 10.1080/09603107.2011.617694
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    Cited by:

    1. Tomasz Skoczylas, 2013. "Modelowanie i prognozowanie zmienności przy użyciu modeli opartych o zakres wahań," Ekonomia journal, Faculty of Economic Sciences, University of Warsaw, vol. 35.
    2. Manabu Asai, 2013. "Heterogeneous Asymmetric Dynamic Conditional Correlation Model with Stock Return and Range," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(5), pages 469-480, August.

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