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


  • Manabu Asai
  • Iván Brugal


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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    References listed on IDEAS

    1. Kaul, Gautam, 1987. "Stock returns and inflation : The role of the monetary sector," Journal of Financial Economics, Elsevier, vol. 18(2), pages 253-276, June.
    2. Robert Engle, 2004. "Risk and Volatility: Econometric Models and Financial Practice," American Economic Review, American Economic Association, vol. 94(3), pages 405-420, June.
    3. Rigobon, Roberto & Sack, Brian, 2004. "The impact of monetary policy on asset prices," Journal of Monetary Economics, Elsevier, vol. 51(8), pages 1553-1575, November.
    4. Schwert, G William, 1989. " Why Does Stock Market Volatility Change over Time?," Journal of Finance, American Finance Association, vol. 44(5), pages 1115-1153, December.
    5. Enrique Sentana, 1995. "Quadratic ARCH Models," Review of Economic Studies, Oxford University Press, vol. 62(4), pages 639-661.
    6. Engle, Robert F & Ng, Victor K, 1993. " Measuring and Testing the Impact of News on Volatility," Journal of Finance, American Finance Association, vol. 48(5), pages 1749-1778, December.
    7. Robert F. Engle & Jose Gonzalo Rangel, 2005. "The Spline GARCH Model for Unconditional Volatility and its Global Macroeconomic Causes," Working Papers 2005/13, Czech National Bank, Research Department.
    8. Nicole Davis & Ali Kutan, 2003. "Inflation and output as predictors of stock returns and volatility: international evidence," Applied Financial Economics, Taylor & Francis Journals, vol. 13(9), pages 693-700.
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    Cited by:

    1. 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.
    2. 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.

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