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Forecasting exchange rate volatility using autoregressive random variance model

Author

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  • Mike So
  • K. Lam
  • W. K. Li

Abstract

Recently, as an alternative to the GARCH model, the autoregressive random variance (ARV) model has been gaining popularity in the modelling of changing volatility, mainly because of the capability in capturing the stochastic nature of volatility. This article highlights the ARV model as an alternative to the GARCH model in modelling volatility. The main focus is to compare the two models in forecasting exchange rate volatility. Although the two approaches generally give close forecasting performance, the ARV method provides a notable improvement in Canadian/ Dollar and Australian/Dollar. The outstanding performance seems to be related to the 'volatility of volatility', i.e. the volatility changes from day to day.

Suggested Citation

  • Mike So & K. Lam & W. K. Li, 1999. "Forecasting exchange rate volatility using autoregressive random variance model," Applied Financial Economics, Taylor & Francis Journals, vol. 9(6), pages 583-591.
    • Handle: RePEc:taf:apfiec:v:9:y:1999:i:6:p:583-591
    DOI: 10.1080/096031099332032
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    References listed on IDEAS

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    1. Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 2002. "Bayesian Analysis of Stochastic Volatility Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 69-87, January.
    2. repec:cep:stiecm:/1993/268 is not listed on IDEAS
    3. Andrew Harvey & Esther Ruiz & Neil Shephard, 1994. "Multivariate Stochastic Variance Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 61(2), pages 247-264.
    4. Mike K.P. So & K. Lam & W.K. Li, 1997. "An Empirical Study of Volatility in Seven Southeast Asian Stock Markets Using ARV Models," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 24(2), pages 261-276, March.
    5. Mike K. P. So & W. K. Li & K. Lam, 1997. "Multivariate modelling of the autoregressive random variance process," Journal of Time Series Analysis, Wiley Blackwell, vol. 18(4), pages 429-446, July.
    6. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    7. Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 1994. "Bayesian Analysis of Stochastic Volatility Models: Comments: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(4), pages 413-417, October.
    8. Fornari, Fabio & Mele, Antonio, 1994. "A stochastic variance model for absolute returns," Economics Letters, Elsevier, vol. 46(3), pages 211-214, November.
    9. Andrew C Harvey & N.G. Shephard, 1993. "Estimation and Testing of Stochastic Variance Models," STICERD - Econometrics Paper Series 268, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    10. Mike K.P. So & K. Lam & W.K. Li, 1997. "An Empirical Study of Volatility in Seven Southeast Asian Stock Markets Using ARV Models," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 24(2), pages 261-276.
    11. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    Cited by:

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    3. E.B. Nkemnole & J.T. Wulu, 2017. "Modeling of stock indices with HMM-SV models," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(2(611), S), pages 45-60, Summer.
    4. So, Mike K.P. & Kwok, Susanna W.Y., 2006. "A multivariate long memory stochastic volatility model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 362(2), pages 450-464.
    5. Perry Sadorsky, 2005. "Stochastic volatility forecasting and risk management," Applied Financial Economics, Taylor & Francis Journals, vol. 15(2), pages 121-135.

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