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Using simulated currency rainbow options to evaluate covariance matrix forecasts

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  • Bystrom, Hans N. E.

Abstract

When choosing evaluation measures for variance and covariance forecasts one has to consider what the actual purpose of these forecasts is. In this paper we extend the results of Gibson and Boyer (1998) by looking at portfolios of rainbow currency options and how simulated trading of such options portfolios can be used as a preference free evaluation measure for the forecasted covariance matrix. The advantage of using portfolios instead of single options is the possibility it gives of relying on shorter return series. We apply the methodology to a system of four U.S. dollar exchange rates and compare the relative performance of different forecasting models. In doing this, we also apply and evaluate the fairly new Orthogonal GARCH technique to exchange rates, both with the option evaluation technique and with standard statistical measures
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Suggested Citation

  • Bystrom, Hans N. E., 2002. "Using simulated currency rainbow options to evaluate covariance matrix forecasts," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 12(3), pages 216-230, July.
    • Handle: RePEc:eee:intfin:v:12:y:2002:i:3:p:216-230
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    References listed on IDEAS

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    1. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    2. Margrabe, William, 1978. "The Value of an Option to Exchange One Asset for Another," Journal of Finance, American Finance Association, vol. 33(1), pages 177-186, March.
    3. 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:

    1. Majdoub, Jihed & Mansour, Walid, 2014. "Islamic equity market integration and volatility spillover between emerging and US stock markets," The North American Journal of Economics and Finance, Elsevier, vol. 29(C), pages 452-470.
    2. Chong, James, 2005. "The forecasting abilities of implied and econometric variance-covariance models across financial measures," Journal of Economics and Business, Elsevier, vol. 57(5), pages 463-490.

    More about this item

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G19 - Financial Economics - - General Financial Markets - - - Other

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