Using Simulated Currency Rainbow Options to Evaluate Covariance Matrix Forecasts
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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|Date of creation:||28 Sep 2000|
|Date of revision:|
|Publication status:||Published in Journal of International Financial Markets, Institutions and Money, 2002, pages 216-230.|
|Contact details of provider:|| Postal: Department of Economics, School of Economics and Management, Lund University, Box 7082, S-220 07 Lund,Sweden|
Phone: +46 +46 222 0000
Fax: +46 +46 2224613
Web page: http://www.nek.lu.se/en
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- Tim Bollerslev, 1986.
"Generalized autoregressive conditional heteroskedasticity,"
EERI Research Paper Series
EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
- Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
- 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.
- Margrabe, William, 1978. "The Value of an Option to Exchange One Asset for Another," Journal of Finance, American Finance Association, vol. 33(1), pages 177-86, March.
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