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Forecasting Conditional Correlation for Exchange Rates using Multivariate GARCH models with Historical Value-at-Risk application
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Economics.
2013 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

The generalization from the univariate volatility model into a multivariate approach opens up a variety of modeling possibilities. This study aims to examine the performance of the two multivariate GARCH models BEKK and DCC, applied on ten years exchange rates data. Estimations and forecasts of the covariance matrix are made for the EUR/SEK and USD/SEK, whereby the forecasts are used in a practical application: 1-day and 10-day ahead historical simulated Value-at-Risk predictions for two theoretical portfolios, one equally weighted and one hedged, consisting of the two exchange rates. An univariate GARCH(1,1) approach is included in the Vale-at-Risk predictions to visualize the diversification effect in the portfolio. The conditional correlation forecasts are evaluated using three measures, OLS-regression, MAE and RMSE, based on an one year evaluation period of intraday data. The Value-at-Risk estimates are evaluated with the backtesting method introduced by Kupiec (1995). The results indicate that the BEKK model performs relatively better than the DCC model, and both these models perform better than the univariate GARCH(1,1) model.

Place, publisher, year, edition, pages
Keyword [en]
multivariate GARCH, exchange rates, conditional correlation, forecasting, Value-at-risk
National Category
URN: urn:nbn:se:uu:diva-203587OAI: oai:DiVA.org:uu-203587DiVA: diva2:636977
Available from: 2013-07-15 Created: 2013-07-15 Last updated: 2013-07-15Bibliographically approved

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