Value-At-Risk Forecasting of the CARBS Indices
The purpose of this paper is to use calibrated univariate GARCH family models to forecast volatility and value at risk (VaR) of the CARBS indices and a global minimum variance portfolio (GMVP) constructed using the CARBS equity indices. The reliability of the different volatility forecasts is tested using the mean absolute error (MAE) and the mean squared error (MSE). The rolling forecast of VaR is tested using a back-testing procedure. The results indicate that the use of a rolling forecast from a GARCH model when estimating VaR for the CARBS indices and the GMVP is not a reliable method.
- Brooks, C. (2014). Introductory econometrics for finance. Cambridge: Cambridge University Press.Google Scholar
- Embrechts, P., Frey, R., & McNeil, A. (2005). Quantitative risk management (Princeton Series in Finance, Vol. 10). Princeton: Princeton University Press.Google Scholar
- Ghalanos, A. (2014). Rugarch: Univariate GARCH models, R package version 1.3-3.Google Scholar
- Hull, J. (2009). Options, futures and other derivatives (7th ed.). Boston: Pearson Education.Google Scholar
- Hull, J. (2012). Risk management and financial institutions (3rd ed.). New York: Wiley.Google Scholar
- Narsoo, J. (2016). Evaluation of GARCH-Type models in volatility and value-at-risk forecasting: evidences from USD/MUR exchange rates. University of Mauritius Research Journal, 22, 1–7.Google Scholar