Volatility Forecasts and Value at Risk Evaluation for the MSCI North America Index
This paper compares different models for volatility forecasts with respect to the value at risk performance (VaR). The VaR measures the potential loss of a portfolio for the next period at a given significance level. We focus on the question if the choice of the appropriate volatility forecasting model is important for the VaR estimation. We compare the forecasting performance of several volatility models for the returns of the MSCI North America index. The resulting VaR estimators are evaluated by comparing the empirical failure rate with the forecasting performance.
KeywordsForecast Performance GARCH Model Volatility Forecast Naive Model Auxiliary Regression
Unable to display preview. Download preview PDF.
- DANIELSON, J. and VRIES, C. (1997): Value-at-Risk and Extreme Returns. In: No 98-017/2 in Tinbergen Institute Discussion Papers from Tinbergen Institute http://www.tinbergen.nl/discussionpapers/98017.pdf.Google Scholar
- DOCKNER, E. and SCHEICHER, M. (1999): Evaluating Volatility Forecasts and Empirical Distributions in Value at Risk Models. Finanzmarkt und Portfolio Management, 1, 39–55.Google Scholar
- MATHSOFT (1996): S-Plus, S+Garch User’s Manual: Data Analysis Products Division, MathSoft, Seattle.Google Scholar
- POJARLIEV, M. and POLASEK, W. (2000): Value at Risk estimation for stock indices using the Basle Committee proposal from 1995: University of Basel, http://www.ihs.ac.at/polasek.Google Scholar
- RiskMetrics (1996): Risk Metrics Technical Document, fourth edition http://www.riskmetrics.com/research/.Google Scholar
- ZUCCHINI, W. and NEUMANN, K. (2001): A Comparison of Several Time Series Models for Assessing the Value at Risk of Shares. Applied Stochastic Models for Business and Industry 2000, 135–148.Google Scholar