An improved model accuracy for forecasting risk measures: application of ensemble methods

Abstract

Statistical-based predictions with extreme value theory improve the performance of the risk model not by choosing the model structure that is expected to predict the best but by developing a model whose results are a combination of models with different shapes. Using different ensemble algorithms to conglomerate the TBATS and the GEV distribution, we found that the stacking ensemble algorithm outperforms other ensembles hence the forecasting accuracy of risk measures is improved with the stacking ensemble algorithm. The risk estimates suggest that the returns on losses averaging 0.014 and 0.018 invested at 90 and 99 percent respectively, are riskier than the returns on gains. Backtesting results further revealed that all the risk measures are reliable, and the combined model is a good one for computing financial risk particularly in South Africa. At high confidence levels, all the risk measures seem to perform better than at lower confidence levels, as evidenced by higher probability values from backtesting using the Kupiec and Christoffersen test at 95 percent than at 99 percent levels of significance.

Keywords

• combined forecasting
• financial markets
• extreme value theory
• machine learning
• C13
• C22