Systematic Bagging Model for market risk – cross pattern learning framework for Value at Risk and Expected Shortfall estimation

Abstract

This research proposes a new framework, the Systematic Bagging Model (SBM), for estimating market risk through leveraging ensembling and cross-learning approaches. The SBM is designed to make possible the generation of stable and accurate forecasts for Value at Risk and Expected Shortfall by using deterministic learning sets from time series data with optimal pattern searching. Empirical validation with the LIGHT Benchmark shows the superiority of an SBM over state-of-the-art standalone models concerning forecasting quality in terms of adequacy, regulatory compliance, and capital effectiveness. This research unveils a strong potential for systematic bagging and cross-learning for improving market risk models, especially during periods of sudden increased volatility.