Achieving effective risk governance by a comprehensive filtered historical simulation tool

Growing regulatory demands require financial institutions to improve risk governance beyond traditional market and credit risk measures. This paper proposes an enhanced filtered historical simulation (FHS) framework to support comprehensive risk management and capital assessment. The methodology combines a global risk factor inventory with generalized autoregressive conditional heteroskedasticity (GARCH)-type volatility filtering, bootstrap resampling, and scenario rescaling to generate coherent profit-and-loss distributions for value at risk (VaR) and expected shortfall (ES). Extensive backtesting across multiple asset classes and market regimes shows that the proposed long-term FHS approach improves tail-risk capture, maintains dependency structures, and achieves regulatory compliance. Finally, it avoids excessive capital conservatism.