Adaptive Window Selection for Financial Risk Forecasting

TL;DR

This paper introduces BAWS, a bootstrap-based adaptive window selection method for financial risk forecasting that dynamically adjusts look-back periods to better handle structural changes in data.

Contribution

The paper presents a novel online learning approach, BAWS, for adaptively selecting window sizes in risk forecasting, improving over existing methods especially during structural changes.

Findings

BAWS outperforms standard rolling window methods.

BAWS performs better than stability-based adaptive window selection.

Effective in scenarios with structural changes.

Abstract

Risk forecasts in financial regulation and internal management are calculated through historical data. The unknown structural changes of financial data poses a substantial challenge in selecting an appropriate look-back window for risk modeling and forecasting. We develop a data-driven online learning method, called the bootstrap-based adaptive window selection (BAWS), that adaptively determines the window size in a sequential manner. A central component of BAWS is to compare the realized scores against a data-dependent threshold, which is evaluate based on an idea of bootstrap. The proposed method is applicable to the forecast of risk measures that are elicitable individually or jointly, such as the Value-at-Risk (VaR) and the pair of the VaR and the corresponding Expected Shortfall. Through simulation studies and empirical analyses, we demonstrate that BAWS generally outperforms the standard rolling window approach and the recently developed method of stability-based adaptive window selection, especially when there are structural changes in the data-generating process.