Adaptive Window Selection for Financial Risk Forecasting

2026-03-01

AI Summary

This study addresses the challenge of selecting an appropriate lookback window for risk modeling in financial time series, where unknown structural breaks complicate the estimation of coherent risk measures. The authors propose a Bootstrap-based Adaptive Window Selection (BAWS) method that dynamically optimizes the historical window length used for joint Value-at-Risk (VaR) and Expected Shortfall (ES) estimation. By integrating bootstrap resampling, the theory of elicitable risk measures, and sequential window evaluation within an online learning framework equipped with a data-driven dynamic threshold mechanism, BAWS adapts to evolving market conditions. Empirical and simulation results demonstrate that the proposed approach significantly outperforms conventional rolling-window methods and existing stability-adaptive techniques, particularly exhibiting greater robustness during periods of structural change, thereby offering a novel data-driven solution for dynamic risk management.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• financial risk forecasting
• structural changes
• look-back window
• adaptive window selection
• risk modeling

Innovation

• adaptive window selection
• bootstrap
• risk forecasting
• structural change
• elicitable risk measures