Adaptive Window Selection for Financial Risk Forecasting

Yinhuan Li, Chenxin Lyu and Ruodu Wang

Papers from arXiv.org

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.

Date: 2026-03
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://arxiv.org/pdf/2603.01157 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2603.01157

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().