Regulatory evaluation of value-at-risk models

Abstract

Beginning in 1998, US commercial banks may determine their regulatory capital requirements for financial market risk exposure using value-at-risk (VaR) models. Currently, regulators have available three hypothesis-testing methods for evaluating the accuracy of VaR models: the binomial, interval forecast, and distribution forecast methods. Given the low power often exhibited by their corresponding hypothesis tests, these methods can often misclassify forecasts from inaccurate models as acceptably accurate. An alternative evaluation method using loss functions based on probability forecasts is proposed. Simulation results indicate that this method is only as capable of differentiating between forecasts from accurate and inaccurate models as their own methods. However, its ability to directly incorporate regulatory loss functions into model evaluations make it a useful complement to the current regulatory evaluation of VaR models.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 158 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.