用贝叶斯方法精准量化金融风险不确定性，提升预测与合规效果。
Bayesian Modeling for Uncertainty Management in Financial Risk Forecasting and Compliance
- 构建贝叶斯框架，融合DLM、GARCH和逻辑回归模型处理风险建模。
- 1天前瞻95%VaR在2020–2024年测试中，新模型表现优于LSTM和GARCH(1,1)。
- 模型可解释性强，支持实时分析，适合金融风控与监管机构使用。
一种贝叶斯分析框架通过精确量化不确定性，显著提升金融风险管理能力。我们提出集成方法，统一改进市场波动率预测、欺诈检测与合规监控。概率性且可解释的模型表现可靠：基于2000–2019年训练、2020–2024年测试的标普500日收益率数据，评估一天前瞻95%价值风险（VaR）预测。无条件（Kupiec）与条件（Christoffersen）覆盖检验显示，LSTM基线接近名义校准；而带学生分布创新项的GARCH(1,1)模型低估尾部风险。所提折扣因子动态线性模型（DLM）产生略宽松的VaR估计，存在违规聚集现象。贝叶斯逻辑回归提升欺诈检测的召回率与AUC-ROC，分层贝塔状态空间模型实现透明且自适应的合规风险评估。该流程具备精确不确定性量化、可解释性及GPU加速，性能最高提升50倍。当前挑战包括欺诈数据稀疏与代理合规标签问题，但框架能提供可操作的风险洞察。未来将扩展特征集，探索切换机制先验，优化可扩展推断。
原文摘要 · Abstract (English)
A Bayesian analytics framework that precisely quantifies uncertainty offers a significant advance for financial risk management. We develop an integrated approach that consistently enhances the handling of risk in market volatility forecasting, fraud detection, and compliance monitoring. Our probabilistic, interpretable models deliver reliable results: We evaluate the performance of one-day-ahead 95% Value-at-Risk (VaR) forecasts on daily S&P 500 returns, with a training period from 2000 to 2019 and an out-of-sample test period spanning 2020 to 2024. Formal tests of unconditional (Kupiec) and conditional (Christoffersen) coverage reveal that an LSTM baseline achieves near-nominal calibration. In contrast, a GARCH(1,1) model with Student-t innovations underestimates tail risk. Our proposed discount-factor DLM model produces a slightly liberal VaR estimate, with evidence of clustered violations. Bayesian logistic regression improves recall and AUC-ROC for fraud detection, and a hierarchical Beta state-space model provides transparent and adaptive compliance risk assessment. The pipeline is distinguished by precise uncertainty quantification, interpretability, and GPU-accelerated analysis, delivering up to 50x speedup. Remaining challenges include sparse fraud data and proxy compliance labels, but the framework enables actionable risk insights. Future expansion will extend feature sets, explore regime-switching priors, and enhance scalable inference.
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