发表机构
University of Illinois, Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出模块化框架VolRouter，将波动率控制转化为基于状态的估计器-控制器对路由问题，在四类金融场景中多数取得最优风险调整表现，证明波动率控制可视为策略选择问题。
AI 中文摘要
波动率控制将风险估计转换为投资组合敞口，但现有方法通常依赖固定的波动率估计器或预定义的控制规则，可能无法适应不断变化的市场条件。我们提出VolRouter，这是一个模块化框架，它将波动率控制表述为基于状态的、在估计器-控制器对上的路由问题。VolRouter首先将市场状况总结为与控制相关的状态轮廓，然后通过三个阶段执行路由：状态推断、切换审查和对选择。该路由器可使用基于规则、可学习或基于LLM的决策模块实现，而投资组合操作仍由预定义的控制策略生成。我们在标普500、多资产、比特币和USDT的波动率控制场景中评估VolRouter。VolRouter在四个场景中的三个场景中实现了最高的夏普比率。在标普500上，它将夏普比率从RV+朴素缩放的0.952提高到1.222，同时将最大回撤从15.10%降低到12.58%，将每日CVaR从1.76%降低到1.32%。在多资产场景中，它将夏普比率从1.498提高到1.540，将CVaR从1.56%降低到1.18%。比特币在风险调整后的表现上显示出类似的改进，而USDT提供了一个边界案例，其中更简单的状态感知选择器仍具有竞争力。消融和敏感性分析表明，改进来自相对策略评估和选择性持续切换，而非仅仅扩大策略库。这些结果表明，当风险管理要求随市场状态变化时，波动率控制可被视为策略选择问题。
英文摘要
Volatility control converts risk estimates into portfolio exposure, yet existing approaches often rely on a fixed volatility estimator or a pre-defined control rule that may not adapt to changing market conditions. We propose VolRouter, a modular framework that formulates volatility control as state-conditioned routing over estimator-controller pairs. VolRouter first summarizes market conditions into a control-relevant state profile and then performs routing through three stages: state inference, switch review, and pair selection. The Router can be implemented using rule-based, learnable, or LLM-based decision modules, while portfolio actions remain generated by predefined control policies. We evaluate VolRouter across S&P 500, Multi-Asset, Bitcoin, and USDT volatility-control settings. VolRouter achieves the highest Sharpe ratio in three of four settings. On S&P 500, it improves Sharpe from 0.952 for RV + Naive Scaling to 1.222 while reducing maximum drawdown from 15.10% to 12.58% and daily CVaR from 1.76% to 1.32%. On Multi-Asset, it improves Sharpe from 1.498 to 1.540 and reduces CVaR from 1.56% to 1.18%. Bitcoin shows similar improvements in risk-adjusted performance, while USDT provides a boundary case where simpler state-aware selectors remain competitive. Ablation and sensitivity analyses show that the improvement comes from relative policy evaluation and selective persistent switching rather than simply expanding the policy library. These results suggest that volatility control can be viewed as a policy-selection problem when risk management requirements vary across market states.
Comments24 pages, 6 figures, ACM ICAIF
