用于全球波动率预测的溢出效应感知网络架构
Spillover-Informed Network Architecture for Global Volatility Forecasting
浏览论文内容
中文总结 AI 辅助
该研究提出溢出效应感知网络架构，利用跨市场关联信息提升全球波动率预测精度，在压力时期表现更优，能为波动率目标投资者带来显著经济价值。
中文摘要 AI 辅助
动荡时期跨境波动率冲击的溢出效应使得准确的股票市场波动率预测对风险管理、衍生品定价和监管资本尤为关键。本文研究模型纳入市场关联信息时波动率预测是否会提升，以及关联测度的选择是否重要。使用2015-2025年各主要地区29个股票指数的日度数据，本文让每个市场的预测依托其关联市场的近期波动率，各关联强度可由地理、收益率相关性或从数据估计的Diebold-Yilmaz（DY）溢出网络设定。该溢出效应感知预测模型相比标准异质性自回归基准，实现了约13%的样本外QLIKE损失降低（p<0.001），并在所有考虑的模型中获得最高的模型置信集p值。多个未明确纳入跨市场结构的基准模型被排除在90%模型置信集之外。在市场压力上升时期（新冠疫情崩盘、俄罗斯入侵乌克兰、2025年美国关税冲击），溢出效应感知预测相比结构相同但未纳入关联信息的模型，实现了约21%的QLIKE损失降低（p=0.018），而在平静时期无显著性能损失。该优势具有经济意义：波动率目标投资者为溢出效应感知预测每年愿意支付322-373个基点（扣除交易成本后），而替代模型仅为85-102个基点且无统计显著性。最后，当模型自行推断关联时，仅从预测目标就能恢复DY网络（置换p=0.0005）。
英文摘要
Spillover of volatility shocks across borders during turbulent periods makes accurate equity market volatility forecasts especially critical for risk management, derivatives pricing, and regulatory capital. In this paper, we examine whether volatility forecasts improve when models incorporate information on how markets are connected, and whether the choice of connection measure matters. Using daily data on 29 equity indices from every major region over 2015-2025, we let each market's forecast draw on the recent volatility of the markets linked to it, with the strength of each link set either by geography, return correlation, or the Diebold-Yilmaz (DY) spillover network estimated from the data. The spillover-informed forecasting model achieved an approximately 13\% reduction in out-of-sample QLIKE loss relative to the standard Heterogeneous AutoRegressive benchmark ($p<0.001$) and attained the highest model confidence set $p$-value among the models considered. Several benchmark models that do not explicitly incorporate cross-market structure were excluded from the 90\% model confidence set. The greatest improvements were observed during periods of elevated market stress: the COVID-19 crash, the Russian invasion of Ukraine, and the $2025$ US tariff shock, spillover-informed forecasts achieved approximately 21\% lower QLIKE loss than an otherwise identical network-blind model ($p = 0.018$), with no significant performance loss during calm periods. The advantage is economically material: a volatility-targeting investor would pay $322$-$373$ basis points per year, net of transaction costs, for spillover-informed forecasts, versus an insignificant $85$-$102$ basis point for the alternatives. Finally, when the model is left to infer connections on its own, it recovers the DY network from the forecast objective alone (permutation $p=0.0005$).
