发表机构
University of Messina; Luiss University（墨西拿大学; 卢伊斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出尾部风险指标IlliQaR，结合Amihud度量与含跳跃成分的计量模型，实证发现纳入跳跃可提升非流动性预测，且IlliQaR是系统性风险指标。
AI 中文摘要
市场效率从根本上依赖于稳定的流动性，因此，预测流动性动态是投资者和监管机构的优先事项。我们引入一种新的尾部风险指标——非流动性风险（Illiquidity-at-Risk，IlliQaR），旨在量化极端流动性枯竭的程度。我们基于已实现的Amihud（一种精确的非流动性度量，由高频数据推导而来，等于已实现波动率与交易量的比率），评估各类线性和非线性计量经济学模型的预测能力，特别关注不连续跳跃成分的影响。考虑这些跳跃对于实现准确的概率覆盖和在系统性压力时期做出更优的IlliQaR预测至关重要，在此期间，标准连续模型会系统性低估流动性蒸发的严重程度。我们的实证分析涵盖标普500指数及25只美国大型股票的横截面，结果表明，纳入跳跃成分可显著提升非流动性预测效果。我们的研究结果显示，个股的IlliQaR违规行为常聚集于标普500指数流动性压力时期，这表明非流动性风险（Illiquidity at Risk）不仅是局部问题，更是系统性问题，其中主要指数可作为个股流动性极端枯竭的领先指标。
英文摘要
Market efficiency relies fundamentally on stable liquidity. Consequently, forecasting liquidity dynamics is a priority for both investors and regulators. We introduce a new tail-risk metric, Illiquidity-at-Risk (IlliQaR), designed to quantify the magnitude of extreme liquidity dry-ups. Relying upon the realized Amihud (a precise illiquidity measurement derived from high-frequency data as the ratio of realized volatility to trading volume) we assess the predictive power of various linear and non-linear econometric models, with a specific focus on the impact of discontinuous jump components. Accounting for these jumps is essential for achieving accurate probability coverage and better IlliQaR predictions during periods of systemic stress, where standard continuous models systematically underestimate the severity of liquidity evaporation. Our empirical analysis, encompassing the S&P 500 index and a cross-section of 25 large U.S. equities, demonstrates that incorporating jumps significantly improves forecasts of illiquidity. Our results suggest that individual stock IlliQaR violations often cluster during periods of S&P 500 liquidity stress. This indicates that Illiquidity at Risk is not just a localized concern but a systemic one, where the main index acts as a leading indicator for extreme dry-ups in individual stock liquidity.
