Real-time detection of local no-arbitrage violations

• 成果类型: Article
• 署名作者: Andersen, Torben G.; Todorov, Viktor; Zhou, Bo
• 署名单位: Northwestern University; Virginia Polytechnic Institute & State University
• 刊物名称: QUANTITATIVE ECONOMICS
• ISSN/ISSBN: 1759-7323
• DOI: 10.3982/QE2585
• 发表日期: 2025
• 页码: 459-495
• 关键词: Asset price; high-frequency data; It & ocirc; semimartingale violation; real-time detection; stopping rule; C14; C22; G12

摘要

This paper focuses on the task of detecting local episodes involving violation of the standard It & ocirc; semimartingale assumption for financial asset prices in real time that might induce arbitrage opportunities. Our proposed detectors, defined as stopping rules, are applied sequentially to continually incoming high-frequency data. We show that they are asymptotically exponentially distributed in the absence of It & ocirc; semimartingale violations. On the other hand, when a violation occurs, we can achieve immediate detection under infill asymptotics. A Monte Carlo study demonstrates that the asymptotic results provide a good approximation to the finite-sample behavior of the sequential detectors. An empirical application to S&P 500 index futures data corroborates the effectiveness of our detectors in swiftly identifying the emergence of an extreme return persistence episode in real time.