CHOOSING BETWEEN PERSISTENT AND STATIONARY VOLATILITY

• 成果类型: Article
• 署名作者: Chronopoulos, Ilias; Giraitis, Liudas; Kapetanios, George
• 署名单位: University of Essex; University of London; Queen Mary University London; University of London; King's College London
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/22-AOS2236
• 发表日期: 2022
• 页码: 3466-3483
• 关键词: maximum likelihood estimation; time-series; statistical-inference; MODEL; variance; DYNAMICS; GARCH

摘要

This paper suggests a multiplicative volatility model where volatility is decomposed into a stationary and a nonstationary persistent part. We pro -vide a testing procedure to determine which type of volatility is prevalent in the data. The persistent part of volatility is associated with a nonstation-ary persistent process satisfying some smoothness and moment conditions. The stationary part is related to stationary conditional heteroskedasticity. We outline theory and conditions that allow the extraction of the persistent part from the data and enable standard conditional heteroskedasticity tests to de-tect stationary volatility after persistent volatility is taken into account. Monte Carlo results support the testing strategy in small samples. The empirical ap-plication of the theory supports the persistent volatility paradigm, suggesting that stationary conditional heteroskedasticity is considerably less pronounced than previously thought.