Weak Identification of Long Memory with Implications for Volatility Modeling

• 成果类型: Article
• 署名作者: Li, Jia; Phillips, Peter C. B.; Shi, Shuping; Yu, Jun
• 署名单位: Singapore Management University; Yale University; University of Auckland; Macquarie University; University of Macau
• 刊物名称: REVIEW OF FINANCIAL STUDIES
• ISSN/ISSBN: 0893-9454
• DOI: 10.1093/rfs/hhaf022
• 发表日期: 2025
• 页码: 3117
• 关键词: instrumental variables regression; LOG-PERIODOGRAM REGRESSION; ASYMPTOTIC INFERENCE; RETURN VOLATILITY; volume; range; estimators; variance

摘要

This paper explores implications of weak identification in common 'long memory' and recent 'rough' approaches to modeling volatility dynamics of financial assets. We unveil an asymptotic near-observational equivalence between a long memory model with weak autoregressive dynamics and a rough model with a near-unit autoregressive root. Standard methods struggle to distinguish them, and conventional asymptotics are invalid. We propose an identification-robust approach to construct confidence sets that reveal the uncertainty and aid inference. Empirical studies based on realized volatility and trading volume often fail to statistically reject either model, thereby providing evidence of their potential coexistence.