Intraday Stochastic Volatility in Discrete Price Changes: The Dynamic Skellam Model

• 成果类型: Article
• 署名作者: Koopman, Siem Jan; Lit, Rutger; Lucas, Andre
• 署名单位: Vrije Universiteit Amsterdam; Tinbergen Institute; Aarhus University; CREATES
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2017.1302878
• 发表日期: 2017
• 页码: 1490-1503
• 关键词: time-series; difference; frequency; inference

摘要

We study intraday stochastic volatility for four liquid stocks traded on the New York Stock Exchange using a new dynamic Skellam model for high-frequency tick-by-tick discrete price changes. Since the likelihood function is analytically intractable, we rely on numerical methods for its evaluation. Given the high number of observations per series per day (1000 to 10,000), we adopt computationally efficient methods including Monte Carlo integration. The intraday dynamics of volatility and the high number of trades without price impact require nontrivial adjustments to the basic dynamic Skellam model. In-sample residual diagnostics and goodness-of-fit statistics show that the final model provides a good fit to the data. An extensive day-to-day forecasting study of intraday volatility shows that the dynamic modified Skellam model provides accurate forecasts compared to alternative modeling approaches. Supplementary materials for this article are available online.