Sparse Kalman filtering approaches to realized covariance estimation from high frequency financial data

• 成果类型: Article; Proceedings Paper
• 署名作者: Ho, Michael; Xin, Jack
• 署名单位: University of California System; University of California Irvine
• 刊物名称: MATHEMATICAL PROGRAMMING
• ISSN/ISSBN: 0025-5610
• DOI: 10.1007/s10107-019-01371-6
• 发表日期: 2019
• 页码: 247-278
• 关键词: jump dynamics; volatility; noisy

摘要

Estimation of the covariance matrix of asset returns from high frequency data is complicated by asynchronous returns, market microstructure noise and jumps. One technique for addressing both asynchronous returns and market microstructure is the Kalman-Expectation-Maximization (KEM) algorithm. However the KEM approach assumes log-normal prices and does not address jumps in the return process which can corrupt estimation of the covariance matrix. In this paper we extend the KEM algorithm to price models that include jumps. We propose a sparse Kalman filtering approach to this problem. In particular we develop a Kalman Expectation Conditional Maximization algorithm to determine the unknown covariance as well as detecting the jumps. In order to promote a sparse estimate of the jumps,we consider both Laplace and the spike and slab jump priors. Numerical results using simulated data show that each of these approaches provide for improved covariance estimation relative to the KEM method in a variety of settings where jumps occur.