Sparse Kalman filtering approaches to realized covariance estimation from high frequency financial data. (English) Zbl 1458.62245
Summary: 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.
MSC:
| 62P05 | Applications of statistics to actuarial sciences and financial mathematics |
| 62M20 | Inference from stochastic processes and prediction |
Software:
astsaReferences:
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