Sparse Kalman Filtering Approaches to Covariance Estimation from High Frequency Data in the Presence of Jumps. (arXiv:1602.02185v1 [q-fin.ST])

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-EM (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.