Threshold selection in jump-discriminant filter for discretely observed jump processes

Threshold estimation is one of the useful techniques in the inference for jump-type stochastic processes from discrete observations. In this method, a jump-discriminant filter is used to infer the continuous part and the jump part separately. Although there are several choices for the filter, statistics constructed via filters are often sensitive to the choice. This paper presents some numerical procedures for selecting a suitable filter based on observations. Key words: threshold estimation; jump-discriminant filter; integrated-volatility, asymptotic unbiasedness, plug-in method. MSC2000: 62M09; 62P05.

• Type: Journal
• Year: 2010
• Where: SMA
• Authors: Yasutaka Shimizu
• Keywords: Jump-discriminant Filter, Jump-type Stochastic Processes, SMA 2010, Solid Modeling, Threshold Estimation