Title: Detecting factors of quadratic variation in the presence of microstructure noise
Authors: Daisuke Kurisu - Tokyo Institute of Technology (Japan) [presenting]
Abstract: A new method is developed for detecting hidden factors of Quadratic Variation (QV) of It\^o semimartingales from a set of discrete observations when the market microstructure noise is present. We propose a statistical way to determine the number of factors of quadratic co-variations of asset prices based on the SIML (separating information maximum likelihood) method. In high-frequency financial data, it is important to disentangle the effects of the possible jumps and the market microstructure noise existed in financial markets. We explore the variance-covariance matrix of hidden returns of the underlying It\^o semimartingales and investigate its characteristic roots and vectors of the estimated quadratic variation. We give some simulation results to see the finite sample properties of the proposed method and illustrate an empirical data analysis on the Tokyo stock market.