[Abstract]
　When estimating the covariance between two different asset returns with financial high frequency data, we need to simultaneously solve two different kind of problems: non-synchronous bias and market micro structure noise. Cumulative Covariance estimator was proposed by Hayashi and Yoshida (2005) to construct an unbiased estimator with non-synchronous data. Subsampling methods have been well known as a determined technique to reduce the variance of realized variance estimators under the situation where data are contaminated with the Market Microstructure Noise. The subsampling version of Cumulative Covariance estimator has been recognized as a possible candidate to handle these two problems through recent studies such as voev and Lunde (2007), Griffin and Oomen (2011). In this paper we examine the Susampling Cumulative Covariance (SCC) estimator as a special form of the Weighted Realized Covariance (WRC) proposed by Kanatani (2004). By minimizing the Mean Squared Error of the WRC we provide a framework to select the number of its subgrids which play a central role in the SCC estimator. Monte Carlo experiments show that the SCC estimator outperforms the other existing ones.

Program

Program

【京都大学大学院経済学研究科・国際経済学セミナーと共催】 要旨：What determines the distributions of skills, occupations, and industries across cities? We develop a theory to jointly address these fundamental questions about the spatial organization of economies. Our model incorporates a system of cities, their internal urban structures, and a high-dimensional theory of factor-driven comparative advantage. It predicts that larger cities will be skill abundant and specialize in skillintensive activities according to the monotone likelihood ratio property. We test the model using data on 270 US metropolitan areas, 3 to 9 educational categories, 22 occupations, and 21 manufacturing industries. The results provide support for our theory's predictions.