"Separating Information Maximum Likelihood Estimation of Realized Volatility and Covariance with Micro-Market Noise"

For estimating the realized volatility and covariance by using high frequency data, we introduce the Separating Information Maximum Likelihood (SIML) method when there are possibly micro-market noises. The resulting estimator is simple and it has the representation as a specific quadratic form of returns. The SIML estimator has reasonable asymptotic properties; it is consistent and it has the asymptotic normality (or the stable convergence in the general case) when the sample size is large under general conditions including non-Gaussian processes and volatility models. Based on simulations, we find that the SIML estimator has reasonable finite sample properties and thus it would be useful for practice. It is also possible to use the limiting distribution of the SIML estimator for constructing testing procedures and confidence intervals.

"Robustness of the Separating Information Maximum Likelihood Estimation of Realized Volatility with Micro-Market Noise"

For estimating the realized volatility and covariance by using high frequency data, Kunitomo and Sato (2008a,b) have proposed the Separating Information Maximum Likelihood (SIML) method when there are micro-market noises. The SIML estimator has reasonable asymptotic properties; it is consistent and it has the asymptotic normality (or the stable convergence in the general case) when the sample size is large under general conditions including non-Gaussian processes and volatility models. We also show that the SIML estimator has the asymptotic robustness in the sense that it is consistent and it has the asymptotic normality when there are autocorrelations in the market noise terms and there are endogenous correlations between the signal and noise terms.

"Realized Volatility, Covariance and Hedging Coefficient of the Nikkei-225 Futures with Micro-Market Noise"

For the estimation problem of the realized volatility, covariance and hedging coefficient by using high frequency data with possibly micro-market noises, we use the Separating Information Maximum Likelihood (SIML) method, which was recently developed by Kunitomo and Sato (2008). By analyzing the Nikkei 225 futures and spot index markets, we have found that the estimates of realized volatility, covariance and hedging coefficient have significant bias by the traditional method which should be corrected. Our method can handle the estimation bias and the tick-size effects of Nikkei 225 futures by removing the possible micro-market noise in multivariate high frequency data.

"On Properties of Separating Information Maximum Likelihood Estimation of Realized Volatility and Covariance with Micro-Market Noise"

For estimating the realized volatility and covariance by using high frequency data, we have introduced the Separating Information Maximum Likelihood (SIML) method when there are possibly micro-market noises by Kunitomo and Sato (2008a, 2008b, 2010a, 2010b). The resulting estimator is simple and it has the representation as a specific quadratic form of returns. We show that the SIML estimator has reasonable asymptotic properties; it is consistent and it has the asymptotic normality (or the stable convergence in the general case) when the sample size is large under general conditions including some non-Gaussian processes and some volatility models. Based on simulations, we find that the SIML estimator has reasonable finite sample properties and thus it would be useful for practice. The SIML estimator has the asymptotic robustness properties in the sense it is consistent when the noise terms are weakly dependent and they are endogenously correlated with the efficient market price process. We also apply our method to an analysis of Nikkei-225 Futures, which has been the major stock index in the Japanese financial sector.

"A Generalized SSAR Model and Predictive Distribution with an Application to VaR"

The asymmetrical movements between the downward and upward phases of the sample paths of time series have been sometimes observed. By generalizing the SSAR (simultaneous switching autoregressive) models, we introduce a class of nonlinear time series models having the asymmetrical sample paths in the upward and downward phases. We show that the class of generalized SSAR models is useful for estimating the asymmetrical predictive distribution given the present and past information. Applications to the prediction based on the predictive median and the estimation of the VaR (value at risk) in financial risk management are discussed.

かご型シロキサンの自己集合による結晶性ナノ構造体の形成

早大学位記番号:新8162早稲田大

Abstract Data Types in Event-B - An Application of Generic Instantiation

Integrating formal methods into industrial practice is a challenging task. Often, different kinds of expertise are required within the same development. On the one hand, there are domain engineers who have specific knowledge of the system under development. On the other hand, there are formal methods experts who have experience in rigorously specifying and reasoning about formal systems. Coordination between these groups is important for taking advantage of their expertise. In this paper, we describe our approach of using generic instantiation to facilitate this coordination. In particular, generic instantiation enables a separation of concerns between the different parties involved in developing formal systems.Comment: In Proceedings of DS-Event-B 2012: Workshop on the experience of and advances in developing dependable systems in Event-B, in conjunction with ICFEM 2012 - Kyoto, Japan, November 13, 201