Block Sampler and Posterior Mode Estimation for Asymmetric Stochastic Volatility Models (Published in "Computational Statistics and Data Analysis", 52-6, 2892-2910. February 2008. )

This article introduces a new efficient simulation smoother and disturbance smoother for asymmetric stochastic volatility models where there exists a correlation between today`s return and tomorrow`s volatility. The state vector is divided into several blocks where each block consists of many state variables. For each block, corresponding disturbances are sampled simultaneously from their conditional posterior distribution. The algorithm is based on the multivariate normal approximation of the conditional posterior density and exploits a conventional simulation smoother for a linear and Gaussian state space model. The performance of our method is illustrated using two examples (1) simple asymmetric stochastic volatility model and (2) asymmetric stochastic volatility model with state-dependent variances. The popular single move sampler which samples a state variable at a time is also conducted for comparison in the first example. It is shown that our proposed sampler produces considerable improvement in the mixing property of the Markov chain Monte Carlo chain.

"Block Sampler and Posterior Mode Estimation for Asymmetric Stochastic Volatility Models"

This article introduces a new efficient simulation smoother and disturbance smoother for asymmetric stochastic volatility models where there exists a correlation between today's return and tomorrow's volatility. The state vector is divided into several blocks where each block consists of many state variables. For each block, corresponding disturbances are sampled simultaneously from their conditional posterior distribution. The algorithm is based on the multivariate normal approximation of the conditional posterior density and exploits a conventional simulation smoother for a linear and Gaussian state space model. The performance of our method is illustrated using two examples (1) simple asymmetric stochastic volatility model and (2) asymmetric stochastic volatility model with state-dependent variances. The popular single move sampler which samples a state variable at a time is also conducted for comparison in the first example. It is shown that our proposed sampler produces considerable.

Block Sampler and Posterior Mode Estimation for A Nonlinear and Non-Gaussian State-Space Model with Correlated Errors

This article introduces a new efficient simulation smoother and disturbance smoother for general state-space models where there exists a correlation between error terms of the measurement and state equations. The state vector is divided into several blocks where each block consists of many state variables. For each block, corresponding disturbances are sampled simultaneously from their conditional posterior distribution. The algorithm is based on the multivariate normal approximation of the conditional posterior density and exploits a conventional simulation smoother for a linear and Gaussian state space model. The performance of our method is illustrated using two examples (1) stochastic volatility models with leverage effects and (2) stochastic volatility models with leverage effects and state-dependent variances. The popular single move sampler which samples a state variable at a time is also conducted for comparison in the first example. It is shown that our proposed sampler produces considerable improvement in the mixing property of the Markov chain Monte Carlo chain.

Control and analysis of a unified power flow controller

This paper presents a control scheme and comprehensive analysis for a unified power flow controller (UPFC) on the basis of theory, computer simulation and experiment. This developed theoretical analysis reveals that a conventional power feedback control scheme makes the UPFC induce power fluctuation in transient states. The conventional control scheme cannot attenuate the power fluctuation, and so the time constant of damping is independent of active and reactive power feedback gains integrated in its control circuit. This paper proposes an advanced control scheme which has the function of successfully damping out the power fluctuation. A UPFC rated at 10 kVA is designed and constructed, which is a combination of a series device consisting of three single-phase pulsewidth modulation (PWM) converters and a shunt device consisting of a three-phase diode rectifier. Although the dynamics of the shunt device are not included, it is possible to confirm and demonstrate the performance of the series device. Experimental results agree well with both analytical and simulated results and show viability and effectiveness of the proposed control scheme </p

Half-integer contributions to the quantum Hall conductivity from single Dirac cones

While the quantum Hall effect in graphene has been regarded as a realization of the anomaly associated with the massless Dirac particle carrying half the usual topological integer, this is hidden due to the doubling of the Dirac cones. In order to confirm the half-integer contribution from each Dirac cone, here we theoretically consider a lattice model in which the relative energy between the two Dirac points is systematically shifted. With an explicit calculation of the topological (Chern) number, we have demonstrated that each Dirac cone does indeed contribute to the Hall conductivity as the half odd integer series (... -3/2, -1/2, 1/2, 3/2, ...) when the Fermi energy traverses the (shifted sets of) Landau levels. The picture is also endorsed, via the bulk-edge correspondence, from the edge mode spectrum for the present model.Comment: 4 pages, 4 figure

Estimating Stochastic Volatility Models Using Daily Returns and Realized Volatility Simultaneously ( Revised in March 2008; Published in "Computational Statistics and Data Analysis", 53-6, 2404-2426. April 2009. )

Realized volatility, which is the sum of squared intraday returns over a certain interval such as a day, has recently attracted the attention of financial economists and econometricians as an accurate measure of the true volatility. In the real market,however, the presence of non-trading hours and market microstructure noise in transaction prices may cause the bias in the realized volatility. On the other hand, daily returns are less subject to the noise and therefore may provide additional information on the true volatility. From this point of view, we propose modeling realized volatility and daily returns simultaneously based on well-known stochastic volatility model. Using intraday data of Tokyo stock price index, we show that this model can estimate realized volatility biases and parameters simultaneously.We take a Bayesian approach and propose an efficient sampling algorithm to implement the Markov chain Monte Carlo method for our simultaneous model. The result of the model comparison between the simultaneous models using both naive and scaled realized volatilities indicates that the effect of non-trading hours is more essential than that of microstructure noise but still the latter has to be considered for better fitting. Our Bayesian approach has an advantage over the conventional two-step correction procedure in that we are able to take the uncertainty in estimation of both biases and parameters into account for the prediction and the evaluation of Value-at-Risk.

"Markov chain Monte Carlo method and its application to the stochastic volatility model"(in Japanese)

In the time series analysis of asset prices, the stochastic volatility models have recently attracted attentions of many researchers since it clearly describes time-varying variance of asset returns. However, it is difficult to evaluate the likelihood and obtain the maximum likelihood estimators of parameters for such models. We take Bayesian approach and use Markov chain Monte Carlo (MCMC) method to overcome such a problem. We first describe MCMC method and conduct a survey of the literature for its application to the stochastic volatility model. The empirical analysis of stock returns data is also given.