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Hiroshima-shi, Japan

Hiroshima University of Economics is a private university in Hiroshima city, Japan, established in 1967. Wikipedia.


Gabriel A.A.,Hiroshima University of Economics
Journal of Food Processing and Preservation | Year: 2014

This study established the inactivation behavior of Listeria monocytogenes in nonfat, low-fat and full-cream milks treated with multifrequency Dynashock ultrasound. Inactivation in all samples was biphasic, with an inactivation lag phase where injury accumulated, prior to log-linear inactivation phase. L.monocytogenes exhibited shortest lag phase of 20.57min in full-cream milk. L.monocytogenes exhibited slowest log-linear inactivation rate of -0.24logcolony-forming unit (cfu)/min in full-cream milk, and fastest inactivation rate of -0.37logcfu/min in low-fat milk. Inactivation rate was slowest in full-cream milk at -0.24logcfu/min, and fastest in low-fat milk at -0.37logcfu/min. Corrected decimal reduction time was shortest in full-cream milk at 24.81min, followed by those in nonfat and low-fat milk at 29.17 and 30.64min, respectively. These results suggest the importance of careful consideration of microbial inactivation behavior in the establishment of food process schedules. © 2014 Wiley Periodicals, Inc. Source


Takaishi T.,Hiroshima University of Economics
Procedia Computer Science | Year: 2013

Usually, the Bayesian inference of the GARCH model is preferably performed by the Markov Chain Monte Carlo (MCMC) method. In this study, we also take an alternative approach to the Bayesian inference by the importance sampling. Using a multivariate Student's t-distribution that approximates the posterior density of the Bayesian inference, we compare the performance of the MCMC and importance sampling methods. The overall performance can be measured in terms of statistical errors obtained for the same size of Monte Carlo data. The Bayesian inference of the GARCH model is performed by the MCMC method implemented by the Metropolis-Hastings algorithm and the importance sampling method for artificial return data and stock return data. We find that the statistical errors of the GARCH parameters from the importance sampling are smaller than or comparable to those obtained from the MCMC method. Therefore we conclude that the importance sampling method can also be applied effectively for the Bayesian inference of the GARCH model as an alternative method to the MCMC method. © 2013 The Authors. Source


Takaishi T.,Hiroshima University of Economics
Journal of Physics: Conference Series | Year: 2013

The stochastic volatility model is one of volatility models which infer latent volatility of asset returns. The Bayesian inference of the stochastic volatility (SV) model is performed by the hybrid Monte Carlo (HMC) algorithm which is superior to other Markov Chain Monte Carlo methods in sampling volatility variables. We perform the HMC simulations of the SV model for two liquid stock returns traded on the Tokyo Stock Exchange and measure the volatilities of those stock returns. Then we calculate the accuracy of the volatility measurement using the realized volatility as a proxy of the true volatility and compare the SV model with the GARCH model which is one of other volatility models. Using the accuracy calculated with the realized volatility we find that empirically the SV model performs better than the GARCH model. © IOP Publishing Ltd 2013. Source


Takaishi T.,Hiroshima University of Economics
Journal of Physics: Conference Series | Year: 2013

A spin model is used for simulations of financial markets. To determine return volatility in the spin financial market we use the GARCH model often used for volatility estimation in empirical finance. We apply the Bayesian inference performed by the Markov Chain Monte Carlo method to the parameter estimation of the GARCH model. It is found that volatility determined by the GARCH model exhibits «volatility clustering» also observed in the real financial markets. Using volatility determined by the GARCH model we examine the mixture-of-distribution hypothesis (MDH) suggested for the asset return dynamics. We find that the returns standardized by volatility are approximately standard normal random variables. Moreover we find that the absolute standardized returns show no significant autocorrelation. These findings are consistent with the view of the MDH for the return dynamics. Source


Takaishi T.,Hiroshima University of Economics
Journal of Physics: Conference Series | Year: 2014

The hybrid Monte Carlo algorithm (HMCA) is applied for Bayesian parameter estimation of the realized stochastic volatility (RSV) model. Using the 2nd order minimum norm integrator (2MNI) for the molecular dynamics (MD) simulation in the HMCA, we find that the 2MNI is more efficient than the conventional leapfrog integrator. We also find that the autocorrelation time of the volatility variables sampled by the HMCA is very short. Thus it is concluded that the HMCA with the 2MNI is an efficient algorithm for parameter estimations of the RSV model. © Published under licence by IOP Publishing Ltd. Source

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