Hiroshima-shi, Japan
Hiroshima-shi, Japan

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


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Takaishi T.,Hiroshima University of Economics | Chen T.T.,Hiroshima University | Zheng Z.,Tokyo University of Information Sciences
Progress of Theoretical Physics Supplement | Year: 2012

We analyze realized volatilities constructed using high-frequency stock data on the Tokyo Stock Exchange. In order to avoid non-trading hours issue in volatility calculations we define two realized volatilities calculated separately in the two trading sessions of the Tokyo Stock Exchange, i.e. morning and afternoon sessions. After calculating the realized volatilities at various sampling frequencies we evaluate the bias from the microstructure noise as a function of sampling frequency. Taking account of the bias to realized volatility we examine returns standardized by realized volatilities and confirm that price returns on the Tokyo Stock Exchange are described approximately by Gaussian time series with time-varying volatility, i.e. consistent with a mixture of distributions hypothesis.


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.


Takaishi T.,Hiroshima University of Economics
2015 IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, IEEE-CYBER 2015 | Year: 2015

In this paper we investigate an Ising model which simulates multiple financial time series. The model is called the multiple time series Ising model that introduces the interaction which couples to spins of other systems. We analyze the return time series data simulated by the model and find that several stylized facts such as volatility clustering appear in the model. Non-zero cross correlations between the absolute returns are also present in the model. On the other hand no cross correlations between returns are observed. We also estimate volatility of the return time series by the GARCH model and check the view of the finite-variance mixture of normal distributions for the return data by using the GARCH volatility. The results are found to be consistent with this veiw. © 2015 IEEE.


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.


Takaishi T.,Hiroshima University of Economics
Journal of Physics: Conference Series | Year: 2010

We perform the Bayesian inference of a GARCH model by the Metropolis-Hastings algorithm with an adaptive proposal density. The adaptive proposal density is assumed to be the Student's t-distribution and the distribution parameters are evaluated by using the data sampled during the simulation. We apply the method for the QGARCH model which is one of asymmetric GARCH models and make empirical studies for Nikkei 225, DAX and Hang indexes. We find that autocorrelation times from our method are very small, thus the method is very efficient for generating uncorrelated Monte Carlo data. The results from the QGARCH model show that all the three indexes show the leverage effect, i.e. the volatility is high after negative observations. © 2010 IOP Publishing Ltd.


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

In this paper we propose an Ising model which simulates multiple financial time series. Our model introduces the interaction which couples to spins of other systems. Simulations from our model show that time series exhibit the volatility clustering that is often observed in the real financial markets. Furthermore we also find non-zero cross correlations between the volatilities from our model. Thus our model can simulate stock markets where volatilities of stocks are mutually correlated. © Published under licence by IOP Publishing Ltd.


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

The realized stochastic volatility (RSV) model that utilizes the realized volatility as additional information has been proposed to infer volatility of financial time series. We consider the Bayesian inference of the RSV model by the Hybrid Monte Carlo (HMC) algorithm. The HMC algorithm can be parallelized and thus performed on the GPU for speedup. The GPU code is developed with CUDA Fortran. We compare the computational time in performing the HMC algorithm on GPU (GTX 760) and CPU (Intel i7-4770 3.4GHz) and find that the GPU can be up to 17 times faster than the CPU. We also code the program with OpenACC and find that appropriate coding can achieve the similar speedup with CUDA Fortran. © Published under licence by IOP Publishing Ltd.


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.


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.


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.

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