Hiroshima-shi, Japan

Hiroshima University of Economics

www.hue.ac.jp
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
Physica A: Statistical Mechanics and its Applications | Year: 2017

We propose a new ARCH-type model that uses a rational function to capture the asymmetric response of volatility to returns, known as the “leverage effect”. Using 10 individual stocks on the Tokyo Stock Exchange and two stock indices, we compare the new model with several other asymmetric ARCH-type models. We find that according to the deviance information criterion, the new model ranks first for several stocks. Results show that the proposed new model can be used as an alternative asymmetric ARCH-type model in empirical applications. © 2017 Elsevier B.V.


Takaishi T.,Hiroshima University of Economics
Journal of Physics: Conference Series | Year: 2017

We perform a large-scale simulation of an Ising-based financial market model that includes 300 asset time series. The financial system simulated by the model shows a fat-tailed return distribution and volatility clustering and exhibits unstable periods indicated by the volatility index measured as the average of absolute-returns. Moreover, we determine that the cumulative risk fraction, which measures the system risk, changes at high volatility periods. We also calculate the inverse participation ratio (IPR) and its higher-power version, IPR6, from the absolute-return cross-correlation matrix. Finally, we show that the IPR and IPR6 also change at high volatility periods. © Published under licence by IOP Publishing Ltd.


Takaishi T.,Hiroshima University of Economics
Journal of Physics: Conference Series | Year: 2016

We study stock market instability by using cross-correlations constructed from the return time series of 366 stocks traded on the Tokyo Stock Exchange from January 5, 1998 to December 30, 2013. To investigate the dynamical evolution of the cross-correlations, crosscorrelation matrices are calculated with a rolling window of 400 days. To quantify the volatile market stages where the potential risk is high, we apply the principal components analysis and measure the cumulative risk fraction (CRF), which is the system variance associated with the first few principal components. From the CRF, we detected three volatile market stages corresponding to the bankruptcy of Lehman Brothers, the 2011 Tohoku Region Pacific Coast Earthquake, and the FRB QE3 reduction observation in the study period. We further apply the random matrix theory for the risk analysis and find that the first eigenvector is more equally de-localized when the market is volatile. © Published under licence by IOP Publishing Ltd.


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
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 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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