Multiple Time Series Ising Model for Financial Market Simulations

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.

[1]  Ryuichi Yamamoto Asymmetric volatility, volatility clustering, and herding agents with a borrowing constraint , 2010 .

[2]  John Ellis,et al.  Int. J. Mod. Phys. , 2005 .

[3]  R. Cont Empirical properties of asset returns: stylized facts and statistical issues , 2001 .

[4]  Analysis of Realized Volatility in Two Trading Sessions of the Japanese Stock Market(Financial and Consumer Markets,Proceedings of the YITP Workshop on Econophysics,Econophysics 2011-The Hitchhiker's Guide to the Economy-) , 2012, 1304.6006.

[5]  Bornholdt's Spin Model of a Market Dynamics in High Dimensions , 2001, cond-mat/0110279.

[6]  S. Bornholdt,et al.  Expectation Bubbles in a Spin Model of Markets , 2001, cond-mat/0105224.

[7]  T. Bollerslev,et al.  Continuous-Time Models, Realized Volatilities, and Testable Distributional Implications for Daily Stock Returns , 2007 .

[8]  H. Stanley,et al.  Quantifying and Modeling Long-Range Cross-Correlations in Multiple Time Series with Applications to World Stock Indices , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  M. Oshikawa,et al.  Random matrix theory analysis of cross correlations in financial markets. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  P. Clark A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices , 1973 .

[11]  S. Bornholdt,et al.  Dynamics of price and trading volume in a spin model of stock markets with heterogeneous agents , 2002, cond-mat/0207253.

[12]  T. Takaishi SIMULATIONS OF FINANCIAL MARKETS IN A POTTS-LIKE MODEL , 2005, cond-mat/0503156.

[13]  V. Plerou,et al.  Random matrix approach to cross correlations in financial data. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  T. Takaishi Finite-Sample Effects on the Standardized Returns of the Tokyo Stock Exchange☆ , 2012 .

[15]  V. Plerou,et al.  Universal and Nonuniversal Properties of Cross Correlations in Financial Time Series , 1999, cond-mat/9902283.

[16]  F. Diebold,et al.  The distribution of realized stock return volatility , 2001 .

[17]  F. Diebold,et al.  The Distribution of Realized Exchange Rate Volatility , 2000 .

[18]  T. Takaishi Analysis of Spin Financial Market by GARCH Model , 2013, 1409.0118.

[19]  Torben G. Andersen,et al.  No-arbitrage semi-martingale restrictions for continuous-time volatility models subject to leverage effects, jumps and i.i.d. noise: Theory and testable distributional implications , 2007 .

[20]  T. Bollerslev,et al.  ANSWERING THE SKEPTICS: YES, STANDARD VOLATILITY MODELS DO PROVIDE ACCURATE FORECASTS* , 1998 .

[21]  Hawoong Jeong,et al.  Systematic analysis of group identification in stock markets. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.