A Simple Model for Stocks Markets

Sánchez, J. R.

doi:10.1142/s0129183102003413

Cited by 8 publications

(8 citation statements)

References 6 publications

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“…In physics to understand financial market dynamics a variety of agent-based models have been proposed and examined [19,20,21,22,23,24,25,27,28,29,30,31,32,33]. It is found that to some extent those models are able to capture some of stylized facts such as fat-tailed distributions of returns and long autocorrelation times of the absolute returns.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Spin Financial Market by GARCH Model

Takaishi¹

2013

J. Phys.: Conf. Ser.

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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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Section: Introductionmentioning

confidence: 99%

Analysis of Spin Financial Market by GARCH Model

Takaishi¹

2013

J. Phys.: Conf. Ser.

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“…From a theoretical point of view, it is clear that it could be very useful to have available a model that could reproduce as closely as possible the behavior of real-world markets. Here a multiscaling analysis on the time series generated by a recently reported model for stock markets is presented [6]. It is found that the scaling properties of the artificial time series generated by the model are in excellent agreement with those of actual time series.…”

Section: Introductionmentioning

confidence: 92%

“…On the other hand, several computational models trying to represent the behavior of actual stock markets have been presented [6,7,8,9]. From a theoretical point of view, it is clear that it could be very useful to have available a model that could reproduce as closely as possible the behavior of real-world markets.…”

Section: Introductionmentioning

confidence: 99%

Multiscale behavior of a simple model for stock markets

Sánchez

2005

Discrete Dynamics in Nature and Society

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The multiscale behavior of a recently reported model for stock markets is presented. It has been shown that indexes of real-world markets display absolute returns with memory properties on a long-time range, a phenomenon known as cluster volatility. The multiscale characteristics of an index are studied by analyzing the power-law scaling of the volatility correlations which display nonunique scaling exponents. Here such analysis is done on an artificial time series produced by a simple model for stock markets. After comparison, excellent agreements with the multiscale behavior of real-time series are found.

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“…Actually many models are able to capture some of the stylized facts. [3][4][5][6][7][8][9][10][11][12][13][14] In empirical finance the volatility is an important value to measure the risk. One of the stylized facts of the volatility is that the volatility of price returns changes in time and shows clustering, so called "volatility clustering".…”

Section: Introductionmentioning

confidence: 99%

Bayesian Inference of Stochastic Volatility Model by Hybrid Monte Carlo

Takaishi

2009

J CIRCUIT SYST COMP

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The hybrid Monte Carlo (HMC) algorithm is applied for the Bayesian inference of the stochastic volatility (SV) model. We use the HMC algorithm for the Markov chain Monte Carlo updates of volatility variables of the SV model. First we compute parameters of the SV model by using the artificial financial data and compare the results from the HMC algorithm with those from the Metropolis algorithm. We find that the HMC algorithm decorrelates the volatility variables faster than the Metropolis algorithm. Second we make an empirical study for the time series of the Nikkei 225 stock index by the HMC algorithm. We find the similar correlation behavior for the sampled data to the results from the artificial financial data and obtain a φ value close to one (φ ≈ 0.977), which means that the time series has the strong persistency of the volatility shock.

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A Simple Model for Stocks Markets

Cited by 8 publications

References 6 publications

Analysis of Spin Financial Market by GARCH Model

Analysis of Spin Financial Market by GARCH Model

Multiscale behavior of a simple model for stock markets

Bayesian Inference of Stochastic Volatility Model by Hybrid Monte Carlo

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