A mini-review on econophysics:Comparative study of Chinese and western financial markets

We present a review of our recent research in econophysics, and focus on the comparative study of Chinese and western financial markets. By virtue of concepts and methods in statistical physics, we investigate the time correlations and spatial structure of financial markets based on empirical high-frequency data. We discover that the Chinese stock market shares common basic properties with the western stock markets, such as the fat-tail probability distribution of price returns, the long-range auto-correlation of volatilities, and the persistence probability of volatilities, while it exhibits very different higher-order time correlations of price returns and volatilities, spatial correlations of individual stock prices, and large-fluctuation dynamic behaviors. Furthermore, multi-agent-based models are developed to simulate the microscopic interaction and dynamic evolution of the stock markets.     

Market dynamics and stock price volatility

This paper presents a possible explanation for some of the empirical properties of asset returns within a heterogeneous-agents framework. The model turns out, even if we assume the input fundamental value follows an simple Gaussian distribution lacking both fat tails and volatility dependence, these features can show up in the time series of asset returns. In this model, the profit comparison and switching between heterogeneous play key roles, which build a connection between endogenous market and the emergence of stylized facts.Received: 21 January 2004, Published online: 12 July 2004PACS: 89.65.Gh Economics; econophysics, financial markets, business and management - 87.23.Ge Dynamics of social systems - 05.10.-a Computational methods in statistical physics and nonlinear dynamics     

Effects of saving and spending patterns on holding time distribution

The effects of saving and spending patterns on holding time distribution of money are investigated based on the ideal gas-like models. We show the steady-state distribution obeys an exponential law when the saving factor is set uniformly, and a power law when the saving factor is set diversely. The power distribution can also be obtained by proposing a new model where the preferential spending behavior is considered. The association of the distribution with the probability of money to be exchanged has also been discussed.Received: 4 September 2003, Published online: 19 November 2003PACS: 89.65.Gh Economics; econophysics, financial markets, business and management - 87.23.Ge Dynamics of social systems - 05.10.-a Computational methods in statistical physics and nonlinear dynamics - 02.50.-r Probability theory, stochastic processes, and statistics     

Networks of equities in financial markets

We review the recent approach of correlation based networks of financial equities. We investigate portfolio of stocks at different time horizons, financial indices and volatility time series and we show that meaningful economic information can be extracted from noise dressed correlation matrices. We show that the method can be used to falsify widespread market models by directly comparing the topological properties of networks of real and artificial markets.Received: 26 November 2003, Published online: 14 May 2004PACS: 89.75.Fb Structures and organization in complex systems - 89.75.Hc Networks and genealogical trees - 89.65.Gh Economics; econophysics, financial markets, business and management     

Partial derivative approach for option pricing in a simple stochastic volatility model

We study a market model in which the volatility of the stock may jump at a random time from a fixed value to another fixed value. This model has already been introduced in the literature. We present a new approach to the problem, based on partial differential equations, which gives a different perspective to the issue. Within our framework we can easily consider several forms for the market price of volatility risk, and interpret their financial meaning. We thus recover solutions previously mentioned in the literature as well as obtaining new ones.Received: 13 May 2004, Published online: 26 November 2004PACS: 02.30.Jr Partial differential equations - 02.50.Ey Stochastic processes - 02.70.Uu Applications of Monte Carlo methods - 89.65.Gh Economics; econophysics, financial markets, business and management     

金融动力学的时空关联与大波动特性——兼谈中西方金融市场的对比研究

文章扼要地评述了金融物理学研究进展,介绍了文章作者在金融动力学时空关联方面的最新研究成果,特别关注中西方金融市场的对比研究.唯象理论研究表明,西方金融市场的价格收益率和波动率的时间关联显示杠杆效应,而中国金融市场则显示反杠杆效应;一种价格收益率和波动率的反馈相互作用可以解释杠杆和反杠杆效应的起源.西方金融市场的个体股票价格的交叉关联呈现标准的行业板块结构,而中国金融市场展示的是一种特殊的板块结构,如"ST板块"和"蓝筹板块"等.股票价格大波动可分为动力学内部产生的和外部事件诱导的两大类.金融动力学的时间反演不对称性,主要来源于外部事件诱导的大波动.     

Detrended cross-correlation analysis: a new method for analyzing two nonstationary time series

Here we propose a new method, detrended cross-correlation analysis, which is a generalization of detrended fluctuation analysis and is based on detrended covariance. This method is designed to investigate power-law cross correlations between different simultaneously recorded time series in the presence of nonstationarity. We illustrate the method by selected examples from physics, physiology, and finance.     

Crossover Phenomena in Detrended Fluctuation Analysis Used in Financial Markets

A systematic analysis of Shanghai and Japan stock indices for the period of Jan. 1984 to Dec. 2005 is performed. After stationarity is verified by ADF （Augmented Dickey-Fuller） test, the power spectrum of the data exhibits a power law decay as a whole characterized by 1/f^β processes with possible long range correlations. Subsequently, by using the method of detrended fluctuation analysis （DFA） of the general volatility in the stock markets, we find that the long-range correlations are occurred among the return series and the crossover phenomena exhibit in the results obviously.Further, Shanghai stock market shows long-range correlations in short time scale and shows short-range correlations in long time scale. Whereas, for Japan stock market, the data behaves oppositely absolutely. Last, we compare the varying of scale exponent in large volatility between two stock markets. All results obtained may indicate the possibility of characteristic of multifractal scaling behavior of the financial markets.     

Multifractal analysis of the Korean agricultural market

We have studied the long-term memory effects of the Korean agricultural market using the detrended fluctuation analysis (DFA) method. In general, the return time series of various financial data, including stock indices, foreign exchange rates, and commodity prices, are uncorrelated in time, while the volatility time series are strongly correlated. However, we found that the return time series of Korean agricultural commodity prices are anti-correlated in time, while the volatility time series are correlated. The $n$-point correlations of time series were also examined, and it was found that a multifractal structure exists in Korean agricultural market prices.     

Micro and macro benefits of random investments in financial markets

In this paper, making use of recent statistical physics techniques and models, we address the specific role of randomness in financial markets, both at the micro and the macro level. In particular, we review some recent results obtained about the effectiveness of random strategies of investment, compared with some of the most used trading strategies for forecasting the behaviour of real financial indexes. We also push forward our analysis by means of a self-organised criticality model, able to simulate financial avalanches in trading communities with different network topologies, where a Pareto-like power law behaviour of wealth spontaneously emerges. In this context, we present new findings and suggestions for policies based on the effects that random strategies can have in terms of reduction of dangerous financial extreme events, i.e. bubbles and crashes.     

Comparing the structure of an emerging market with a mature one under global perturbation

In this paper we investigate the Tehran stock exchange (TSE) and Dow Jones Industrial Average (DJIA) in terms of perturbed correlation matrices. To perturb a stock market, there are two methods, namely local and global perturbation. In the local method, we replace a correlation coefficient of the cross-correlation matrix with one calculated from two Gaussian-distributed time series, whereas in the global method, we reconstruct the correlation matrix after replacing the original return series with Gaussian-distributed time series. The local perturbation is just a technical study. We analyze these markets through two statistical approaches, random matrix theory (RMT) and the correlation coefficient distribution. By using RMT, we find that the largest eigenvalue is an influence that is common to all stocks and this eigenvalue has a peak during financial shocks. We find there are a few correlated stocks that make the essential robustness of the stock market but we see that by replacing these return time series with Gaussian-distributed time series, the mean values of correlation coefficients, the largest eigenvalues of the stock markets and the fraction of eigenvalues that deviate from the RMT prediction fall sharply in both markets. By comparing these two markets, we can see that the DJIA is more sensitive to global perturbations. These findings are crucial for risk management and portfolio selection.     

Is econophysics a new discipline? The neopositivist argument

Econophysics is a new approach which applies various models and concepts associated with statistical physics to economic (and financial) phenomena. This field of research is a new step in the history and the evolution of Physics Sciences and the question about the disciplinary characteristics of this field must be asked. At first glance, it might appear that economics and econophysics share the same subject of research (that of analysis of economic reality). In this paper I will use neopositivism to show that econophysics is methodologically very different from economics and that it can be considered as a separate discipline. The neopositivist framework provides econophysics with some arguments for rejecting mainstream economics.     

Majority orienting model for the oscillation of market price

The present paper introduces a majority orienting model in which the dealers behavior changes based on the influence of the price to show the oscillation of stock price in the stock market. We show the oscillation of the price for the model by applying the van der Pol equation which is a deterministic approximation of our model.Received: 29 October 2003, Published online: 15 March 2004PACS: 89.65.Gh Economics; econophysics, financial markets, business and management - 05.45.Tp Time series analysis - 02.50.Ey Stochastic processesY. Itoh: Also at The Graduate University for Advanced Studies     

Measuring daily Value-at-Risk of SSEC index: A new approach based on multifractal analysis and extreme value theory

Recent studies in the econophysics literature reveal that price variability has fractal and multifractal characteristics not only in developed financial markets, but also in emerging markets. Taking high-frequency intraday quotes of the Shanghai Stock Exchange Component (SSEC) Index as example, this paper proposes a new method to measure daily Value-at-Risk (VaR) by combining the newly introduced multifractal volatility (MFV) model and the extreme value theory (EVT) method. Two VaR backtesting techniques are then employed to compare the performance of the model with that of a group of linear and nonlinear generalized autoregressive conditional heteroskedasticity (GARCH) models. The empirical results show the multifractal nature of price volatility in Chinese stock market. VaR measures based on the multifractal volatility model and EVT method outperform many GARCH-type models at high-risk levels.     

Non-linear characteristics and long-range correlations in Asian stock markets

We test several non-linear characteristics of Asian stock markets, which indicates the failure of efficient market hypothesis and shows the essence of fractal of the financial markets. In addition, by using the method of detrended fluctuation analysis (DFA) to investigate the long range correlation of the volatility in the stock markets, we find that the crossover phenomena exist in the results of DFA. Further, in the region of small volatility, the scaling behavior is more complicated; in the region of large volatility, the scaling exponent is close to 0.5, which suggests the market is more efficient. All these results may indicate the possibility of characteristic multifractal scaling behaviors of the financial markets.     

Special issue on Econophysics

What is “Econophysics"? Who is an “econophysicist"? The coining of a new scientific term, composed of the names of two fields, traditionally considered to be far from each other, brings new dreams to investigators, by mere virtue of a new ensemble of viewpoints. The term “econophysics" has revealed a kinship between the fields of physics and economics, which was not obvious before. The first officially recognized conference by a professional society on “econophysics", Applications of Physics to Financial Analysis (APFA, soon to become APFA1) was held in Dublin in 1999. Since then APFA and its companion meetings have begun to reveal new branches of research from the established pathways explored in applied statistical physics and thus economics (in particular finance). The analysis of fluctuations in financial data by new or modified techniques has led to new insights. Such analysis involves physicists looking for correlation between entities in financial matter in much the same way as they have done for physical systems in their laboratories. This approach leads to useful new methods and results in different outputs. The studies of phase transitions and non-equilibrium effects, including self-organisation have progressed the understanding of many physical phenomena. So why not use the same methodology in a field which is thought to be governed by sociology, psychology, politics and other so called softer science? The observations of deterministic chaos, scaling, in financial time series (tools such as recurrence, plots exploiting symmetries in pricing theory or the use of the wavelet or path integral or renormalisation group method) will still give some work ahead even though all these tools have a basic origin or are rather standard tools nowadays. Characterization of data and theory talks broke new ground in pursuit of e.g. useful strategies or political consequences. One continues to ask, how is it that fluctuations or other agents in a system conspire to give surprising anomalous properties? By broadening discussion to the category of econophysics topics, as covered in APFA2 (held in Liège, Belgium on July 13-15, 2000), we have gained new paradigms to study this question. Several reports to APFA2 are not included in the following to avoid duplicating reports in this proceedings. Very warm and profound acknowledgments are in order here. APFA2 was made possible mainly by the European Physical Society (EPS), the Fond National de la Recherche Scientifique (FNRS, Brussels), the Fonds voor Wetenschappelijk Onderzoek-Vlaanderen (FWO), and the University of Liège.     

A New Look at Calendar Anomalies: Multifractality and Day-of-the-Week Effect

Stock markets can become inefficient due to calendar anomalies known as the day-of-the-week effect. Calendar anomalies are well known in the financial literature, but the phenomena remain to be explored in econophysics. This paper uses multifractal analysis to evaluate if the temporal dynamics of market returns also exhibit calendar anomalies such as day-of-the-week effects. We apply multifractal detrended fluctuation analysis (MF-DFA) to the daily returns of market indices worldwide for each day of the week. Our results indicate that distinct multifractal properties characterize individual days of the week. Monday returns tend to exhibit more persistent behavior and richer multifractal structures than other day-resolved returns. Shuffling the series reveals that multifractality arises from a broad probability density function and long-term correlations. The time-dependent multifractal analysis shows that the Monday returns’ multifractal spectra are much wider than those of other days. This behavior is especially persistent during financial crises. The presence of day-of-the-week effects in multifractal dynamics of market returns motivates further research on calendar anomalies for distinct market regimes.     

Understanding the Nature of the Long-Range Memory Phenomenon in Socioeconomic Systems

In the face of the upcoming 30th anniversary of econophysics, we review our contributions and other related works on the modeling of the long-range memory phenomenon in physical, economic, and other social complex systems. Our group has shown that the long-range memory phenomenon can be reproduced using various Markov processes, such as point processes, stochastic differential equations, and agent-based models—reproduced well enough to match other statistical properties of the financial markets, such as return and trading activity distributions and first-passage time distributions. Research has lead us to question whether the observed long-range memory is a result of the actual long-range memory process or just a consequence of the non-linearity of Markov processes. As our most recent result, we discuss the long-range memory of the order flow data in the financial markets and other social systems from the perspective of the fractional Lèvy stable motion. We test widely used long-range memory estimators on discrete fractional Lèvy stable motion represented by the auto-regressive fractionally integrated moving average (ARFIMA) sample series. Our newly obtained results seem to indicate that new estimators of self-similarity and long-range memory for analyzing systems with non-Gaussian distributions have to be developed.     

Replicating financial market dynamics with a simple self-organized critical lattice model

We explore a simple lattice field model intended to describe statistical properties of high-frequency financial markets. The model is relevant in the cross-disciplinary area of econophysics. Its signature feature is the emergence of a self-organized critical state. This implies scale invariance of the model, without tuning parameters. Prominent results of our simulation are time series of gains, prices, volatility, and gains frequency distributions, which all compare favorably to features of historical market data. Applying a standard GARCH(1,1) fit to the lattice model gives results that are almost indistinguishable from historical NASDAQ data.     

Multifractal detrended fluctuation analysis of derivative and spot markets

We investigate the multifractal properties of price increments in the cases of derivative and spot markets. Through the multifractal detrended fluctuation analysis, we estimate the generalized Hurst and the Renyi exponents for price fluctuations. By deriving the singularity spectrum from the above exponents, we quantify the multifractality of a financial time series and compare the multifractal properties of two different markets. The different behavior of each agent-group in transactions is also discussed. In order to identify the nature of the underlying multifractality, we apply the method of surrogate data to both sets of financial data. It is shown that multifractality due to a fat-tailed distribution is significant.