An ideal gas approach to classify countries using financial indices

Traditionally, countries’ development is classified based on several features that can be related to economic and social factors. However, this classification task is costly due to the difficulty of obtaining those features. We propose a method to classify countries based on financial indices using an ideal gas model. The probability density function (pdf) of the return series of the financial indices is used to characterize the fluctuation of a market. Based on the pdf, the volatility and the B$B$ coefficient, which describe the behavior of world markets, are estimated. The evaluation procedure uses 34 indices from developed and developing countries. The results show that the proposed method is an accurate, fast and low-cost computational alternative to the classifications provided by traditional organizations.     

Intraday volatility spillovers between spot and futures indices: Evidence from the Korean stock market

This study provides empirical evidence of the relationship between spot and futures markets in Korea. In particular, the study focuses on the volatility spillover relationship between spot and futures markets by using three high-frequency (10 min, 30 min, and 1 h time-scales) intraday data sets of KOSPI 200 spot and futures contracts. The results indicate a strong bi-directional causal relationship between futures and spot markets, suggesting that return volatility in the spot market can influence that in the futures market and vice versa. Thus, the results indicate that new information is reflected in futures and spot markets simultaneously. This bi-directional causal relationship provides market participants with important guidance on understanding the intraday information transmission between the two markets. Thus, on a given trading day, there may be sudden and sharp increases or decreases in return volatility in the Korean stock market as a result of positive feedback and synchronization of spot and futures markets.     

Probability distribution function and multiscaling properties in the Korean stock market

We consider the probability distribution function (pdf) and the multiscaling properties of the index and the traded volume in the Korean stock market. We observed the power law of the pdf at the fat tail region for the return, volatility, the traded volume, and changes of the traded volume. We also investigate the multifractality in the Korean stock market. We consider the multifractality by the detrended fluctuation analysis (MFDFA). We observed the multiscaling behaviors for index, return, traded volume, and the changes of the traded volume. We apply MFDFA method for the randomly shuffled time series to observe the effects of the autocorrelations. The multifractality is strongly originated from the long time correlations of the time series.     

Tail distribution of index fluctuations in World markets

We have investigated the tail distribution of the daily fluctuations in 202 different indices in the stock markets of 59 countries for the time span of the last 20 years. Power law, log-normal, Weibull, exponential and power law with exponential cutoff distributions are considered as possible candidates for the tail distribution of the normalized returns. It is found that the power exponent depends strongly on the choice of the tail threshold and a sizeable number of indices can be better fitted by a distribution function other than the power law at the region that has power law exponent of 3. Also, we have found that the power exponent is not an indicator of the maturity of the market.     

A multifractal approach for stock market inefficiency

In this paper, the multifractality degree in a collection of developed and emerging stock market indices is evaluated. Empirical results suggest that the multifractality degree can be used as a quantifier to characterize the stage of market development of world stock indices. We develop a model to test the relationship between the stage of market development and the multifractality degree and find robust evidence that the relationship is negative, i.e., higher multifractality is associated with a less developed market. Thus, an inefficiency ranking can be derived from multifractal analysis. Finally, a link with previous volatility time series results is established.     

Distribution of the Japanese posted land price and the generalized entropy

The land price can be considered as the economic data which reflects the interactive activity of markets (land market). Since the cumulative distributions often exhibit power law behavior in a number of complex systems, we attempted to investigate the posted land prices available in Japan for the most recent six years of the recession period after the bubble economy. They also seem to be in this category within a certain range. We observe that the generalized exponential function with one parameter which has been frequently used in generalized thermostatistics serving to track the annual change of the distributions.Received: 20 November 2003, Published online: 8 June 2004PACS: 05.90. + m Other topics in statistical physics, thermodynamics, and nonlinear dynamical systems - 89.90. + n Other topics in areas of applied and interdisciplinary physics - 89.75.Da Systems obeying scaling laws     

Idiosyncratic risk in the Dow Jones Eurostoxx50 Index

Recent evidence by Campbell et al. [J.Y. Campbell, M. Lettau B.G. Malkiel, Y. Xu, Have individual stocks become more volatile? An empirical exploration of idiosyncratic risk, The Journal of Finance (February) (2001)] shows an increase in firm-level volatility and a decline of the correlation among stock returns in the US. In relation to the Euro-Area stock markets, we find that both aggregate firm-level volatility and average stock market correlation have trended upwards.We estimate a linear model of the market risk–return relationship nested in an EGARCH(1, 1)-M model for conditional second moments. We then show that traditional estimates of the conditional risk–return relationship, that use ex-post excess-returns as the conditioning information set, lead to joint tests of the theoretical model (usually the ICAPM) and of the Efficient Market Hypothesis in its strong form.To overcome this problem we propose alternative measures of expected market risk based on implied volatility extracted from traded option prices and we discuss the conditions under which implied volatility depends solely on expected risk. We then regress market excess-returns on lagged market implied variance computed from implied market volatility to estimate the relationship between expected market excess-returns and expected market risk.We investigate whether, as predicted by the ICAPM, the expected market risk is the main factor in explaining the market risk premium and the latter is independent of aggregate idiosyncratic risk.     

A self-adapting herding model: The agent judge-abilities influence the dynamic behaviors

We propose a self-adapting herding model, in which the financial markets consist of agent clusters with different sizes and market desires. The ratio of successful exchange and merger depends on the volatility of the market and the market desires of the agent clusters. The desires are assigned in term of the wealth of the agent clusters when they merge. After an exchange, the beneficial cluster’s desire keeps on the same, the losing one’s desire is altered which is correlative with the agent judge-ability. A parameter R is given to all agents to denote the judge-ability. The numerical calculation shows that the dynamic behaviors of the market are influenced distinctly by R, which includes the exponential magnitudes of the probability distribution of sizes of the agent clusters and the volatility autocorrelation of the returns, the intensity and frequency of the volatility.     

A long-range memory stochastic model of the return in financial markets

We present a nonlinear stochastic differential equation (SDE) which mimics the probability density function (PDF) of the return and the power spectrum of the absolute return in financial markets. Absolute return as a measure of market volatility is considered in the proposed model as a long-range memory stochastic variable. The SDE is obtained from the analogy with an earlier proposed model of trading activity in the financial markets and generalized within the nonextensive statistical mechanics framework. The proposed stochastic model generates time series of the return with two power law statistics, i.e., the PDF and the power spectral density, reproducing the empirical data for the one-minute trading return in the NYSE.     

Correlation of financial markets in times of crisis

Using the eigenvalues and eigenvectors of correlations matrices of some of the main financial market indices in the world, we show that high volatility of markets is directly linked with strong correlations between them. This means that markets tend to behave as one during great crashes. In order to do so, we investigate financial market crises that occurred in the years 1987 (Black Monday), 1998 (Russian crisis), 2001 (Burst of the dot-com bubble and September 11), and 2008 (Subprime Mortgage Crisis), which mark some of the largest downturns of financial markets in the last three decades.     

Herding and zero-intelligence agents in the order book dynamics of an artificial double auction market

Effects of herding on the order book dynamics of a double auction market is studied by an agent-based model. This is done by comparing results from a zero-intelligence model and a model in which herding effect is implemented by aggregation of agents who take market orders into opinion groups. The number of opinion groups in a simulation step is determined from previous volatilities of the market as different agents compare the price change over different time intervals. Besides confirming that when herding is included the tail of the distribution of volatility is enhanced, we found several new results. First, the autocorrelation time of volatility is much shorter than the memory of most of the agents because limit orders have strong influence on the location of best bid and best ask. Second, from the relation between bid-ask imbalance and price return we find that herding reduces the chance for a small imbalance to produce a large price change. Furthermore, herding tends to decrease spread. This is because herding decreases the chance that a market order changes the size of the spread. Finally, we find that the relation between spread and volatility in our models does not agree with empirical data, this indicates a difference between agents with no strategies and agents in real financial markets.     

Intraday volatility and network topological properties in the Korean stock market

We examine whether the relationship between market volatility and network properties in the low-frequency level can be applied to the high-frequency level. For the analysis, we use the minimum spanning tree (MST) method constructed from intraday Korean stock market data. The results show that the higher the market volatility is, the denser the MST of stocks becomes. The normalized tree length shows a strong negative relationship with market volatility, indicating that the distances between nodes are shorter when the market volatility is high. The mean occupation layer shows the tendency of having a smaller value in a higher volatility market. The maximum number of links becomes larger when the market volatility increases. All these network properties support the network being dense and shrinking in high market volatility conditions; that is, the degree of co-movement in financial market is reinforced in the intraday high-frequency level.     

Continuous Time Random Walk with Correlated Waiting Times. The Crucial Role of Inter-Trade Times in Volatility Clustering

In many physical, social, and economic phenomena, we observe changes in a studied quantity only in discrete, irregularly distributed points in time. The stochastic process usually applied to describe this kind of variable is the continuous-time random walk (CTRW). Despite the popularity of these types of stochastic processes and strong empirical motivation, models with a long-term memory within the sequence of time intervals between observations are rare in the physics literature. Here, we fill this gap by introducing a new family of CTRWs. The memory is introduced to the model by assuming that many consecutive time intervals can be the same. Surprisingly, in this process we can observe a slowly decaying nonlinear autocorrelation function without a fat-tailed distribution of time intervals. Our model, applied to high-frequency stock market data, can successfully describe the slope of decay of the nonlinear autocorrelation function of stock market returns. We achieve this result without imposing any dependence between consecutive price changes. This proves the crucial role of inter-event times in the volatility clustering phenomenon observed in all stock markets.     

K9和熔石英玻璃纳秒基频激光损伤特性的实验对比研究

利用光学元件基频激光损伤测试平台,通过实验测试相同条件下K9和熔石英两类常用 光学元件的初始损伤阈值、损伤增长阈值和损伤增长规律,对比研究了两类光学元件的基频激光损伤特性.结果表明,K9和熔石英光学元件的初始损伤阈值基本相同,损伤面积增长都遵循指数性增长规律,损伤深度成线性增长.但两者损伤增长特性仍有很大的差别,与熔石英相比,K9激光损伤增长阈值较低,并且相同通量下的激光损伤增长更为迅速,通过两类光学材料抗压性能的巨大差异很好地解释了这一现象.该研究结果对国内高功率激光装置的透射光学材料工程应用有非常重要的参考价值.     

Multiscaling and clustering of volatility

The dynamics of prices in stock markets has been studied intensively both experimentally (data analysis) and theoretically (models). Nevertheless, while the distribution of returns of the most important indices is known to be a truncated Lévy, the behaviour of volatility correlations is still poorly understood. What is well known is that absolute returns have memory on a long time range, this phenomenon is known in financial literature as clustering of volatility. In this paper we show that volatility correlations are power laws with a non-unique scaling exponent. This kind of multiscale phenomenology is known to be relevant in fully developed turbulence and in disordered systems and it is pointed out here for the first time for a financial series. In our study we consider the New York Stock Exchange (NYSE) daily index, from January 1966 to June 1998, for a total of 8180 working days.     

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.     

A contribution to the systematics of stochastic volatility models

We systematically compare several classes of stochastic volatility models of stock market fluctuations. We show that the long-time return distribution is either Gaussian or develops a power-law tail, while the short-time return distribution has generically a stretched-exponential form, but can also assume an algebraic decay, in the family of models which we call “GARCH” type. The intermediate regime is found in the exponential Ornstein-Uhlenbeck process. We also calculate the decay of the autocorrelation function of volatility.     

Long memory volatility in Chinese stock markets

In this study, the long memory property in the volatility of Chinese stock markets is examined. For this purpose, we applied two semi-parametric tests (GPH and LW) and the FIGARCH model, to four Chinese market indices: Shanghai A, Shanghai B, Shenzhen A and Shenzhen B. From the results of our analysis, we can conclude that the volatility of Chinese stock markets exhibits long memory features, and that the assumption of non-normality provides better specifications regarding long memory volatility processes.     

Modeling financial crisis period: A volatility perspective of Credit Default Swap market

The volatility of financial markets is often assumed constant, but phenomena such as volatility clustering and jumps in volatility suggest that this assumption is rarely true. Numerous studies have been conducted to investigate the jump or breakpoint of the volatility phenomenon, and their findings have been applied in modeling volatility. However, few studies address the issue from a practical point of view. Specifically, a financial crisis accompanied by markedly increased volatility can be approached from this perspective to suggest the persistence or termination of a crisis. This paper develops the ICSS-CRISIS algorithm, a new approach to identify a crisis period along with the conditions for the ICSS algorithm which represents the structural breakpoints of volatility. This algorithm recommends a guideline to determine whether an existing crisis in the market resulted from financial volatility, was terminated, or is continuing. The method is tested along with the ICSS algorithm to prove the effectiveness of Credit Default Swap index data.