发布于:2018-03-08 星期四 22:19:37 点击数:798




        王纲金,管理学博士,现任湖南大学工商管理学院副教授、硕士生导师、岳麓学者晨星岗A,美国波士顿大学博士后,主要从事金融工程与风险管理、复杂金融系统、金融物理学、复杂金融网络、系统性金融风险等领域研究。目前在Quantitative Finance, International Review of Financial Analysis, Journal of International Financial Markets, Institutions & Money, International Review of Economics and Finance, Emerging Markets Review, Finance Research Letters, Journal of Economic Interaction and Coordination, Computational Economics, Expert Systems with Applications, Physica A: Statistical Mechanics and its Applications等SSCI/SCI收录期刊上发表学术论文20余篇,主持和参与国家自然科学基金、省部级项目10余项。具体包括:主持承担国家自然科学基金面上基金(No.71871088)“基于多层信息溢出网络的金融机构关联性与系统性风险研究”、国家自然科学基金青年基金(No.71501066)“金融市场尾部相关性网络的建模及其演化与稳定性研究”等课题;参与国家自然科学基金面上项目(No.71373072)“复杂金融网络动态演化行为与危机传染及其控制研究”、国家自然科学基金面上项目(No.71573077)“渐进开放市场中资产所有权的异质跨境整合效应及风险分散策略”、高等学校博士学科点专项科研基金(No.20130161110031)“藕合实体经济的金融市场风险评估与协同监管研究”等课题。 



       2011.09~2014.12  湖南大学  管理科学与工程  博士

       2008.09~2011.06  湖南大学 计算机科学与技术  硕士

       2004.09~2008.07  河南理工大学 数学与应用数学  学士


        2017.12~              湖南大学工商管理学院 副教授

        2015.09~2017.08  美国波士顿大学 博士后

        2015.01~2017.12  湖南大学工商管理学院 助理教授        











Shuyu Yi, Zishuang Xu, Gang-Jin Wang*. Volatility connectedness in the cryptocurrency market: Is Bitcoin a dominant cryptocurrency?,International Review of Financial Analysis,  2018,  60: 98-114. [SSCI] [Link]

Abstract: Using the spillover index approach and its variants, we examine both static and dynamic volatility connectedness among eight typical cryptocurrencies. The results reveal that their connectedness fluctuates cyclically and has shown an obvious rise trend since the end of 2016. In the variance decomposition framework, we further construct a volatility connectedness network linking 52 cryptocurrencies using the LASSO-VAR for estimating high-dimensional VARs. We find that these 52 cryptocurrencies are tightly interconnected and “mega-cap” cryptocurrencies are more likely to propagate volatility shocks to others. However, some unnoticeable cryptocurrencies (e.g., Maidsafe Coin) are also significant net-transmitters of volatility connectedness and even have larger contribution of volatility spillovers to others.

     Gang-Jin Wang*, Chi Xie, Longfeng Zhao, Zhi-Qiang Jiang*. Volatility connectedness in the Chinese banking system: Do state-owned commercial banks contribute more?, Journal of International Financial Markets, Institutions & Money, 2018, 60: 98-114. [SSCI] [Link] 

Abstract: Using the volatility spillover network of Diebold and Yilmaz (2014), we investigate volatility connectedness in the Chinese banking system based on daily range-based volatility series of 14 publicly-traded commercial banks from 2008 to 2016. Both static and dynamic total connectedness show that the 14 commercial banks are highly interconnected. Total directional connectedness (including from-connectedness, to-connectedness and net-connectedness) shows that state-owned commercial banks contribute less to volatility connectedness than joint-stock and city commercial banks, and that city commercial banks are the largest (net-) emitters of volatility connectedness. Statically, we find a positive (negative) rank correlation between size and from-connectedness (to-connectedness and net-connectedness) of banks. Dynamically, however, the positive rank correlation loses its statistical significance and the negative rank correlation disappears completely during the recent global financial crisis and “the 2015–2016 Chinese stock market turbulence.” Our findings suggest (i) that a bank might be “too big to fail,” but not necessarily “too interconnected to fail” and vice versa, and (ii) that these two cases may coexist conditional on the system being in distress.

    Gang-Jin Wang*, Zhi-Qiang Jiang, Min Lin, Chi Xie*, H. Eugene Stanley. Interconnectedness and systemic risk of China’s financial institutions. Emerging Markets Review, 2018, 35: 1-18. (Lead article) [SSCI]  [PDF] [Link]

Abstract: We investigate the interconnectedness and systemic risk of China’s financial institutions by constructing dynamic tail-event driven networks (TENETs) at 1% risk level based on weekly returns of 24 publicly-listed financial institutions from 2008 to 2016. Total connectedness reaches a peak when the system exhibits stress, especially during the recent period from mid-2014 to end-2016. Large commercial banks and insurers usually exhibit systemic importance, but some small firms are systemically important due to their high level of incoming (outgoing) connectedness. Our results provide useful information to regulators when they assess systemic risk of financial institutions and formulate macroprudential supervision policy.

    Gang-Jin Wang*, Chi Xie, H. Eugene Stanley. Correlation structure and evolution of world stock markets: Evidence from Pearson and partial correlation-based networks. Computational Economics, 2018, 51(3): 607-635.  [SSCI/SCI] [ESI Economics and Business 2018年7月高被引论文]   [PDF] [Link2]  

Abstract: We construct a Pearson correlation-based network and a partial correlation-based network, i.e., two minimum spanning trees (MST-Pearson and MST-Partial), to analyze the correlation structure and evolution of world stock markets. We propose a new method for constructing the MST-Partial. We use daily price indices of 57 stock markets from 2005 to 2014 and find (i) that the distributions of the Pearson correlation coefficient and the partial correlation coefficient differ completely, which implies that the correlation between pairs of stock markets is greatly affected by other markets, and (ii) that both MSTs are scale-free networks and that the MST-Pearson network is more compact than the MST-Partial. Depending on the geographical locations of the stock markets, two large clusters (i.e., European and Asia-Pacific) are formed in the MST-Pearson, but in the MST-Partial the European cluster splits into two subgroups bridged by the American cluster with the USA at its center. We also find (iii) that the centrality structure indicates that outcomes obtained from the MST-Partial are more reasonable and useful than those from the MST-Pearson, e.g., in the MST-Partial, markets of the USA, Germany, and Japan clearly serve as hubs or connectors in world stock markets, (iv) that during the 2008 financial crisis the time-varying topological measures of the two MSTs formed a valley, implying that during a crisis stock markets are tightly correlated and information (e.g., about price fluctuations) is transmitted quickly, and (v) that the presence of multi-step survival ratios indicates that network stability decreases as step length increases. From these findings we conclude that the MST-Partial is an effective new tool for use by international investors and hedge-fund operators.

Min Lin, Gang-Jin Wang*, Chi Xie, H. Eugene Stanley. Cross-correlations and influence in world gold markets. Physica A, 2018, 490: 504-512.  [SCI/SSCI]  [PDF] [Link]

 Abstract: Using the detrended cross-correlation analysis (DCCA) coefficient and the detrended partial cross-correlation analysis (DPCCA) coefficient, we investigate cross-correlations and net cross-correlations among five major world gold markets (London, New York, Shanghai, Tokyo, and Mumbai) at different time scales. We propose multiscale influence measures for examining the influence of individual markets on other markets and on the entire system. We find (i) that the cross-correlations, net cross-correlations, and net influences among the five gold markets vary across time scales, (ii) that the cross-market correlation between London and New York at each time scale is intense and inherent, meaning that the influence of other gold markets on the London–New York market is negligible, (iii) that the remaining cross-market correlations (i.e., those other than London–New York) are greatly affected by other gold markets, and (iv) that the London gold market significantly affects the other four gold markets and dominates the world-wide gold market. Our multiscale findings give market participants and market regulators new information on cross-market linkages in the world-wide gold market.

Gang-Jin Wang*, Chi Xie, Kaijian He*, H. Eugene Stanley. Extreme risk spillover network: Application to financial institutions. Quantitative Finance, 2017, 17(9): 1417-1433.  [SSCI/SCI] [PDF] [Link]

Abstract: Using the CAViaR tool to estimate the value-at-risk (VaR) and the Granger causality risk test to quantify extreme risk spillovers, we propose an extreme risk spillover network for analyzing the interconnectedness across financial institutions. We construct extreme risk spillover networks at 1% and 5% risk levels (which we denote 1% and 5% VaR networks) based on the daily returns of 84 publicly listed financial institutions from four sectors—banks, diversified financials, insurance, and real estate—during the period 2006–2015. We find that extreme risk spillover networks have a time-lag effect. Both the static and dynamic networks show that on average the real estate and bank sectors are net senders of extreme risk spillovers and the insurance and diversified financials sectors are net recipients, which coheres with the evidence from the recent global financial crisis. The networks during the 2008–2009 financial crisis and the European sovereign debt crisis exhibited distinctive topological features that differed from those in tranquil periods. Our approach supplies new information on the interconnectedness across financial agents that will prove valuable not only to investors and hedge fund managers, but also to regulators and policy-makers.

Gang-Jin Wang, Chi Xie*, Shou Chen. Multiscale correlation networks analysis of the US stock market: A wavelet analysis. Journal of Economic Interaction and Coordination, 2017, 12(3): 561-594.  [SSCI]  [Link]

Abstract: We investigate the interaction among stocks in the US market over various time horizons from a network perspective. Unlike the high-frequency data-driven multiscale correlation networks used in previous works, we propose method-driven multiscale correlation networks that are constructed by wavelet analysis and topological methods of minimum spanning tree (MST) and planar maximally filtered graph (PMFG). Using these techniques, we construct MST and PMFG networks of the US stock market at different time scales. The key empirical results show that (1) the topological structures and properties of networks vary across time horizons, (2) there is a sectoral clustering effect in the networks at small time scales, and (3) only a part of connections in the networks survives from one time scale to the next. Our results in terms of MSTs and PMFGs for different time scales supply a new perspective for participants in financial markets, especially for investors or hedgers who have different investment or hedging horizons.

Gang-Jin Wang*, Chi Xie, Min Lin, H. Eugene Stanley. Stock market contagion during the global financial crisis: A multiscale approach. Finance Research Letters, 2017, 22: 163-168.   [SSCI]  [PDF] [Link]

Abstract: We propose a multiscale correlation contagion statistic to test for stock market contagion during the global financial crisis (GFC) from the US to the other six G7 and BRIC countries. We find that cross-market correlations between the US and selected countries are conditional on the time scale. Stock market contagion during the GFC is dependent on both the recipient country and the time scale, e.g., contagion from the US to Japan, China, and Brazil occurs when the time scale is longer than 50 days or more. Our findings are important to international investors when they make decisions about global portfolio diversification.

      Gang-Jin Wang*, Chi Xie, Zhi-Qiang Jiang, H. Eugene Stanley. Extreme risk spillover effects in world gold markets and the global financial crisis. International Review of Economics and Finance, 2016, 46: 55-77 (SSCI, Business, Finance或Economics: JCR Q1) [PDF] [Link]

Abstract: Using the approach of Granger causality in risk, we investigate extreme risk spillover effects among four major world gold markets (London, New York, Tokyo and Shanghai) before and after the recent global financial crisis. We find (i) that there are strong extreme risk spillover effects between London and New York, and London and Shanghai, (ii) that most of the extreme risk spillovers to Tokyo and Shanghai are from New York rather than from London, but London leads New York in risk spillovers, (iii) that extreme risk spillover effects from Tokyo and Shanghai to New York are limited, but those to London play an important role, and (iv) that extreme risk spillover effects between Tokyo and Shanghai are weak or negligible. We also find that extreme risk is more quickly transmitted in the post-crisis era than in the pre-crisis era, an effect that is related to the safe-haven or risk hedging property or the speculative value of gold.

简介:黄金是一种极为特殊的商品,同时兼有商品、货币、金融三种属性。采用风险Granger因果检验方法,研究了全球四大黄金市场(伦敦、纽约、东京、上海)在全球金融危机前后的风险溢出效应。实证发现:(i) 伦敦与纽约、伦敦与上海市场之间存在显著的风险溢出效应;(ii) 东京与上海市场的风险溢出主要来源于纽约市场而不是伦敦市场,但是伦敦市场是纽约市场的风险溢出来源;(iii) 东京与上海市场对纽约市场的风险溢出效应较为有限,但是它们对伦敦市场具有一定的影响;(iv) 东京与上海市场间的风险溢出效应较弱或不显著;(v) 极端风险在危机后比在危机前传递更为迅速,体现了黄金在金融危机时期的避险、保值、投机的功能。

     Gang-Jin Wang*, Chi Xie, Zhi-Qiang Jiang, H. Eugene Stanley. Who are the net senders and recipients of volatility spillovers in China’s financial markets? Finance Research Letters, 2016, 18: 255-262 [SSCI, Business, Finance: JCR Q3] [PDF] [Link]

Abstract: Using a spillover index approach, we investigate volatility spillovers across China’s stock, bond, commodity futures, and foreign exchange (FX) markets and their evolution during the period 2005–2015. We find that these four financial markets are weakly integrated. The stock market is the largest net sender of volatility spillovers to other markets, followed by the bond market, and the FX and commodity futures markets are net recipients. The time-varying volatility spillovers show that the recent global financial crisis and the European sovereign debt crisis strongly influenced China’s financial markets.


      Gang-Jin Wang*, Chi Xie. Tail dependence structure of the foreign exchange market: A network view. Expert Systems with Applications, 2016, 46: 164-179. [SCI/SSCI, Operations Research & Management Science: JCR Q1][Link]

Abstract: Tail dependence of financial entities describes when the price of one financial asset has an extreme fluctuation (e.g., price sharply rises or falls), the degree of its effect on the price fluctuation of another asset. Under the background of the global financial crisis, tail dependence structure of financial entities plays an important role in financial risk management, portfolio selection, and asset pricing. In this paper, we propose a concept of tail dependence networks to investigate the tail dependence structure of the foreign exchange (FX) market. Lower- and upper-tail dependence networks for 42major currencies in the FX market from 2005 to 2012 are constructed by combing the symmetrized Joe-Clayton copula model and two filtered graph algorithms, i.e., the minimum spanning tree (MST) and the planar maximally filtered graph (PMFG).We also construct the tail dependence hierarchical trees (HTs) associated with the MSTs to analyze the currency clusters. We find that (1) the two series of lower- and upper-tail dependence coefficients present different statistical properties; (2) the upper-tail dependence networks are tighter than the lower-tail dependence networks; and (3) different currency clusters, cliques and communities are respectively found in the two tail dependence networks. The key empirical results indicate that market participants should consider the different topological features at different market situations (e.g., a booming market or a recession market) to make decisions on the investing or hedging strategies. Overall, our obtained results based on the tail dependence networks are new insights in financial management and supply a novel analytical tool for market participants.

简介:金融资产的尾部相关性可以较好地描述极端事件发生时资产间的相互作用。上尾相关是指极度乐观或剧烈上涨时期资产间的相关性,而下尾相关是指极度悲观或剧烈下跌时期资产间的相关性。本文提出了尾部相关性网络以研究外汇市场尾部相关性结构。基于外汇市场42个主要货币在2005年到2012年间的汇率数据,采用SJC Copula法和两个滤波图算法(即最小生成树与平面最大限度滤波图法)构建了外汇市场上、下尾相关性网络。实证发现:上尾相关性网络相对于下尾相关性网络更为紧密,两个尾部相关性网络具有不同的货币聚类、派系与社团结构。因此,市场参与者应根据不同的市场环境选择不同的尾部相关性网络进行投资或对冲决策。

   Gang-Jin Wang*, Chi Xie. Correlation structure and dynamics of international real estate securities markets: A network perspective. Physica A: Statistical Mechanics and its Applications, 2015, 424: 176-193. [SCI/SSCI, Grade two by ABS 2015, JCR Q2][Link] 

Abstract: In this paper, we investigate the correlation structure and dynamics of international real estate securities markets by using daily returns of 20 national markets during the period 2006–2012 from a network perspective. We construct the minimum spanning tree (MST), the hierarchical tree (HT), and the planar maximally filtered graph (PMFG) obtained from the correlation matrix computed by the daily returns during the investigated period, and analyze the corresponding clustering structure, hierarchical structure, and community structure. We also build the time-varying MST and PMFG networks by a rolling window to examine the dynamics of correlation structure. The empirical results show that (1) the distribution of correlation coefficients is asymmetric, fat-tailed, and non-Gaussian. (2) The distributions of the influence-strength of the MST and PMFG networks obey a power-law. (3) Two clusters (i.e., the European and Asia-Pacific clusters) are found in the MST network, three hierarchical clusters (i.e., two like in the MST and the North American cluster) in the HT, and three communities in the PMFG network, which shows that national markets are linked together according to their geographical distributions. (4) The descriptive statistics of correlation coefficients and distances of the MSTs and PMFGs are time-varying; especially during periods of crisis they have a large fluctuation. (5) A huge number of linkages between national markets survive from one time to the next, and the long-term stability of the correlation structure in international real estate securities markets descends as time goes on. Our obtained results are new insights in international real estate securities markets and have wide applications for investment portfolio and risk management.

简介:近年来我国房地产市场发展势头迅猛,房地产价格不断上涨,对我国经济发展产生了一定影响,那么全球房地产市场是怎样的情况呢?它们具有怎么样的联动性呢?本文从复杂网络的视角研究了全球20个房地产市场的相关性结构及其动态性。分别通过构建最小生成树(MST)、层次树(HT)、平面最大限度滤波图(PMFG)研究了货币的聚类结构、层次结构与社团结构。实证发现:(1) MST和PMFG网络的影响强度呈现幂律分布;(2) 在网络中,市场间相关性结构呈现地理分布的聚类效应,如MST网络中发现了欧洲与亚太聚类,在HT和PMFG还发现了北美聚类;(3) 网络呈现时变的动态特征,相邻两个网络的边具有很大的相似性,而网络的长期稳定性却随时间的推移而下降。

    Chi Xie, Zhou Mao, Gang-Jin Wang*. Forecasting RMB exchange rate based on a nonlinear combination model of ARFIMA, SVM, and BPNN. Mathematical Problems in Engineering, 2015, Article ID 635345, 10 pages. [SCI/SSCI, JCR Q3]

Abstract: There are various models to predict financial time series like the RMB exchange rate. In this paper, considering the complex characteristics of RMB exchange rate, we build a nonlinear combination model of the autoregressive fractionally integrated moving average (ARFIMA) model, the support vector machine (SVM) model, and the back-propagation neural network (BPNN) model to forecast the RMB exchange rate. The basic idea of the nonlinear combination model (NCM) is to make the prediction more effective by combining different models’ advantages, and the weight of the combination model is determined by a nonlinear weighted mechanism. The RMB exchange rate against US dollar (RMB/USD) and the RMB exchange rate against Euro (RMB/EUR) are used as the empirical examples to evaluate the performance of NCM. The results show that the prediction performance of the nonlinear combination model is better than the single models and the linear combination models, and the nonlinear combination model is suitable for the prediction of the special time series, such as the RMB exchange rate.


      Gang-Jin Wang, Chi Xie*, Ling-Yun He, Shou Chen. Detrended minimum-variance hedge ratio: A new method for hedge ratio at different time scales. Physica A: Statistical Mechanics and its Applications, 2014, 405: 70-79. [SCI/SSCI, Grade two by ABS 2015, JCR Q2]

Abstract: In this paper, based on the detrended fluctuation analysis (DFA) method and the detrended cross-correlation analysis (DCCA) method, we propose an improved method of minimumvariance (MV) hedge ratio, i.e., the detrended minimum-variance (D-MV) hedge ratio, which can measure the hedge ratio at different time scales. The proposed D-MV hedge ratio is defined as the detrended covariance function between spot and futures returns divided by the detrended variance function of futures returns. Through the simulated and empirical analysis, we find that (i) the outcomes of the hedge ratio and the corresponding hedging effectiveness for the D-MV hedge ratio are diverse at different time scales, which can meet needs of various hedging participants with different hedging horizons; (ii) our proposed D-MV hedge ratio has a better hedging performance and a greater potential to determine the hedge ratio because its results of hedging effectiveness at most of time scales are better than those of the traditional MV hedge ratio; and (iii) as for the method of D-MV hedge ratio for different polynomial orders m in the fitting procedure, the D-MV-1 hedge ratio (i.e., the linear polynomial in the fitting procedure) has the best hedging capability for determining the hedge ratio.

简介:风险转移是期货市场最重要的功能之一,它主要通过套期保值策略来实现,而套期保值理论中最核心的问题是如何设定最优套期保值比率。本文提出了降趋势最小方差(D-MV)套期保值比率方法,可以在不同时间尺度下设定套期保值比率,它定义为现货与期货收益率的降趋势协方差函数与期货收益率的降趋势方差函数的比值。通过模拟与实证分析发现:(1) D-MV套期保值方法所设定的套期保值比率与有效性在不同时间尺度下具有不同的值,从而能满足不同套期保值者不同的套期保值周期需求;(2) D-MV套期保值方法的套期保值性能在绝大多数时间尺度下要优于MV套期保值方法;(3) D-MV-1套期保值方法具有最优的套期保值性能。

     Gang-Jin Wang, Chi Xie*, Peng Zhang, Feng Han, Shou Chen. Dynamics of foreign exchange networks: A time-varying copula approach. Discrete Dynamics in Nature and Society, 2014, Article ID 170921, 11 pages. [SCI/SSCI, JCR Q3]

Abstract: Based on a time-varying copula approach and the minimum spanning tree (MST) method, we propose a time-varying correlation network-based approach to investigate dynamics of foreign exchange (FX) networks. In piratical terms, we choose the daily FX rates of 42 major currencies in the international FX market during the period of 2005–2012 as the empirical data. The empirical results show that (i) the distributions of cross-correlation coefficients (distances) in the international FX market (network) are fat-tailed and negatively skewed; (ii) financial crises during the analyzed period have a great effect on the FX network’s topology structure and lead to the US dollar becoming more centered in the MST; (iii) the topological measures of the FX network show a large fluctuation and display long-range correlations; (iv) the FX network has a long-term memory effect and presents a scale-free behavior in the most of time; and (v) a great majority of links between currencies in the international FX market survive from one time to the next, and multistep survive rates of FX networks drop sharply as the time increases.


   Chi Xie, Jiao-Jiao Yang, Gang-Jin Wang*. A new method for setting futures portfolios’ maintenance margins: Evidence from Chinese commodity futures markets. Journal of Applied Mathematics, 2014, Article ID 325975, 11 pages. [SCI/SSCI]

Abstract: The Chinese commodity futures markets neglect the existence of the risk hedge and diversification between futures contracts, thus leading to overcharge futures portfolio holders’ maintenance margins. To this end, this paper proposes a new method, namely, the multivariate t-Copula-POT-PSRM method, which combines three models, that is, the multivariate t-Copula, the peaks over threshold (POT), and the power spectral risk measures (PSRM), to set futures portfolios’ maintenance margins. In the empirical analysis, we first construct four kinds of futures portfolios and set their maintenance margins by using the new method. Then, we introduce two evaluation indicators, namely, the prudence index (PI) and the opportunity cost index (OCI), to assess the effectiveness of the proposed method. We also compare the outcomes of the two evaluation indicators of the new method with those of the widely used linear additive model. The empirical results show that the new method can, respectively, lower the OCI value of all four kinds of futures portfolios for the In-sample period and the Out-of-sample period without significantly reducing the PI value as against the traditional model, which implies that the proposed method can be used to balance security and investment efficiency in the futures market.


   Gang-Jin Wang, Chi Xie*, Shou Chen, Feng Han. Cross-correlations between energy and emissions markets: New evidence from fractal and multifractal analysis. Mathematical Problems in Engineering, 2014, Article ID 197069, 13 pages. [SCI/SSCI, JCR Q3]

Abstract: We supply a new perspective to describe and understand the behavior of cross-correlations between energy and emissions markets. Namely, we investigate cross-correlations between oil and gas (Oil-Gas), oil and CO2 (Oil-CO2), and gas and (Gas-CO2) based on fractal and multifractal analysis. We focus our study on returns of the oil, gas, and during the period of April 22, 2005–April 30, 2013. In the empirical analysis, by using the detrended cross-correlation analysis (DCCA) method, we find that cross-correlations for Oil-Gas, Oil-CO2, and Gas-CO2 obey a power-law and are weakly persistent. Then, we adopt the method of DCCA cross-correlation coefficient to quantify cross-correlations between energy and emissions markets. The results show that their cross-correlations are diverse at different time scales. Next, based on the multifractal DCCA method, we find that cross-correlated markets have the nonlinear and multifractal nature and that the multifractality strength for three cross-correlated markets is arranged in the order of Gas-CO2 > Oil-Gas > Oil-CO2. Finally, by employing the rolling windows method, which can be used to investigate time-varying cross-correlation scaling exponents, we analyze short-term and long-term market dynamics and find that the recent global financial crisis has a notable influence on short-term and long-term market dynamics.


     Gang-Jin Wang, Chi Xie*, Shou Chen, Jiao-Jiao Yang, Ming-Yan Yang. Random matrix theory analysis of cross-correlations in the US stock market: Evidence from Pearson’s correlation coefficient and detrended cross-correlation coefficient. Physica A: Statistical Mechanics and its Applications, 2013, 392(17): 3715-3730. [SCI, Grade two by ABS 2015, JCR Q2]

Abstract: In this study, we first build two empirical cross-correlation matrices in the US stock market by two different methods, namely the Pearson’s correlation coefficient and the detrended cross-correlation coefficient (DCCA coefficient). Then, combining the two matrices with the method of random matrix theory (RMT), we mainly investigate the statistical properties of cross-correlations in the US stock market. We choose the daily closing prices of 462 constituent stocks of S&P 500 index as the research objects and select the sample data from January 3, 2005 to August 31, 2012. In the empirical analysis, we examine the statistical properties of cross-correlation coefficients, the distribution of eigenvalues, the distribution of eigenvector components, and the inverse participation ratio. From the two methods, we find some new results of the cross-correlations in the US stock market in our study, which are different from the conclusions reached by previous studies. The empirical cross-correlation matrices constructed by the DCCA coefficient show several interesting properties at different time scales in the US stock market, which are useful to the risk management and optimal portfolio selection, especially to the diversity of the asset portfolio. It will be an interesting and meaningful work to find the theoretical eigenvalue distribution of a completely random matrix R for the DCCA coefficient because it does not obey the Marčenko–Pastur distribution.

简介:金融变量或资产之间的交互作用,表现为金融变量或资产收益间的协方差或互相关矩阵,而互相关矩阵决定了资产投资组合策略的选择以及资产风险的管理。如何有效挖掘并利用协方差矩阵或互相关矩阵中的有效信息,已经成为金融市场与金融工程研究领域以及业界实践的难点与热点问题。随机矩阵理论(RMT)为此提供了一个有效的工具,可以用来分析互相关矩阵的统计性质并检验与过滤互相关矩阵中的随机噪声。本文提出了结合DCCA系数法与随机矩阵理论的方法,以研究不同时间尺度下美国股票市场内部的互相关性及其统计性质。同时与Pearson相关系数法进行对比分析。实证发现:(1) 最大特征值及其特征值向量携带了整个市场的信息,而市场因素反过来又影响特征值的分布。此外,落在RMT预测范围内的实证特征值并不是单纯的随机噪声而携带了某些信息;(2) 最大特征向量所构建的资产组合能很好的还原出市场指数中各成分股的权重;(3) 不同时间尺度下的互相关矩阵呈现出不同的统计性质,这将有利于资产的风险管理以及最优资产组合的选择,特别是资产组合的多样性。

    Gang-Jin Wang, Chi Xie*. Cross-correlations between the CSI 300 spot and futures markets. Nonlinear Dynamics, 2013, 73(3): 1687-1696. [SCI, JCR Q1]

Abstract: Financial markets are complex dynamical systems. One of the important features of market dynamics is the existence of cross-correlations between financial variables. Based on the high-frequency transaction prices (every 5 min) data, in this study, we investigate the cross-correlations between China Securities Index 300 (CSI 300) spot and futures markets. Qualitatively, employing a statistical test in analogy to the Ljung-Box test, we find that the cross-correlations are significant at the 1 % level. Quantitatively, using the multifractal detrending moving-average cross-correlation analysis (MF-XDMA) method, we find that the cross-correlations are strongly multifractal. An interesting finding is that the cross-correlation exponent is larger than the averaged generalized scaling exponent for different q, which is different from the general conclusion. Using the method of rolling windows, we find that the cross-correlations are positive over time, which suggests that China’s securities markets are not mature and efficient markets at present.


    Gang-Jin Wang, Chi Xie*, Yi-Jun Chen, Shou Chen. Statistical properties of the foreign exchange network at different time scales: Evidence from detrended cross-correlation coefficient and minimum spanning tree. Entropy, 2013, 15(5): 1643-1662. [SCI, JCR Q2]

Abstract: We investigate the statistical properties of the foreign exchange (FX) network at different time scales by two approaches, namely the methods of detrended cross-correlation coefficient (DCCA coefficient) and minimum spanning tree (MST). The daily FX rates of 44 major currencies in the period of 2007-2012 are chosen as the empirical data. Based on the analysis of statistical properties of cross-correlation coefficients, we find that the cross-correlation coefficients of the FX market are fat-tailed. By examining three MSTs at three special time scales (i.e., the minimum, medium, and maximum scales), we come to some conclusions: USD and EUR are confirmed as the predominant world currencies; the Middle East cluster is very stable while the Asian cluster and the Latin America cluster are not stable in the MSTs; the Commonwealth cluster is also found in the MSTs. By studying four evaluation criteria, we find that the MSTs of the FX market present diverse topological and statistical properties at different time scales. The scale-free behavior is observed in the FX network at most of time scales. We also find that most of links in the FX network survive from one time scale to the next.


     Gang-Jin Wang, Chi Xie*. Cross-correlations between Renminbi and four major currencies in the Renminbi currency basket. Physica A: Statistical Mechanics and its Applications, 2013, 392(6): 1418-1428. [SCI/SSCI, Grade two by ABS 2015, JCR Q2]

Abstract: We investigate the cross-correlations between Renminbi (CNY) and four major currencies (USD, EUR, JPY, and KRW) in the Renminbi currency basket, i.e., the cross-correlations of CNY–USD, CNY–EUR, CNY–JPY, and CNY–KRW. Qualitatively, using a statistical test in analogy to the Ljung-Box test, we find that cross-correlations significantly exist in CNY–USD, CNY–EUR, CNY–JPY, and CNY–KRW. Quantitatively, employing the detrended cross-correlation analysis (DCCA) method, we find that the cross-correlations of CNY–USD, CNY–EUR, CNY–JPY, and CNY–KRW are weakly persistent. We use the DCCA cross-correlation coefficient ρDCCA to quantify the level of cross-correlations and find the currency weight in the Renminbi currency basket is arranged in the order of USD>EUR>JPY >KRW. Using the method of rolling windows, which can capture the time-varying cross-correlation scaling exponents, we find that: (i) CNY and USD are positively cross-correlated over time, but the cross-correlations of CNY–USD are anti-persistent during the US sub-prime crisis and the European debt crisis. (ii) The cross-correlation scaling exponents of CNY-EUR have the cyclical fluctuation with a nearly two-year cycle. (iii) CNY–JPY has long-term negative cross-correlations, during the European debt crisis, but CNY and KRW are positively cross-correlated.

简介:研究了人民币及其货币篮子中4个主要货币(即美元、欧元、日元、韩元)之间的互相关性。首先,通过互相关性检验,定性地发现互相关性显著存在于CNY- USD、CNY-EUR、CNY-JPY、CNY-KRW;其次,采用分形分析理论中DCCA法,定量地发现CNY-USD、CNY-EUR、CNY-JPY、CNY-KRW呈现出弱的持久性;然后,基于非线性的DCCA系数法,度量了4对互相关性的强度,发现这4个货币在人民币货币篮子的权重的顺序依次为USD>EUR>JPY>KRW;最后,结合滑窗分析法,研究了CNY-USD、CNY-EUR、CNY-JPY、CNY-KRW的时变互相关尺度指数,发现在金融危机时期,互相关尺度指数出现大的波动。

    Gang-Jin Wang, Chi Xie*. Cross-correlations between WTI crude oil market and U.S. stock market: A perspective from econophysics. Acta Physica Polonica B, 2012, 43(10): 2021-2036. [SCI/SSCI, JCR Q3]

Abstract: In this study, we take a fresh look at the cross-correlations between WTI crude oil market and U.S. stock market from the perspective of econophysics. We choose the three major U.S. stock indices (i.e., DJIA, NASDAQ and S&P 500) as the research objects and select the sample data from Jan 2, 2002 to Jun 29, 2012. In the empirical process, first, using a statistical test in analogy to the Ljung-Box test, we find that there are cross-correlations between WTI and DJIA, WTI and NASDAQ, and WTI and S&P 500 at the 5% significance level. Then, employing the multifractal detrended cross-correlation analysis (MF-DCCA) method, we find that the cross-correlated behavior between WTI crude oil market and U.S. stock market is nonlinear and multifractal. An interesting finding is that the cross-correlation exponent is smaller than the average scaling exponent when q<0, and larger than the average scaling exponent when q>0. Finally, using the rolling windows method, which can capture the dynamics of cross-correlations, we find that there are three special periods whose time-varying Hurst exponents are different from the others.


    Gang-Jin Wang, Chi Xie*, Feng Han, Bo Sun. Similarity measure and topology evolution of foreign exchange markets using dynamic time warping method: Evidence from minimal spanning tree. Physica A: Statistical Mechanics and its Applications, 2012, 391(16): 4136-4146. [SCI, Grade two by ABS 2015, JCR Q2]

Abstract: In this study, we employ a dynamic time warping method to study the topology of similarity networks among 35 major currencies in international foreign exchange (FX) markets, measured by the minimal spanning tree (MST) approach, which is expected to overcome the synchronous restriction of the Pearson correlation coefficient. In the empirical process, firstly, we subdivide the analysis period from June 2005 to May 2011 into three sub-periods: before, during, and after the US sub-prime crisis. Secondly, we choose NZD (New Zealand dollar) as the numeraire and then, analyze the topology evolution of FX markets in terms of the structure changes of MSTs during the above periods. We also present the hierarchical tree associated with the MST to study the currency clusters in each sub-period. Our results confirm that USD and EUR are the predominant world currencies. But USD gradually loses the most central position while EUR acts as a stable center in the MST passing through the crisis. Furthermore, an interesting finding is that, after the crisis, SGD (Singapore dollar) becomes a new center currency for the network.


   Gang-Jin Wang, Chi Xie*, Feng Han. Multi-scale approximate entropy analysis of foreign exchange markets efficiency. Systems Engineering Procedia, 2012, 3: 201-208.

Abstract: Market efficiency analysis is an important aspect in financial engineering. Based on weak-form efficient markets hypothesis (EMH), we characterize the market efficiency in foreign exchange (FX) markets by using the multi-scale approximate entropy (MApEn) to assess the randomness in FX markets. We split 17 daily FX rates from 1984 to 2011 into three periods by two global events, Southeast Asia currency crisis and American sub-prime crisis. The empirical results indicate that the developed FX markets is more efficient than emerging FX markets, and that the financial crisis promotes the market efficiency in FX markets significantly, especially in emerging markets, like China, Hong Kong, Korea and African market.





[1] 国家自然科学基金项目面上项目:基于多层信息溢出网络的金融机构关联性与系统性风险贡献研究(No. 71871088),2019-2022, 47万

[2] 国家自然科学基金项目青年项目:金融市场尾部相关性网络的建模及其演化与稳定性研究(No. 71501066),2016-2018,18.5万

[3] 湖南省自然科学基金项目青年项目:金融市场间信息溢出网络的构建及其演化机制研究(No. 2017JJ3024),2017-2019,5万


[1] 国家自然科学基金面上项目:复杂金融网络动态演化行为与危机传染及其控制研究(No. 71373072),主持人:谢赤,2014-2017

[2] 国家自然科学基金面上项目:渐进开放市场中资产所有权的异质跨境整合效应及风险分散策略(No. 71573077),主持人:贺红波,2016-2019

[3] 高等学校博士学科点专项科研基金:藕合实体经济的金融市场风险评估与协同监管研究(No. 20130161110031),主持人:谢赤,2014-2016