Financial market volatility and contagion effect: A copula–multifractal volatility approach

Chen, Wang; Wei, Yu; Lang, Qiaoqi; Yu, Lin; Liu, Maojuan

doi:10.1016/j.physa.2013.12.016

Cited by 38 publications

(25 citation statements)

References 65 publications

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“…A wide range of variations have been developed based on this technique. In this respect, Chen, Wei, Lang, Lin & Liu (2014) propose a new approach based on the multifractal volatility method to study the contagion effect between the U. S. and Chinese stock markets. Changqing, Chi, Cong, & Yan (2015) construct a dynamic Markov Regime Switching Copula (mrsc) models to measure the financial risk contagion between the Chinese market and other international stock markets.…”

Section: Literature Reviewmentioning

confidence: 99%

Contagion and Stock Interdependence in the BRIC+M Block

Castro

Pacheco

Rosales

2018

ETYP

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This paper aims to analyze the contagion effect among the stock markets of the bric+m block (Brazil, Russia, India, China plus Mexico). The contagion effect is proved through increases on dependence parameters during crisis periods. The dependence parameters are estimated through a dynamic bivariate copula approach for the period July 1997 to December 2015. During this period there were instability and calm episodes, which allow analyzing changes in the relations of dependence. Empirical results show strong evidence of time-varying dependence among the bric+m markets and an increasing dependence relation during the global financial crisis period.

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Section: Literature Reviewmentioning

confidence: 99%

Contagion and Stock Interdependence in the BRIC+M Block

Castro

Pacheco

Rosales

2018

ETYP

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“…Standard deviation is also used to study market risk but mainly in highly random market conditions. W Chen et al [31] have analyzed the global financial system using multiracial volatility approach.…”

Section: Introductionmentioning

confidence: 99%

Diffusion Entropy Analysis: Stability of Indian Stock Market

Kumar*,

kumar,

Kumar*

2019

IJRTE

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The large amount of available data of stock markets becomes very beneficial when it is transformed to valuable information. The analysis of this huge data is essential to extract out the useful information. In the present work, we employ the method of diffusion entropy to study time series of different indexes of Indian stock market. We analyze the stability of Nifty50 index of National Stock Exchange (NSE) India and SENSEX index of Bombay Stock Exchange (BSE), India in the vicinity of global financial crisis of 2008. We also apply the technique of diffusion entropy to analyze the stability of Dow Jones Industrial Average (DJIA) index of USA. We compare the results of Indian Stock market with the USA stock market (DJIA index). We conduct an empirical analysis of the stability of Nifty50, Sensex and DJIA indexes. We find significant drop in the value of diffusion entropy of Nifty50, Sensex and DJIA during the period of crisis. Both Indian and USA stock markets show bull market effects in the pre-crisis and post-crisis periods and bear market effect during the period of crisis. Our findings reveal that diffusion entropy technique can replicate the price fluctuations as well as critical events of the stock market.

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“…The goal has been to unveil possible multifractal long-range cross correlations between two time series. Such long-range cross correlations in pairs of series have widely applied in financial markets, ranging from uncovering the facts of cross multifractal nature [1][2][3] in different markets to building trading strategies to get excess returns [4], from improving the estimation of hedge ratio [5] to incorporating the copula-multifractality into the calculation of volatilities [6]. An early method, joint multifractal analysis, was invented in 1990 to study the relationship between the dissipation rates of kinetic energy and passive scalar fluctuations in fully developed turbulence and to handle the joint partition function of two multifractal measures [7].…”

Section: Introductionmentioning

confidence: 99%

Multifractal Cross Wavelet Analysis

et al. 2017

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Complex systems are composed of mutually interacting components and the output values of these components usually exhibit long-range cross-correlations. Using wavelet analysis, we propose a method of characterizing the joint multifractal nature of these long-range cross correlations, a method we call multifractal cross wavelet analysis (MFXWT). We assess the performance of the MFXWT method by performing extensive numerical experiments on the dual binomial measures with multifractal cross correlations and the bivariate fractional Brownian motions (bFBMs) with monofractal cross correlations. For binomial multifractal measures, we find the empirical joint multifractality of MFXWT to be in approximate agreement with the theoretical formula. For bFBMs, MFXWT may provide spurious multifractality because of the wide spanning range of the multifractal spectrum. We also apply the MFXWT method to stock market indices, and in pairs of index returns and volatilities we find an intriguing joint multifractal behavior. The tests on surrogate series also reveal that the cross correlation behavior, particularly the cross correlation with zero lag, is the main origin of cross multifractality.

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Financial market volatility and contagion effect: A copula–multifractal volatility approach

Cited by 38 publications

References 65 publications

Contagion and Stock Interdependence in the BRIC+M Block

Contagion and Stock Interdependence in the BRIC+M Block

Diffusion Entropy Analysis: Stability of Indian Stock Market

Multifractal Cross Wavelet Analysis

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