Dynamics of Correlation Structure in Stock Market

Djauhari, Maman A.; Gan, Siew Lee

doi:10.3390/e16010455

Cited by 8 publications

(3 citation statements)

References 36 publications

(60 reference statements)

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“…The analysis of interactions between financial objects is usually carried out in correlation network approaches. Topological network analysis is a method that provides practical tools for interpreting market characteristics (Mantegna and Stanley, 2000;Djauhari and Lee, 2014). The structure of a network determines how different nodes are connected and how data are transmitted between these nodes.…”

Section: Literature Reviewmentioning

confidence: 99%

Network analysis of the foreign exchange market using minimum spanning trees constructed from the dynamic time warping distance measure

Landmesser-Rusek

2024

Argumenta Oeconomica

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This paper evaluates the changes that occurred in the topological structure of the foreign exchange market due to the COVID-19 pandemic and after the Russian invasion of Ukraine. A network of 17 currencies was analysed from 1.01.2019 to 31.07.2022 in four sub-periods: before the pandemic, in the year of the pandemic outbreak, in the further course of the pandemic, and the year the war started. Dynamic time warping (DTW) distances between pairs of time series for individual currencies were calculated, and based on these distances, minimum spanning trees (MST) were constructed whose topological characteristics were then analysed. The results show that the topological structure of the foreign exchange market varied in the sub-periods studied. The financial crises caused by the COVID-19 pandemic and the Russian invasion of Ukraine affected the FX network in different ways. However, the two strongest monetary clusters (i.e. clusters centred on the USD and EUR, respectively) have still emerged. Comparing the MSTs for the crisis sub-periods, it can be seen that the USD took a dominant position during the pandemic outbreak, while the EUR played a more significant role during the war. This study contributes to the literature by characterising the topological structure of the FX market in specific sub-periods – during the COVID-19 pandemic and after the Russian invasion of Ukraine. Thus, it represents a particularly relevant study of the impact of crises on the global financial market.

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Section: Literature Reviewmentioning

confidence: 99%

Network analysis of the foreign exchange market using minimum spanning trees constructed from the dynamic time warping distance measure

Landmesser-Rusek

2024

Argumenta Oeconomica

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“…The Jaccard index can be used to quantify the degree of proximity between two networks based on common properties they share with each other [10,33]. This measure is also useful in tracking the structural (or topological) changes of networks [10,34]. For two networks G 1 and G 2 , the Jaccard index is defined by…”

Section: Jaccard Indexmentioning

confidence: 99%

New York Stock Exchange performance: evidence from the forest of multidimensional minimum spanning trees

Gan

Djauhari

2015

J. Stat. Mech.

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. The standard practice of stock network analysis is still too far from reality. It involves Pearson correlation (PC) to quantify the similarity among stocks based on closing price only, and the minimum spanning tree (MST) to filter the economic information. In daily practice, stock is represented by its four prices: the opening, highest, lowest, and closing prices. To incorporate the information from these prices, vector correlation is used instead of PC. Furthermore, to get robust network topological properties, the MST is replaced by the forest of all possible MSTs. Its advantages is demonstrated in the analysis of 1515 stocks traded in the New York Stock Exchange from 2005–2014.

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“…Following this idea, some researchers considered the dynamic conditional correlation multivariate GARCH (DCC-MV-GARCH) model to find dynamic conditional correlations among stocks [ 13 , 14 , 15 , 16 , 17 ]. Some other researchers constructed correlation networks over a sliding window, such as Djauhari and Gan [ 18 ], and Papana et al [ 19 ]. Although not strictly relevant to the issue of dynamics of stock networks, but still relevant to the analysis of dynamic correlations, we can also see some other methods on dependence analyses in the literature, such as the time-varying copula approach [ 20 ], bivariate EGARCH model [ 21 ], DSTCC-GARCH models [ 22 ], multivariate normal mixture models [ 23 ], detrended cross-correlation analysis (DCCA) [ 24 , 25 ], and detrended fluctuation analysis (DFA) [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Stock Net Entropy: Evidence from the Chinese Growth Enterprise Market

Han

Wan

et al. 2018

Entropy

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By introducing net entropy into a stock network, this paper focuses on investigating the impact of network entropy on market returns and trading in the Chinese Growth Enterprise Market (GEM). In this paper, indices of Wu structure entropy (WSE) and SD structure entropy (SDSE) are considered as indicators of network heterogeneity to present market diversification. A series of dynamic financial networks consisting of 1066 daily nets is constructed by applying the dynamic conditional correlation multivariate GARCH (DCC-MV-GARCH) model with a threshold adjustment. Then, we evaluate the quantitative relationships between network entropy indices and market trading-variables and their bilateral information spillover effects by applying the bivariate EGARCH model. There are two main findings in the paper. Firstly, the evidence significantly ensures that both market returns and trading volumes associate negatively with the network entropy indices, which indicates that stock heterogeneity, which is negative with the value of network entropy indices by definition, can help to improve market returns and increase market trading volumes. Secondly, results show significant information transmission between the indicators of network entropy and stock market trading variables.

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Dynamics of Correlation Structure in Stock Market

Cited by 8 publications

References 36 publications

Network analysis of the foreign exchange market using minimum spanning trees constructed from the dynamic time warping distance measure

Network analysis of the foreign exchange market using minimum spanning trees constructed from the dynamic time warping distance measure

New York Stock Exchange performance: evidence from the forest of multidimensional minimum spanning trees

Stock Net Entropy: Evidence from the Chinese Growth Enterprise Market

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