A Perspective on Correlation-Based Financial Networks and Entropy Measures

Kukreti, Vishwas; Pharasi, Hirdesh K.; Gupta, Priya; Kumar, Sunil

doi:10.3389/fphy.2020.00323

Cited by 17 publications

(14 citation statements)

References 56 publications

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“…Secondly, we have computed the eigenentropy [26] which involves the calculation of the Shannon entropy using the eigenvector centralities of the correlation matrix C τ (t) of market indices. Both mean correlation and eigenentropy have been shown to detect critical events in financial markets [26][27][28]. Thirdly, we have computed the risk corresponding to the Markowitz portfolio of the market indices, which is a proxy for the fragility or systemic risk of the global financial network [29].…”

Section: Cross-correlation Matrix and Market Indicatorsmentioning

confidence: 99%

Network-centric Indicators for Fragility in Global Financial Indices

et al. 2021

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Over the last 2 decades, financial systems have been studied and analyzed from the perspective of complex networks, where the nodes and edges in the network represent the various financial components and the strengths of correlations between them. Here, we adopt a similar network-based approach to analyze the daily closing prices of 69 global financial market indices across 65 countries over a period of 2000–2014. We study the correlations among the indices by constructing threshold networks superimposed over minimum spanning trees at different time frames. We investigate the effect of critical events in financial markets (crashes and bubbles) on the interactions among the indices by performing both static and dynamic analyses of the correlations. We compare and contrast the structures of these networks during periods of crashes and bubbles, with respect to the normal periods in the market. In addition, we study the temporal evolution of traditional market indicators, various global network measures, and the recently developed edge-based curvature measures. We show that network-centric measures can be extremely useful in monitoring the fragility in the global financial market indices.

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Section: Cross-correlation Matrix and Market Indicatorsmentioning

confidence: 99%

Network-centric Indicators for Fragility in Global Financial Indices

et al. 2021

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“…Based on network theory, all system components can be linked by their interactions to form a network; exploring global network topologies helps quantify their interconnectedness to build early warning indicators [10]. Various studies of bank, guarantee, and stock networks have found empirical evidence that global network topologies can reveal financial crises [11][12][13][14]. However, a small portion of research has emphasized that more informative signals may hide in tiny changes of local network topologies [15] because networks in similar global topologies may differ noticeably at a local level [16].…”

Section: Introductionmentioning

confidence: 99%

Motif Transition Intensity: A Novel Network-Based Early Warning Indicator for Financial Crises

et al. 2022

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Financial crisis, rooted in a lack of system resilience and robustness, is a particular type of critical transition that may cause grievous economic and social losses and should be warned against as early as possible. Regarding the financial system as a time-varying network, researchers have identified early warning signals from the changing dynamics of network motifs. In addition, network motifs have many different morphologies that unveil high-order correlation patterns of a financial system, whose synchronous change represents the dramatic shift in the financial system’s functionality and may indicate a financial crisis; however, it is less studied. This paper proposes motif transition intensity as a novel method that quantifies the synchronous change of network motifs in detail. Applying this method to stock networks, we developed three early warning indicators. Empirically, we conducted a horse race to predict ten global crises during 1991–2020. The results show evidence that the proposed indicators are more efficient than the VIX and the other 39 network-based indicators. In a detailed analysis, the proposed indicators send sensitive and comprehensible warning signals, especially for the U.S. subprime mortgage crisis and the European sovereign debt crisis. Furthermore, the proposed method provides a new perspective to detect critical signals and may be extended to predict other crisis events in natural and social systems.

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“…Further, a recent review by Kukreti et al . [ 31 ] critically examines correlation-based networks and entropy approaches in evolving financial systems. To understand the topology of the correlation-based networks as well as to define the complexity, a volume-based dimension has also been proposed by Nie et al .…”

Section: Introductionmentioning

confidence: 99%

“…Very recently, based on the distribution properties of the eigenvector centralities of correlation matrices, Chakraborti & Pharasi [30] have proposed a computationally cheap yet uniquely defined and nonarbitrary eigen-entropy measure, to show that the financial market undergoes 'phase separation' and there exists a new type of scaling behaviour (data collapse) in financial markets. Further, a recent review by Kukreti et al [31] critically examines correlation-based networks and entropy approaches in evolving financial systems. To understand the topology of the correlation-based networks as well as to define the complexity, a volume-based dimension has also been proposed by Nie et al [32].…”

Section: Introductionmentioning

confidence: 99%

Network geometry and market instability

et al. 2021

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The complexity of financial markets arise from the strategic interactions among agents trading stocks, which manifest in the form of vibrant correlation patterns among stock prices. Over the past few decades, complex financial markets have often been represented as networks whose interacting pairs of nodes are stocks, connected by edges that signify the correlation strengths. However, we often have interactions that occur in groups of three or more nodes, and these cannot be described simply by pairwise interactions but we also need to take the relations between these interactions into account. Only recently, researchers have started devoting attention to the higher-order architecture of complex financial systems, that can significantly enhance our ability to estimate systemic risk as well as measure the robustness of financial systems in terms of market efficiency. Geometry-inspired network measures, such as the Ollivier–Ricci curvature and Forman–Ricci curvature, can be used to capture the network fragility and continuously monitor financial dynamics. Here, we explore the utility of such discrete Ricci curvatures in characterizing the structure of financial systems, and further, evaluate them as generic indicators of the market instability. For this purpose, we examine the daily returns from a set of stocks comprising the USA S&P-500 and the Japanese Nikkei-225 over a 32-year period, and monitor the changes in the edge-centric network curvatures. We find that the different geometric measures capture well the system-level features of the market and hence we can distinguish between the normal or ‘business-as-usual’ periods and all the major market crashes. This can be very useful in strategic designing of financial systems and regulating the markets in order to tackle financial instabilities.

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A Perspective on Correlation-Based Financial Networks and Entropy Measures

Cited by 17 publications

References 56 publications

Network-centric Indicators for Fragility in Global Financial Indices

Network-centric Indicators for Fragility in Global Financial Indices

Motif Transition Intensity: A Novel Network-Based Early Warning Indicator for Financial Crises

Network geometry and market instability

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