A review of two decades of correlations, hierarchies, networks and clustering in financial markets

Marti, Gautier; Nielsen, Frank; Bińkowski, Mikołaj; Donnat, Philippe

doi:10.48550/arxiv.1703.00485

Cited by 17 publications

(21 citation statements)

References 198 publications

(305 reference statements)

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“…Many large scale systems are best described as networks [3,4,5,12,20,33,31,41]. A standard approach of network construction is to create covariance-based measures of interlinkages [41].…”

Section: Discussionmentioning

confidence: 99%

“…A co-movement network is constructed by considering each response variable as a node of the graph and an undirected edge between two nodes exists if the corresponding correlation is nonzero. This kind of network construction out of observational multi-variate data has been very successful as a modeling paradigm in finance [31] and biology [12,3] among others. However, such inference about existence of linkages from purely observational data has a problem.…”

Section: Introductionmentioning

confidence: 99%

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Sparsistent filtering of comovement networks from high-dimensional data

Chakrabarti¹

2021

Preprint

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Network filtering is an important form of dimension reduction to isolate the core constituents of large and interconnected complex systems. We introduce a new technique to filter large dimensional networks arising out of dynamical behavior of the constituent nodes, exploiting their spectral properties. As opposed to the well known network filters that rely on preserving key topological properties of the realized network, our method treats the spectrum as the fundamental object and preserves spectral properties. Applying asymptotic theory for high dimensional data for the filter, we show that it can be tuned to interpolate between zero filtering to maximal filtering that induces sparsity and consistency while having the least spectral distance from a linear shrinkage estimator.

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“…Many large scale systems are best described as networks [3,4,5,12,20,33,31,41]. A standard approach of network construction is to create covariance-based measures of interlinkages [41].…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sparsistent filtering of comovement networks from high-dimensional data

Chakrabarti¹

2021

Preprint

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“…About the same time, Mantegna, another econophysicist, discovered the hierarchical structure of financial correlations [15] whose seminal and influential work sparked a rich empirical research in financial networks and clustering. An extensive review of this literature can be found in [16].…”

Section: Financial Correlation Matricesmentioning

confidence: 99%

CORRGAN: Sampling Realistic Financial Correlation Matrices Using Generative Adversarial Networks

Marti

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We propose a novel approach for sampling realistic financial correlation matrices. This approach is based on generative adversarial networks. Experiments demonstrate that generative adversarial networks are able to recover most of the known stylized facts about empirical correlation matrices estimated on asset returns. This is the first time such results are documented in the literature. Practical financial applications range from trading strategies enhancement to risk and portfolio stress testing. Such generative models can also help ground empirical finance deeper into science by allowing for falsifiability of statements and more objective comparison of empirical methods.

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“…Other authors have also followed up on and expanded this work by studying graph dynamics over time [3,20] and examined methods for building the graph [5,21,19]. In fact, to this day, the topic of graphs as a model for equity markets remains a subject of discussion in the literature [1,23].…”

Section: Previous Workmentioning

confidence: 99%

Market Graph Clustering Via QUBO and Digital Annealing

Hong¹,

Miasnikof

Kwon³

et al. 2020

Preprint

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Our goal is to find representative nodes of a market graph that best replicate the returns of a broader market graph (index), a common task in the financial industry. We model our reference index as a market graph and express the index tracking problem in a quadratic K-medoids form. We take advantage of a purpose built hardware architecture, the Fujitsu Digital Annealer, to circumvent the NP-hard nature of the problem and solve our formulation efficiently. In this article, we combine three separate areas of the literature, market graph models, K-medoid clustering and quadratic binary optimization modeling, to formulate the index-tracking problem as a quadratic K-medoid graphclustering problem. Our initial results show we accurately replicate the returns of a broad market index, using only a small subset of its constituent assets. Moreover, our quadratic formulation allows us to take advantage of recent hardware advances, to overcome the NP-hard nature of the problem.

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A review of two decades of correlations, hierarchies, networks and clustering in financial markets

Cited by 17 publications

References 198 publications

Sparsistent filtering of comovement networks from high-dimensional data

Sparsistent filtering of comovement networks from high-dimensional data

CORRGAN: Sampling Realistic Financial Correlation Matrices Using Generative Adversarial Networks

Market Graph Clustering Via QUBO and Digital Annealing

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