Structure of Investor Networks and Financial Crises

Baltakys, Kęstutis; Viet, Hung Le; Kanniainen, Juho

doi:10.3390/e23040381

Cited by 4 publications

(4 citation statements)

References 43 publications

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“…In addition, it is of good research value to predict the important nodes in the risk propagation process of economic networks [28,38]. Extending existing economic networkbased models [25], so that their parameters can be statistically learned from the data, while different modeling frameworks can be compared for validation.…”

Section: Discussionmentioning

confidence: 99%

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RETRACTED: An Adaptive Hierarchical Network Model for Studying the Structure of Economic Network

Yang

Javaid

2022

Entropy

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The interdependence of financial institutions is primarily responsible for creating a systemic hierarchy in the industry. In this paper, an Adaptive Hierarchical Network Model is proposed to study the problem of hierarchical relationships arising from different individuals in the economic domain. In the presented dynamically evolving network model, new directed edges are generated depending on the existing nodes and the hierarchical structures among the network, and these edges decay over time. When the preference of nodes in the network for higher ranks exceeds a certain threshold value, the equality state in the network becomes unstable and rank states emerge. Meanwhile, we select four real data sets for model evaluation and observe the resilience in the network hierarchy evolution and the differences formed by different patterns of hierarchy preference mechanisms, which help us better understand data science and network dynamics evolution.

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Section: Discussionmentioning

confidence: 99%

“…At the same time, the rapid growth of financial data is becoming more and more important to reflect the connection between data through the web [25]. Different financial institutions are becoming more and more closely connected, and the financial structure has become dynamically diverse.…”

Section: Literature Reviewmentioning

confidence: 99%

RETRACTED: An Adaptive Hierarchical Network Model for Studying the Structure of Economic Network

Yang

Javaid

2022

Entropy

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“…In economics, the role of networks has long been studied in game theory and payment systems (Kobayashi, Masuda, 2021), in finance (Faggini, Bruno, Parziale, 2019;Baltakys, Le Viet, Kanniainen, 2021), or in economics (Rendón de la Torre et al, 2016). However, in many fields of economics, network analysis has not received much attention until recently (Nagurney, 2019;Kobayashi, Masuda, 2021).…”

Section: = First Pension Lat Wage Rrmentioning

confidence: 99%

Sieci adekwatności emerytalnej w Europie

Jajko-Siwek

2023

Folia Oeconomica

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Proces starzenia się ludności Europy stanowi wyzwanie dla systemów emerytalnych. Oznacza to konieczność poszukiwania rozwiązań, które pozwolą poszczególnym krajom generować adekwatne świadczenia emerytalne. Celem artykułu jest przedstawienie typologii sieci adekwatnych świadczeń emerytalnych w krajach europejskich. Jako metodę badania zastosowano sieci społecznościowe. W analizie uwzględniono typologię sieci wynikającą z czynników emerytalnych, takich jak: staż pracy, wiek emerytalny, płeć, liczba dzieci oraz czas nauki. Sprawdzono również korelację między sieciami. Najbardziej nietypową sieć uzyskano dla liczby dzieci. Spośród wszystkich badanych czynników najbardziej zbliżone są do siebie sieci uzyskane dla stażu pracy i wieku emerytalnego. Otrzymane wyniki wskazują, że systemy emerytalne, które są zbudowane w ten sam sposób, charakteryzują się także podobnymi wzorcami poziomu czynników emerytalnych. Wzorce powiązań dla badanych zmiennych nie są zróżnicowane ze względu na płeć.

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“…Neuroscientists infer structural and functional connections among neurons or brain regions from neural recordings to inform their understanding of the brain [4]. Financial networks inferred from recordings of stock and/or currency prices or market transactions are important tools for assessing national and global risks of financial crises [5,6].…”

Section: Introductionmentioning

confidence: 99%

Network inference via process motifs for lagged correlation in linear stochastic processes

Schwarze¹,

Ichinaga²,

Brunton³

2022

Preprint

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A major challenge for causal inference from time-series data is the trade-off between computational feasibility and accuracy. Motivated by process motifs for lagged covariance in an autoregressive model with slow mean-reversion, we propose to infer networks of causal relations via pairwise edge measure (PEMs) that one can easily compute from lagged correlation matrices. Motivated by contributions of process motifs to covariance and lagged variance, we formulate two PEMs that correct for confounding factors and for reverse causation. To demonstrate the performance of our PEMs, we consider network interference from simulations of linear stochastic processes, and we show that our proposed PEMs can infer networks accurately and efficiently. Specifically, for autocorrelated time-series data, our approach achieves accuracies higher than or similar to Granger causality, transfer entropy, and convergent crossmapping-but with much shorter computation time than possible with any of these methods. Our fast and accurate PEMs are easy-to-implement methods for network inference with a clear theoretical underpinning. They provide promising alternatives to current paradigms for the inference of linear models from time-series data, including Granger causality, vector-autoregression, and sparse inverse covariance estimation.

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Structure of Investor Networks and Financial Crises

Cited by 4 publications

References 43 publications

RETRACTED: An Adaptive Hierarchical Network Model for Studying the Structure of Economic Network

RETRACTED: An Adaptive Hierarchical Network Model for Studying the Structure of Economic Network

Sieci adekwatności emerytalnej w Europie

Network inference via process motifs for lagged correlation in linear stochastic processes

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