Higher order assortativity in complex networks

Arcagni, Alberto; Grassi, Rosanna; Stefani, Silvana; Torriero, Anna

doi:10.1016/j.ejor.2017.04.028

Cited by 26 publications

(18 citation statements)

References 42 publications

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“…This shift can be explained again by the impact of the large number of uncategorised nodes. f An alternative strategy to go beyond first order neighbours in the computation of assortativity has been recently proposed by [54]. g The distance between nodes in a network is defined as the length of the shortest directed path connecting two nodes, where a path is a sequence of links.…”

Section: C8 Models Training and Hyper-parameter Optimisationmentioning

confidence: 99%

Corporate payments networks and credit risk rating

Letizia

Lillo

2019

EPJ Data Sci.

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Aggregate and systemic risk in complex systems are emergent phenomena depending on two properties: the idiosyncratic risk of the elements and the topology of the network of interactions among them. While a significant attention has been given to aggregate risk assessment and risk propagation once the above two properties are given, less is known about how the risk is distributed in the network and its relations with its topology. We study this problem by investigating a large proprietary dataset of payments among 2.4M Italian firms, whose credit risk rating is known. We document significant correlations between local topological properties of a node (firm) and its risk. Moreover we show the existence of an homophily of risk, i.e. the tendency of firms with similar risk profile to be statistically more connected among themselves. This effect is observed when considering both pairs of firms and communities or hierarchies identified in the network. We leverage this knowledge to show the predictability of the missing rating of a firm using only the network properties of the associated node.

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Section: C8 Models Training and Hyper-parameter Optimisationmentioning

confidence: 99%

Corporate payments networks and credit risk rating

Letizia

Lillo

2019

EPJ Data Sci.

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“…For example, the level of herd behavior among mutual funds increases during high periods of volatility and price downturns [39], precisely when financial shocks disturb the markets. An alternative to study the problem of herding could consider a more detailed analysis of the assortativity of the bipartite network of funds and stocks when considering higher order assortativity measures that could describe new topological characteristics of the network [49].…”

Section: Bipartite Networkmentioning

confidence: 99%

Modeling Overlapped Mutual Funds’ Portfolios: A Bipartite Network Approach

2019

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This paper studies the topology of the Chilean mutual fund industry using networks methods. With the physical positions of the local equity portfolios managed during 2003.01-2017.4, we analyze their connectivity structure in both the mutual funds’ bipartite network and their one-mode projection. We estimate network measures to examine the potential effects on the topology arising from changes in the industrial environment and changes in the mutual funds’ investment strategies in their overlapped portfolios. Our main results show that changes in the bipartite network and its one-mode projection are correlated with variables related to funds’ investment strategies and with industry-specific variables. In consequence, these elements are a new potential of disturbance in the financial network conformed by stocks and mutual funds. We contribute to the existing literature, improving the understanding of the aggregate behavior of a financial sector which despite its economic importance has attracted little attention from a systemic risk perspective.

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“…4 An alternative strategy to go beyond first order neighbours in the computation of assortativity has been recently proposed by [Arcagni et al, 2017].…”

Section: Assortative Mixing Of Riskmentioning

confidence: 99%

Corporate Payments Networks and Credit Risk Rating

Letizia

Lillo

2018

SSRN Journal

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Aggregate and systemic risk in complex systems are emergent phenomena depending on two properties: the idiosyncratic risks of the elements and the topology of the network of interactions among them. While a significant attention has been given to aggregate risk assessment and risk propagation once the above two properties are given, less is known about how the risk is distributed in the network and its relations with the topology. We study this problem by investigating a large proprietary dataset of payments among 2.4M Italian firms, whose credit risk rating is known. We document significant correlations between local topological properties of a node (firm) and its risk. Moreover we show the existence of an homophily of risk, i.e. the tendency of firms with similar risk profile to be statistically more connected among themselves. This effect is observed when considering both pairs of firms and communities or hierarchies identified in the network. We leverage this knowledge to show the predictability of the missing rating of a firm using only the network properties of the associated node.keyword financial networks -corporate networks -credit risk -credit rating -machine learning JEL classification:

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Higher order assortativity in complex networks

Cited by 26 publications

References 42 publications

Corporate payments networks and credit risk rating

Corporate payments networks and credit risk rating

Modeling Overlapped Mutual Funds’ Portfolios: A Bipartite Network Approach

Corporate Payments Networks and Credit Risk Rating

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