Dynamical macroprudential stress testing using network theory

Levy-Carciente, Sary; Kenett, Dror Y.; Avakian, Adam; Stanley, H. Eugene; Havlin, Shlomo

doi:10.1016/j.jbankfin.2015.05.008

Cited by 79 publications

(41 citation statements)

References 63 publications

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“…The analysis of economic and financial networks, with the aim of measuring and monitoring the risks arising from Systemically Important Financial Institutions (SIFIs), has received a lot of attention [1] since a distress hitting these institutions could easily reverberate in the whole market [2]. …”

Section: Introductionmentioning

confidence: 99%

Discovering SIFIs in Interbank Communities

2016

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This paper proposes a new methodology based on non-negative matrix factorization to detect communities and to identify central nodes in a network as well as within communities. The method is specifically designed for directed weighted networks and, consequently, it has been applied to the interbank network derived from the e-MID interbank market. In an interbank network indeed links are directed, representing flows of funds between lenders and borrowers. Besides distinguishing between Systemically Important Borrowers and Lenders, the technique complements the detection of systemically important banks, revealing the community structure of the network, that proxies the most plausible areas of contagion of institutions’ distress.

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Section: Introductionmentioning

confidence: 99%

Discovering SIFIs in Interbank Communities

2016

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confidence: 94%

“…Market clustering might be an example of an existing market structure that can amplify seemingly unimportant events into wide-spread market volatility. In case market clusters coincide with otherwise interconnected institutions, for example banks, common asset devaluation can be a crucial default contagion channel, as suggested in recent interdisciplinary research (Tsatskis, 2012;Glasserman and Young, 2016;Levy-Carciente et al, 2015).Market clustering is expected to cause price shocks, because it amplifies the effect of existing sources of price fluctuations. More specifically, market clustering is expected to increase the chance of price shocks in two different situations: Firstly, when the order deluge due to the group behaviour overwhelms the supply (Stein, 2009;Braun-Munzinger et al, 2018) and, secondly, when the supply is thin due to the homogeneity of the investors' pool, i.e.…”

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confidence: 99%

Crowded Trades, Market Clustering, and Price Instability

et al. 2020

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Crowded trades by similarly trading peers influence the dynamics of asset prices, possibly creating systemic risk. We propose a market clustering measure using granular trading data. For each stock the clustering measure captures the degree of trading overlap among any two investors in that stock. We investigate the effect of crowded trades on stock price stability and show that market clustering has a causal effect on the properties of the tails of the stock return distribution, particularly the positive tail, even after controlling for commonly considered risk drivers. Reduced investor pool diversity could thus negatively affect stock price stability. source of price instability, but also a channel of volatility spillovers, eventually resulting in correlated price jumps. In that case, market clustering would foster systemic risks. Market clustering might be an example of an existing market structure that can amplify seemingly unimportant events into wide-spread market volatility. In case market clusters coincide with otherwise interconnected institutions, for example banks, common asset devaluation can be a crucial default contagion channel, as suggested in recent interdisciplinary research (Tsatskis, 2012;Glasserman and Young, 2016;Levy-Carciente et al., 2015).Market clustering is expected to cause price shocks, because it amplifies the effect of existing sources of price fluctuations. More specifically, market clustering is expected to increase the chance of price shocks in two different situations: Firstly, when the order deluge due to the group behaviour overwhelms the supply (Stein, 2009;Braun-Munzinger et al., 2018) and, secondly, when the supply is thin due to the homogeneity of the investors' pool, i.e. a lack of liquidity at one side of the order book (Weber and Rosenow, 2006). In both situations market clustering increases the chance that the demand exceeds the supply, either in buy or sell orders.We start our investigation of the influence of trading patterns by studying the relation between market clustering and the price dynamics of individual stocks. Our market clustering measure is unique in the sense that it quantifies two aspects of group behavior: clustering and crowdedness. We define price instability as an increase of the number of sharp price fluctuations, such that the tails of the log return distribution are heavier. Specifically, we investigate whether there is a causal relation between market clustering and the skewness, kurtosis, tail indices, positive and negative outlier counts, changes in downside risk and upside gains.The analysis of trading patterns depends on the ability to distinguish to what extent the patterns are the result of higher order features like group behaviour instead of lower order properties. In this research, we represent stock trading by a complex bipartite network (a two-layer network with non-trivial topological features). The two layers are the investors and the stocks. The links between the layers are the trades during a particular time period.We use the maxi...

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“…This paper extends pinning control to financial network and deems the government's bailout against risks as pinning control or an exogenous policy restraint for certain banks, so that the solvency of the whole bank network changes in line with the government's expectations and then the goal of bailout is attained. The comparative result of different bailout strategies offers a basis of judgment for bailout behaviors [33].…”

Section: Introductionmentioning

confidence: 99%

A Regulation Model for the Solvency of Banking System: Based on the Pinning Control Theory of Complex Network

Gao

Zhou

Ouyang

2017

Discrete Dynamics in Nature and Society

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A dynamic model is proposed based on the pinning control theory of complex network in order to simulate government bailouts against financial crisis and then is applied to a stress test of China's interbank borrowing and lending network from 2007 to 2014. The proposed model takes many cases into account, so it is able to simulate bailout effects with different parameters, capture temporal and individual differences of banks' spillovers effects, and reflect their sensitivity to government bailouts indirectly. This paper offers an innovative model to identify the systemic-important banks in financial crisis and construct a macroprudential regulation system based on network theory.

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Dynamical macroprudential stress testing using network theory

Cited by 79 publications

References 63 publications

Discovering SIFIs in Interbank Communities

Discovering SIFIs in Interbank Communities

Crowded Trades, Market Clustering, and Price Instability

A Regulation Model for the Solvency of Banking System: Based on the Pinning Control Theory of Complex Network

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