Stability analysis of financial contagion due to overlapping portfolios

Caccioli, Fabio; Shrestha, Munik; Moore, Cristopher; Farmer, J. Doyne

doi:10.1016/j.jbankfin.2014.05.021

Cited by 340 publications

(193 citation statements)

References 42 publications

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“…The observed figures and patterns are largely in line with Thurner (2011) andCaccioli et al (2014). An increased allowed leverage in Scenario 7 provides opportunity for increased balance sheets, at the expense of an increased number of bankruptcies as a result of higher risk.…”

Section: The Effects Of Policy Actionssupporting

confidence: 77%

“…Fischer and Riedler (2014) build a banking ABM and reproduce many empirical stylized facts from the financial markets, like a lognormal distribution of bank balance sheets, a positive and convex relation between leverage and size-inequality, and a positive relation between leverage and systemic risk. Caccioli et al (2014) develop an agent-based network model and show that amplification of financial contagion can occur due to the combination of overlapping portfolios and excessive leverage. In this respect, they argue that too much portfolio diversification can induce significant systemic risk.…”

Section: Introductionmentioning

confidence: 99%

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Interbank loans, collateral and modern monetary policy

Wolski

Leur

2016

Journal of Economic Dynamics and Control

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Section: The Effects Of Policy Actionssupporting

confidence: 77%

Section: Introductionmentioning

confidence: 99%

Interbank loans, collateral and modern monetary policy

Wolski

Leur

2016

Journal of Economic Dynamics and Control

View full text Add to dashboard Cite

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“…Although seemingly intuitive, the concept of crowding has remained elusive and is, in fact, somewhat paradoxical as every buy trade is executed against a sell trade of the same magnitude. Some clarification is therefore needed, and the subject has recently garnered substantial interest in academic [2, 6,10,11,20] and applied research [13].…”

Section: Introductionmentioning

confidence: 99%

Zooming In on Equity Factor Crowding

et al. 2020

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Crowding is most likely an important factor in the deterioration of strategy performance, the increase of trading costs and the development of systemic risk. We study the imprints of crowding on both anonymous market data and a large database of metaorders from institutional investors in the U.S. equity market. We propose direct metrics of crowding that capture the presence of investors contemporaneously trading the same stock in the same direction by looking at fluctuations of the imbalances of trades executed on the market. We identify significant signs of crowding in well known equity signals, such as Fama-French factors and especially Momentum. We show that the rebalancing of a Momentum portfolio can explain between 1-2% of order flow, and that this percentage has been significantly increasing in recent years.

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“…Common asset holdings have attracted considerable attention, especially in the context of fire-sale spill-overs and cascade dynamics (Caccioli et al, 2014(Caccioli et al, , 2015Greenwood et al, 2015).…”

Section: Literature Reviewmentioning

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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Stability analysis of financial contagion due to overlapping portfolios

Cited by 340 publications

References 42 publications

Interbank loans, collateral and modern monetary policy

Interbank loans, collateral and modern monetary policy

Zooming In on Equity Factor Crowding

Crowded Trades, Market Clustering, and Price Instability

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