Systemic Risk in an Interconnected Banking System with Endogenous Asset Markets

Bluhm, Marcel; Krahnen, Jan Pieter

doi:10.2139/ssrn.2421265

Cited by 16 publications

(11 citation statements)

References 54 publications

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“…Billio et al (2012) examined the connectedness between banks, dealers, insurance companies, and hedge funds using a Granger-causality network and suggested that banks contribute more to shock transmissions than do other financial institutions. Bluhm and Krahnen (2014) employed a network model of interconnected bank balance sheets to explore the emergence of systemic risk and proposed a new macroprudential risk management approach. On the basis of the measure of the realized systemic risk beta, Hautsch, Schaumburg, and Schienle (2014) ranked a firm's share of the systemic risk in the US financial system.…”

Section: Literature Reviewmentioning

confidence: 99%

Estimating the connectedness of commodity futures using a network approach

Xiao

Fang

et al. 2019

Journal of Futures Markets

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Using a network approach of variance decompositions, we measure the connectedness of 18 commodity futures and characterize both static and dynamic connectedness. Our results show that metal futures are net transmitters of shocks to other futures, and agricultural futures are vulnerable to shocks from the others. Furthermore, almost two‐thirds of the volatility uncertainty for commodity futures are due to the connectedness of shocks across the futures market. Dynamically, we find connectedness always increases in times of turmoil. An analysis of connectedness networks suggests that investors could be forewarned that the connectedness of various classes of futures could threaten their portfolios.

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Section: Literature Reviewmentioning

confidence: 99%

Estimating the connectedness of commodity futures using a network approach

Xiao

Fang

et al. 2019

Journal of Futures Markets

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“…The recent financial crises have highlighted the importance to properly assess systemic risk in capital markets. In particular, both researchers and regulators realized that the financial system is actually more fragile than previously thought, because of the complexity of interconnections between financial institutions [2][3][4][5][6], resulting both from direct exposures to bilateral contracts and from indirect exposures to common assets [7][8][9][10][11]. Indeed, while interconnectedness means diversification and thus reduces individual risk, it can however increase systemic risk: financial distress can spread between institutions through such exposures and propagate over the market, leading to amplification effects like default cascades [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Entangling Credit and Funding Shocks in Interbank Markets

Cimini

Serri

2016

PLoS ONE

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Credit and liquidity shocks represent main channels of financial contagion for interbank lending markets. On one hand, banks face potential losses whenever their counterparties are under distress and thus unable to fulfill their obligations. On the other hand, solvency constraints may force banks to recover lost fundings by selling their illiquid assets, resulting in effective losses in the presence of fire sales—that is, when funding shortcomings are widespread over the market. Because of the complex structure of the network of interbank exposures, these losses reverberate among banks and eventually get amplified, with potentially catastrophic consequences for the whole financial system. Inspired by the recently proposed Debt Rank, in this work we define a systemic risk metric that estimates the potential amplification of losses in interbank markets accounting for both credit and liquidity contagion channels: the Debt-Solvency Rank. We implement this framework on a dataset of 183 European banks that were publicly traded between 2004 and 2013, showing indeed that liquidity spillovers substantially increase systemic risk, and thus cannot be neglected in stress-test scenarios. We also provide additional evidence that the interbank market was extremely fragile up to the global financial crisis, becoming slightly more robust only afterwards.

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“…External requirements are often set by regulators to safeguard sufficient buffers for various risks (credit risk -using both risk weighted and risk insensitive measures (i.e. leverage ratios), counterparty credit risk, or liquidity risk (Bluhm and Krahnen, 2014;Cont and Schaaning, 2016;Ellul et al, 2011;Thurner et al, 2012;Aymanns and Farmer, 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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Systemic Risk in an Interconnected Banking System with Endogenous Asset Markets

Cited by 16 publications

References 54 publications

Estimating the connectedness of commodity futures using a network approach

Estimating the connectedness of commodity futures using a network approach

Entangling Credit and Funding Shocks in Interbank Markets

Crowded Trades, Market Clustering, and Price Instability

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