Assessing Systemic Risk Due to Fire Sales Spillover Through Maximum Entropy Network Reconstruction

Gangi, Domenico Di; Lillo, Fabrizio; Pirino, Davide

doi:10.2139/ssrn.2639178

Cited by 26 publications

(57 citation statements)

References 84 publications

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“…The main characteristic of MaxEntropy is that it generates fully connected networks, i.e., it assumes maximum diversification. Di Gangi et al (2015) show that, in the case of bipartite networks, MaxEntropy implies that all market participants hold the exact same portfolio weights.…”

Section: Detailsmentioning

confidence: 99%

“…Overall, this paper contributes to different strands of literature: first, we add to the growing literature on reconstructing financial networks from partial information (Squartini et al (2017); Gandy and Veraart (2017); Anand et al (2017); see Squartini et al (2018) for a recent survey). For the case of bipartite networks we are only aware of the works of Di Gangi et al (2015) and Squartini et al (2017). Given that most existing reconstruction methods have been designed for the case of unipartite credit networks, we adjust some of these methods to the case of bipartite networks.…”

Section: Introductionmentioning

confidence: 99%

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Reconstructing and Stress Testing Credit Networks

2017

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Financial networks are an important source of systemic risk, but often only partial network information is available. In this paper, we use data on bank-firm credit relationships in Japan and conduct a horse race between different network reconstruction methods in terms of their ability to reproduce the actual credit networks. We then compare the different reconstruction methods in terms of their implied systemic risk levels. In most instances we find that the observed credit network significantly displays the highest systemic risk level. Lastly, we explore different policies to improve the robustness of the system.

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Section: Detailsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reconstructing and Stress Testing Credit Networks

2017

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“…Price impact Securities Investment Nier et al (2007) exponential † single long Gai and Kapadia (2010) exponential † single long Arinaminpathy et al (2012) exponential † multiple ‡ long Cont and Wagalath (2013) linear multiple long/short Huang et al (2013) linear multiple long Caccioli et al (2014) exponential † multiple long Di Gangi et al (2018) linear multiple ‡ long Greenwood et al (2015) linear multiple long Caccioli et al (2015) linear single long Battiston et al (2016) linear multiple long Serri et al (2016) linear multiple long Cont and Schaanning (2017) linear multiple long Table 1: A survey of price impact functions and portfolio investment constraints utilised in shared-asset models of financial contagion. †corresponds to "linear market impact for log-prices" (Caccioli et al, 2014, p.238).…”

Section: Papermentioning

confidence: 99%

Understanding flash crash contagion and systemic risk: A micro–macro agent-based approach

Paulin

Calinescu

Wooldridge

2019

Journal of Economic Dynamics and Control

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The purpose of this paper is to advance the understanding of the conditions that give rise to flash crash contagion, particularly with respect to overlapping asset portfolio crowding. To this end, we designed, implemented, and assessed a hybrid micro-macro agent-based model, where price impact arises endogenously through the limit order placement activity of algorithmic traders. Our novel hybrid microscopic and macroscopic model allows us to quantify systemic risk not just in terms of system stability, but also in terms of the speed of financial distress propagation over intraday timescales. We find that systemic risk is strongly dependent on the behaviour of algorithmic traders, on leverage management practices, and on network topology. Our results demonstrate that, for high-crowding regimes, contagion speed is a non-monotone function of portfolio diversification. We also find the surprising result that, in certain circumstances, increased portfolio crowding is beneficial to systemic stability. We are not aware of previous studies that have exhibited this phenomenon, and our results establish the importance of considering non-uniform asset allocations in future studies. Finally, we characterise the time window available for regulatory interventions during the propagation of flash crash distress, with results suggesting ex ante precautions may have higher efficacy than ex post reactions.

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“…The portfolio holdings of the N banks can be represented by using a bipartite graph, where the first set of nodes is composed by the N banks and the second set of nodes is composed by the M risky investments; i.e. each bank j investing in the investment asset i can be represented by a link in the bipartite network connecting the bank node j with the investment node i (see Duarte andEisenbach 2013 andDi Gangi et al 2015 for the properties of such network in the US banking system).…”

Section: Overlapping Portfoliosmentioning

confidence: 99%

When Micro Prudence Increases Macro Risk: The Destabilizing Effects of Financial Innovation, Leverage, and Diversification

Corsi

Marmi

Lillo

2016

Operations Research

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract By exploiting basic common practice accounting and risk management rules, we propose a simple analytical dynamical model to investigate the effects of micro-prudential changes on macro-prudential outcomes. Specifically, we study the consequence of the introduction of a financial innovation that allow reducing the cost of portfolio diversification in a financial system populated by financial institutions having capital requirements in the form of VaR constraint and following standard mark-to-market and risk management rules. We provide a full analytical quantification of the multivariate feedback effects between investment prices and bank behavior induced by portfolio rebalancing in presence of asset illiquidity and show how changes in the constraints of the bank portfolio optimization endogenously drive the dynamics of the balance sheet aggregate of financial institutions and, thereby, the availability of bank liquidity to the economic system and systemic risk. The model shows that when financial innovation reduces the cost of diversification below a given threshold, the strength (due to higher leverage) and coordination (due to similarity of bank portfolios) of feedback effects increase, triggering a transition from a stationary dynamics of price returns to a non stationary one characterized by steep growths (bubbles) and plunges (bursts) of market prices. Permanent repository linkJEL classification: E51, G11, G18, G21.

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Assessing Systemic Risk Due to Fire Sales Spillover Through Maximum Entropy Network Reconstruction

Cited by 26 publications

References 84 publications

Reconstructing and Stress Testing Credit Networks

Reconstructing and Stress Testing Credit Networks

Understanding flash crash contagion and systemic risk: A micro–macro agent-based approach

When Micro Prudence Increases Macro Risk: The Destabilizing Effects of Financial Innovation, Leverage, and Diversification

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