Systemic Illiquidity in the Interbank Network

Ferrara, Gerardo; Langfield, Sam; Liu, Zijun; Ota, Tomohiro

doi:10.2139/ssrn.3723435

Cited by 3 publications

(3 citation statements)

References 31 publications

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“…Consider first a nominal scenario over a single period T = 1, in which e(0)=(105, 25, 10,190,10,120,0) . In this case, with no control, all nodes default.…”

Section: A Nominal Controlmentioning

confidence: 99%

Control of Dynamic Financial Networks (The Extended Version)

Calafiore,

Fracastoro,

Proskurnikov

2022

Preprint

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The current global financial system forms a highly interconnected network where a default in one of its nodes can propagate to many other nodes, causing a catastrophic avalanche effect. In this paper we consider the problem of reducing the financial contagion by introducing some targeted interventions that can mitigate the cascaded failure effects. We consider a multi-step dynamic model of clearing payments and introduce an external control term that represents corrective cash injections made by a ruling authority. The proposed control model can be cast and efficiently solved as a linear program. We show via numerical examples that the proposed approach can significantly reduce the default propagation by applying small targeted cash injections.

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“…Consider first a nominal scenario over a single period T = 1, in which e(0)=(105, 25, 10,190,10,120,0) . In this case, with no control, all nodes default.…”

Section: A Nominal Controlmentioning

confidence: 99%

Control of Dynamic Financial Networks (The Extended Version)

Calafiore,

Fracastoro,

Proskurnikov

2022

Preprint

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“…Such concerns can lead to precautionary liquidity hoarding, which restricts the supply of funding (Acharya and Merrouche, 2012;Gale and Yorulmazer, 2013;Heider et al, 2015). In such a scenario, systemic illiquidity can propagate through the network of financial institutions (Ferrara et al, 2017). In this light, the ESRB's surveys on second-round effects attempt to quantify the tendency of institutions to herd into strategies that might cause funding problems to spread between institutions during an adverse macroeconomic scenario.…”

Section: Liquidity Hoardingmentioning

confidence: 99%

From the Horse's Mouth: Surveying Responses to Stress by Banks and Insurers

Brinkhoff

Langfield

Weeken³

2018

SSRN Journal

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“…Billio et al (2012) study the financial crisis of 2007, focusing on the interconnectedness among hedge funds, banks, brokers, and insurance companies, and propose five measures of systemic risk based on statistical relations among the market returns of these institutions. More recently, Ferrara et al (2018) study the systemic illiquidity using data from Bank of England.…”

Section: Introductionmentioning

confidence: 99%

Financial Network and Systemic Risk—A Dynamic Model

Hong¹,

Wang²,

Yao

2021

Production and Operations Management

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We develop a dynamic model to study the systemic risk of the banking network, so as to study the dynamics of bank defaults. In contrast to the existing literature, we show that while the possibility of contagion is determined by interconnectedness of the financial network, whether a financial crisis can occur depends on the profile of the liquid assets of the banks in the system. Based on the dynamic model, we introduce a time to crisis index that allows us to predict the occurrence of a financial crisis. We then provide an intuitive measure of systemic risk. To illustrate the potential usefulness of our model, we provide an analysis of the system of twenty‐two German banks. We show how many of the banks are fundamentally weak, where the contagion effect may arise from, how strong the contagion effect is, and how significant the systemic risk is.

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Systemic Illiquidity in the Interbank Network

Cited by 3 publications

References 31 publications

Control of Dynamic Financial Networks (The Extended Version)

Control of Dynamic Financial Networks (The Extended Version)

From the Horse's Mouth: Surveying Responses to Stress by Banks and Insurers

Financial Network and Systemic Risk—A Dynamic Model

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