Network-based risk measurements for interbank systems

PLOS ONE, Nov 2019

This paper focuses on evaluating the systemic risk in interbank networks, proposing a series of measurements: risk distance, risk degree and m-order risk degree. The proposed measurements are formally proven to have good basic and extended properties that are able to reflect the effect of bank size, liability size, liability distribution, and the discount factor on the default risk, not only of a single bank, but also of the entire system. Additionally, the abovementioned properties and the relationship between risk distance and financial contagion indicate the rationality embodied in the proposed measurements. This paper also provides some implications on how to decrease or prevent the systemic risk in an interbank system.

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Network-based risk measurements for interbank systems

July Network-based risk measurements for interbank systems Yongli Li 0 Guanghe Liu 0 Paolo Pin Filippo Radicchi, Indiana University, UNITED STATES 0 School of Business Administration, Northeastern University , Shenyang , P.R.China , 2 Business School, Sun Yat-Sen University , Guangzhou , P.R.China , 3 Department of Decision Sciences, Innocenzo Gasparini Institute for Economic Research, and Bocconi Institute for Data Science and Analytics, University of Bocconi , Milano , Italy This paper focuses on evaluating the systemic risk in interbank networks, proposing a series of measurements: risk distance, risk degree and m-order risk degree. The proposed measurements are formally proven to have good basic and extended properties that are able to reflect the effect of bank size, liability size, liability distribution, and the discount factor on the default risk, not only of a single bank, but also of the entire system. Additionally, the abovementioned properties and the relationship between risk distance and financial contagion indicate the rationality embodied in the proposed measurements. This paper also provides some implications on how to decrease or prevent the systemic risk in an interbank system. - Data Availability Statement: All relevant data are within the paper. Note that this paper mainly focuses on the theoretical analysis and only some numerical examples are provided. All the data adopted in the numerical examples have been contained and reported in our paper, and thus no supporting information files are further needed in our opinion. 1 Introduction Since the Asian financial crisis of 1997, special attention has been paid to the role of the growing interconnectedness between financial institutions among the many factors that affect financial contagion [1,2]. Particularly after the global crisis of 2007?08, the architecture of financial system building on the abovementioned interconnectedness was viewed as being crucial for its central role in the financial contagion [3?5]. In fact, the abovementioned interconnectedness between financial institutes constitutes the edge of a financial network and the corresponding financial institutes are regarded as the nodes. Particularly, following numerous studies in this field such as [6] and [7], we also focus on the interbank system that can be considered as a fundamental structure for complex financial systems. Note that interbank borrowing and loans, if any, form the abovementioned interconnectedness that link the corresponding banks in the interbank network [8]. The network representation allows to study propagation of failures: recalling the two mentioned financial crisis, for example, one bank's insolvency may lead to the default cascades in the interbank network. Here, two periods are considered: several banks are assumed to default in the first period and the set of these banks is named initial default set, and then in the second period, some of the remaining banks may be induced to default because of the existing borrowing and loan links. Facing this phenomenon, we want to explore two problems. The first one is when one bank's insolvency occur, which bank will be the next victim? The second one is which initial default set will cause the largest Italian Ministry of Education Progetti di Rilevante Interesse Nazionale (PRIN) Grant 2015592CTH (to PP). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. amount of banks to default in the second period? This paper will inherit the idea of risk propagation in networks [9] to cope with the two problems by way of providing a series of convenient measurements. More concretely, if the answer of the first question is known in advance of financial contagion, we can inject liquidity into the more susceptive banks to avoid the spread of the crisis. As the first contribution, we provide a new measurement named risk distance, with the property that a shorter risk distance with the given initial default set means a higher likelihood of default. Among a growing literature on risk analysis in financial networks, the harmonic distance presented by [10] is noteworthy because it captures the susceptibility of each bank to the distress of any other, so that it functions similarly to our proposed risk distance. However, the risk distance that we define is different from the harmonic distance in two aspects: one is that our risk distance considers the cash and marketable assets carried by the banks so that the analysed banks can be heterogeneous, the other is that the risk distance defined here does not assume that the initial default set only contains a single bank. As a result, we prove that our newly proposed risk distance has several different properties with the famous harmonic distance in the following parts of this paper. Overall, our risk distance is a node-level (or say microscopic) indicator that reflects the default likelihood of the rema (...truncated)


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Yongli Li, Guanghe Liu, Paolo Pin. Network-based risk measurements for interbank systems, PLOS ONE, 2018, Volume 13, Issue 7, DOI: 10.1371/journal.pone.0200209