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Statistics & Risk Modeling

with Applications in Finance and Insurance

Editor-in-Chief: Stelzer, Robert

4 Issues per year


SCImago Journal Rank (SJR) 2015: 0.105

Mathematical Citation (MCQ) Quotient 2014: 0.14

Online
ISSN
2196-7040
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Volume 31, Issue 1 (Mar 2014)

Issues

Optimal control of interbank contagion under complete information

Andreea Minca
  • Corresponding author
  • School of Operations Research and Information Engineering, Cornell University, Ithaca, NY 14850, USA
  • Email:
/ Agnès Sulem
  • INRIA Paris-Rocquencourt, Domaine de Voluceau, Rocquencourt, BP 105, Le Chesnay Cedex, 78153, France, and Université Paris-Est, 77455, Marne-la-Vallée, France
  • Email:
Published Online: 2014-03-28 | DOI: https://doi.org/10.1515/strm-2013-1165

Abstract

We study a preferred equity infusion government program set to mitigate interbank contagion. Financial institutions are prone to insolvency risk channeled through the network of interbank debt and to funding liquidity risk. The government seeks to maximize, under budget constraints, the total net worth of the financial system or, equivalently, to minimize the dead-weight losses induced by bank runs. The government is assumed to have complete information on interbank debt. The problem of quantifying the optimal amount of infusions can be expressed as a convex combinatorial optimization problem, tractable when the set of banks eligible for intervention (core banks) is sufficiently, yet realistically, small. We find that no bank has an incentive to withdraw from the program, when the preferred dividend rate paid to the government is equal to the government's outside return on the intervention budget. On the other hand, it may be optimal for the government to make infusions in a strict subset of core banks.

Keywords: Systemic risk; liquidity risk; bank runs; financial contagion; financial networks; optimal intervention; bail-outs

AMS (2010): 91B30; 91G50; 90B15; 90B50; 90B10; 91B15

About the article

Accepted: 2013-12-27

Received: 2013-10-10

Published Online: 2014-03-28

Published in Print: 2014-03-28


Citation Information: Statistics & Risk Modeling, ISSN (Online) 2196-7040, ISSN (Print) 2193-1402, DOI: https://doi.org/10.1515/strm-2013-1165. Export Citation

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