Strongly stable networks

Jackson, Matthew O.; Nouweland, Anne van den

doi:10.1016/j.geb.2004.08.004

Cited by 218 publications

(64 citation statements)

References 18 publications

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“…Networks immune to such Pareto improving deviations are weakly Pareto efficient. We will be interested here in weakly Pareto efficient Nash networks and Pareto efficient Nash equilibria (PENE) -both notions are closely related to the notions of strong equilibria and strong stability introduced by Jackson and van den Nouweland (2005). In fact, in two-player games of network formation, the set of strong equilibria is the same as the set of weakly Pareto efficient Nash equilibria 4 .…”

Section: Rules Of Network Formationmentioning

confidence: 99%

Shadow Banks and Systemic Risks

Gong

Page

2015

SSRN Journal

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We answer the following question: Does regulating the banking network increase systemic risk in the entire financial network in the presence of unregulated shadow banks? In order to answer this question, we introduce a formal definition of systemic risk based on the equilibrium state dynamics generated by a stochastic game of network formation with banks and shadow banks. Based on the equilibrium dynamics, we discuss the properties of equilibrium financial networks. The predictions of the model are consistent with empirical evidence in the literature. Our analysis makes clear the systemic importance of connections between commercial banks and shadow banks in determining the health of the entire financial network. Lastly, but most importantly, we give a definition of systemic risk based on equilibrium dynamics, and therefore, a definition which takes into account the strategic interactions of financial institutions when facing different regulatory policies. Given our definition of systemic risk, we discuss various policies for regulating the financial system. Then, specializing our dynamic stochastic game model to a more basic model in which banks and shadow banks form a network including connections to the real economy, we show, via numerical simulations of our stochastic game model under various regulatory regimes, that imposing bank regulations not faced by shadow banks does indeed increase systemic risk throughout the financial network. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means without the prior permission in writing of the publisher nor be issued to the public or circulated in any form other than that in which it is published.Requests for permission to reproduce any article or part of the Working Paper should be sent to the editor at the above address. AbstractWe answer the following question: Does regulating the banking network increase systemic risk in the entire financial network in the presence of unregulated shadow banks? In order to answer this question, we introduce a formal definition of systemic risk based on the equilibrium state dynamics generated by a stochastic game of network formation with banks and shadow banks. Based on the equilibrium dynamics, we discuss the properties of equilibrium financial networks. The predictions of the model are consistent with empirical evidence in the literature. Our analysis makes clear the systemic importance of connections between commercial banks and shadow banks in determining the health of the entire financial network. Lastly, but most importantly, we give a definition of systemic risk based on equilibrium dynamics, and therefore, a definition which takes into account the strategic interactions of financial institutions when facing different regulatory policies. Given our definition of systemic risk, we discuss various policies for regulating the financial system. Then, specializing our dynamic stochastic game model to a more basic model in which banks ...

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Section: Rules Of Network Formationmentioning

confidence: 99%

Shadow Banks and Systemic Risks

Gong

Page

2015

SSRN Journal

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“…Hence, a legitimate counterargument to network analysis could be that such an approach is unnecessary since network characteristics are simply a direct consequence of conventional attributional data and therefore do not contain additional independent information. However, extensive work has gone into understanding the process of network formation and the conditions for the existence, uniqueness, and stability of network equilibria (see, for example, Bala and Goyal 2000;Jackson and Watts 2002;Jackson and van den Nouweland 2005), and one of the implications of this body of literature is that one-to-one correspondences between sets of unit-specific attributes and specific network structures may not exist (that is, not all tall children are considered popular, or height may not be as relevant an attribute in particular networks). Therefore, in most applications network analysis is bound to add value above and beyond what attributional data can tell us.…”

Section: Network Analysis and Market Prestigementioning

confidence: 99%

Prestigious stock exchanges: A network analysis of international financial centers

Cetorelli

Peristiani

2013

Journal of Banking & Finance

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractIn this paper, we use methods from social network analysis to assess the relative importance of financial centers around the world. Using data from virtually the entire universe of global equity activity, we present two sets of complete rankings for up to forty-five separate locations for the 1990-2006 period. The first phase of our analysis evaluates international stock exchanges based on their ability to attract global initial public offerings (IPOs). The second phase compares the capacity of these exchanges to provide an efficient trading platform for cross-listed companies. U.S. exchanges are effectively the unique hosts for cross-border equity activity originating from many other locations. Moreover, they are the destination of choice for most companies coming from locations with highly prestigious exchanges. We find that, despite a diminished ability to attract cross-border IPOs, U.S. exchanges have maintained an undisputable lead in global equity activity throughout the entire sample period. We do find evidence of the rising importance of competing exchanges-in particular, the London Stock Exchange, the Deutsche Börse, and the Hong Kong Stock Exchange-and also of an expanding role for a number of emerging market stock exchanges. However, this rising pattern reflects improved competitive conditions in a growing global market rather than a sudden decline in the activity of U.S. exchanges.

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“…The survey article in [11] presents the most notable works on network formation in cooperative games; furthermore, the existence of networks that are stable against changes in link choices by any coalition is studied in [13]. In [14], Andelman et al analyze strong equilibria with respect to players' scheduling as well as a different class of network creation games in which links may be formed between any pair of agents.…”

Section: Related Workmentioning

confidence: 99%

A Nash Bargaining Solution for Cooperative Network Formation Games

et al. 2011

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Abstract. The Network Formation problem has received increasing attention in recent years. Previous works have addressed this problem considering almost exclusively networks designed by selfish users, which can be consistently suboptimal. This paper addresses the network formation issue using cooperative game theory, which permits to study ways to enforce and sustain cooperation among agents. Both the Nash bargaining solution and the Shapley value are widely applicable concepts for solving these games. However, we show that the Shapley value presents three main drawbacks in this context: (1) it is non-trivial to define meaningful characteristic functions for the cooperative network formation game, (2) it can determine for some players cost allocations that are even higher than those at the Nash Equilibrium (i.e., if players refuse to cooperate), and (3) it is computationally very cumbersome. For this reason, we solve the cooperative network formation game using the Nash bargaining solution (NBS) concept. More specifically, we extend the NBS approach to the case of multiple players and give an explicit expression for users' cost allocations. Furthermore, we compare the NBS to the Shapley value and the Nash equilibrium solution, showing its advantages and appealing properties in terms of cost allocation to users and computation time to get the solution. Numerical results demonstrate that the proposed Nash bargaining solution approach permits to allocate costs fairly to users in a reasonable computation time, thus representing a very effective framework for the design of efficient and stable networks.

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Strongly stable networks

Cited by 218 publications

References 18 publications

Shadow Banks and Systemic Risks

Shadow Banks and Systemic Risks

Prestigious stock exchanges: A network analysis of international financial centers

A Nash Bargaining Solution for Cooperative Network Formation Games

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