2012 two key ndings emerge from this analysis first

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Unformatted text preview: iated and thus capture the possibility that institutions have portfolios that are exposed to common shocks (see for example Caccioli et al. 2012). Two key findings emerge from this analysis. First we compute the probability that default at a given node causes defaults at other nodes (via network spillovers), and compare this with the probability that all of these nodes default by direct shocks to their outside assets with no network transmission. We derive a general formula that shows when the latter probability is larger than the former, in which case we say that contagion is weak. A particular implication is that contagion is always weak unless there is substantial heterogeneity in node sizes as measured by their claims outside the financial sector. More generally, contagion will tend to be weak unless the originating node is large, highly leveraged, and – crucially – has a relatively high proportion of its obligations to other financial institutions as opposed to the nonfinancial sector. Second, the analysis shows that the total additional losses generated by network spillover effects are surprisingly small under a wide range of shock distributions for plausible values of model parameters. Both of these results are consistent with the empirical and simulation literature on network stress testing, which finds that contagion is quite difficult to generate through the interbank spillover of losses (Degryse and Nguyen 2004, Elsinger, Lehar, and Summer 2006, Furfine 2003, Georg 2011, Nier et al. 2007). Put differently, our results show that contagion through spillover effects becomes most significant under the conditions described in Yellen (2013), when financial institutions inflate their balance sheets by increasing leverage and expanding interbank claims backed by a fixed set of real assets. These results do not imply that all forms of network contagion are unimportant; rather they show that simple spillover or “domino” effects have only a limited impact at realistic levels of payment obligations between banks. This leads us to examine other potential sources of contagion. The prior literature has focused on the role of fire sales, that is, the dumping of assets on the market in order to cover losses.3 Here we shall focus on two alternative mechanisms that are more immediate extensions of the simple spillover mechanism in the Eisenberg-Noe model and thus allow a more immediate comparison: bankruptcy costs and losses of confidence. Bankruptcy costs magnify the costs associated with default both directly, through costs like legal fees, and indirectly through delays in payments to creditors and disruptions to the provision of financial intermediation services necessary to the real economy. We model these effects in reduced form through a multiplier on losses when a node defaults. This approach allows us to estimate how much the probability of contagion, and the expected losses induced by contagion, increase as a function of bankruptcy costs. A somewhat surprising finding is that bankruptcy costs must be quite large in order to have an appreciable impact on expected losses as they propagate through the network. A second mechanism that we believe t...
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This document was uploaded on 02/20/2014 for the course ECON 101 at Pontificia Universidad Católica de Chile.

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