Monday, June 24, 2013

Causes and Cures for Financial Contagion

Although it is intuitively clear that interconnectedness has some effect on the transmission of shocks, it is less clear whether and how it significantly increases the likelihood and magnitude of losses compared to a financial system that is not interconnected.

The Office of Financial Research, a government study group created by the Dodd-Frank Act, has released a study of factors that contribute to financial contagion. The paper, entitled “How Likely is Contagion in Financial Networks?" studies the interconnections between financial institutions and examines the conditions under which these interconnections increase and amplify financial shocks.

Using the European banking system as the model for their study, the OFR's study found that expected losses from network effects are small without substantial heterogeneity in bank sizes and a high degree of reliance on interbank funding. They are also small unless shocks are magnified by some mechanism beyond simple spillover effects, like bankruptcy costs, fire sales, and mark-to-market revaluations of assets.

Although there are a great many different lines of interconnections in the financial system, like networks defined through ownership hierarchies, payment systems, derivatives contracts, brokerage relationships, and correlations in stock prices, etc., the study's authors focused on the network defined by liabilities between financial institutions. They chose to study payment obligations channels because they believe that they create the most direct channel for the spread of losses.  As a "control group" by which to contrast the interconnected financial model, the study's authors rather ingeniously created a model financial system absent these liability interconnections.

To compare systems with and without interconnections, we proceed as follows. First, we define our nodes to be financial institutions that borrow and lend on a significant scale, which together with their obligations to one another constitute the financial network. In addition, such institutions borrow and lend to the nonfinancial sector, which is composed of investors, households, and nonfinancial firms. We compare this system to one without connections that is constructed as follows. We remove all of the obligations between the financial nodes while keeping their links with the nonfinancial sector unchanged. We also keep node equity values as before by creating, for each node, a fictitious outside asset (or liability) whose value equals the net value of the connections at that node that were removed.
OFR then applied the same set of test shock conditions to each model, the interconnected and the unconnected.
We then apply the same shock distributions to both systems, with the shocks to real assets originating in the external sector and the fictitious assets (if any) assumed to be impervious to shocks. We can ascertain how much the network connections contribute to increased defaults and losses by comparing the outcomes in the two systems.
From their analyses, two key findings emerge:

  • 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. (emphasis added)

  • 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. (emphasis added) 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.

The study adds some valuable data to our understanding of to what extent contagion contributed to the severity of the financial crisis, and under what conditions contagion might be most severe.  Losses in the network of payment obligations between banks and financial institutions apparently are less to blame for the contagion than the structure of the network, which may further be complicated by bankruptcy costs and feedback effects. The authors liken this to a slippery slope: "once some node suffers a deterioration in its balance sheet, its mark-to-market value decreases, which reduces the value of the nodes to which it has obligations, causing their balance sheets to deteriorate. The result can be a system-wide reduction in value that was triggered solely by a loss of confidence rather than an actual default." This latest work should be read closely by global policymakers and regulators as they try to inoculate they global financial system from the next potentially contagious shock.