Asset commonality, debt maturity and systemic risk

Allen, Franklin, Ana Babus, and Elena Carletti, 2012, “Asset commonality, debt maturity and systemic risk,” Journal of Financial Economics 104 (2012), 519-534.

Purpose:  To model a connection between the commonality of financial institutions’ (banks’) asset portfolios and the maturity of their debts.

Motivation:  The financial crisis starting in 2007 brought attention to the systemic risks stemming from linkages between the portfolios of financial institutions.  This paper investigates how debt maturity might cause such linkages to become a systemic risk.

Findings:

  • Long-term debt is not related to the riskiness of overlaps between institutions’ asset portfolios.
  • Clustered asset structures (where portfolios only overlap within a cluster) produce more systemic risk, and usually lead to lower total welfare.
  • Short-term debt can transfer insolvency between banks.
  • The use of short-term debt and the link between short-term debt and systemic risk, depend upon the structure of overlaps between banks’ portfolios (asset structure).

Model:  Six banks invest in six risky projects, exchange shares in their own project with others, and finance their investment with either short- or long-term debt in period 0.

  • The six banks exchange shares in each other’s projects to reduce risk, and do so in two patterns.
    • “Clustered” – two groups of three banks each exchange assets solely within the group, so that all banks within each group have identical portfolios.
      • In this structure, there is greater information spillover within groups, so trouble at one bank is most likely to also cause investors in the others not to roll over. Thus, there are more liquidations than in the unclustered group.
    • “Unclustered” – each bank exchanges only with the two neighboring banks, and no two banks have identical portfolios.
  • When banks use short-term debt, investors decide at the end of period 1 whether to roll over the banks’ debt based on a signal indicating whether at least one bank will fail.
  • Solvency or insolvency is determined at the end of period 2.
  • Investors’ failure to roll over causes early bank liquidation and is the source of systemic failure.

Conclusions:

  • When banks use short-term debt and have overlapping asset portfolios, the structure of the overlap is an important factor in determining systemic risk.

Challenges in Identifying and Measuring Systemic Risk

Hansen, Lars Peter, “Challenges in Identifying and Measuring Systemic Risk,” SSRN, February 14, 2014, http://ssrn.com/abstract=2158946.

Purpose & Motivation:  The “great recession” sparked immense interest in “systemic risk,” but that phrase is at risk of becoming a buzzword used to justify policy based on vague notions.  This essay offers perspective on the difficulty of quantifying systemic risk, or the connections between financial markets and the broader economy.

The Problem:  Before something can be rigorously discussed, debated, and analyzed, it must be measured.  The danger in allowing “systemic risk” to remain a qualitative label is that related policy cannot be based on hard data and informed debate.  Such policy is potentially harmful, and always difficult to criticize.

Challenges:  A lot of people are trying to quickly produce measures of systemic risk, to guide policy responses to the 2007-2009 recession.  While the hunt for numbers can be helpful, numbers without theory are difficult to apply.  We need economic theories that tell us which risk measurement techniques are useful for which purposes.

  • Challenge #1: Distinguishing “systematic” from “systemic”
    • “Systematic” risk is an investor’s exposure to macroeconomic shocks. It cannot be eliminated through diversification, and so demands a premium.
      • Macroeconomists try to identify shocks and propose policy that will mitigate their effects.
      • Finance research asks what premium is demanded by each element of systematic risk.
    • “Systemic” risk refers to the markets breaking down altogether. It has multiple interpretations:
      • A bank run – this is a concern of central banks in their position as “lenders of last resort”
      • The susceptibility of a network of financial institutions to shocks that start in one place and spread throughout – understanding this requires knowing which networks are risky and which shocks could start a fire
      • The insolvency of a major financial sector or institution
    • Challenge #2: Quantifying risk and uncertainty
      • Even the best models are inaccurate. Economists face uncertainty when they select between competing models, and agents within those models also face uncertainty.

Current Approaches to Measuring Systemic Risk:

  • Tail Measures look at the relation between the tails of financial institutions’ equity returns. This approach is plagued by the paucity of historical data, and ignores non-public institutions that may be important.
  • Contingent Claims Analysis uses option pricing techniques to value the debt (as a put option) and equity (as a call option) of an entire sector of the financial system. Development of this analysis requires better understanding of the overall market’s risk appetite and of the shocks that demand large risk premia.
  • Network Models look at the degree of interconnectedness between institutions. Rampant endogeneity complicates these models.
  • Dynamic, Stochastic Macroeconomic Models seem promising, but applications to systemic risk are still new.
  • Problems with data collection and sharing – researchers deal with confidentiality issues, biases in data collection, and limitations in the scope of publicly-available data.

Conclusions:  Attempts to examine systemic risk in the financial system face difficulties determining how to model the system, which data to use, and how to measure the data.  However, the high public interest in this topic and the general lack of good research present an alluring challenge.