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.

The High-Frequency Trading Arms Race: Frequent Batch Auctions as a Market Design Response

Budish, Eric, Peter Cramton, and John Shim, “The High-Frequency Trading Arms Race:  Frequent Batch Auctions as a Market Design Response,” SSRN, December 23, 2013, <http://ssrn.com/abstract=2388265>.

Purpose:  To argue that the continuous limit order book design of current securities markets is socially wasteful, reduces trading depth (large trades are less available), and increases trading spreads; and to propose batch auctions as a better market design.

Motivation:  High-frequency trading firms (HFTs) spend hundreds of millions of dollars to increase their communication speeds with financial markets by just a few thousandths of a second.  Is this “arms race” a healthy competition, or does it reveal a flaw in the design of our financial markets?

Findings:

  • Very high correlations that are observed between securities traded on different exchanges break down over very short time intervals (a few milliseconds).
  • Arbitrage opportunities available to the fastest traders may amount to billions of dollars annually.
  • Time horizons for HFT have shrunk over time, but competition has not reduced the size of the opportunity
  • The continuous limit order book (first come, first served) market design increases bid-ask spreads and penalizes liquidity providers who would offer large trades, thus keeping markets thin
  • The arms race hurts social welfare by incentivizing investment in expensive high-speed technology
  • Frequent batch auctions (such as once per second) would eliminate these shortcomings
    • By greatly reducing the advantage of high speed
    • By forcing HFTs to compete on price instead of on speed alone
  • Batch auctions may also improve both market stability and regulators’ ability to oversee trading activity

Data/Methods:

  • Direct-feed data is from the NYSE and the CME for the period 1/1/2005 to 12/31/2011, including all activity on the exchanges’ limit order books with millisecond-level time stamps. This is the same data that HFTs use.
    • Compute correlation between % changes in the bid-ask midpoints of highly correlated securities.
    • Calculate arbitrage profits by assuming the ability to instantaneously trade and summing the profits from arbitrage opportunities over a period.
  • Model: investors, (quantity) N HFTs, and security x perfectly correlated (with latency) with a public signal y.
    • 1 HFT acts as the “liquidity provider” and N-1 act as “stale-quote snipers.”
    • When the public signal moves, the liquidity provider adjusts its prices, and the snipers simultaneously try to buy or sell at the old prices; snipers are successful with probability 1/N.
    • The liquidity provider builds the probability of getting sniped into its bid-ask spreads.

Conclusions:

  • In equilibrium, the cost of HFTs investments in speed, the total profits to be made by HFTs’ technical arbitrage, and the revenue extracted from investors by the liquidity provider’s bid-ask spreads are all equal.
  • In the model, a positive bid-ask spread exists even in the case of perfect information, so investors lose out.
  • Prisoners’ Dilemma: HFTs would be better off mutually agreeing not to invest in speed
  • Both empirically and theoretically, the size of the arms-race price does not depend on the speed of HFTs.
  • The HFT arms race hurts both investors and society, and could be corrected by moving to batch auctions.

 

The figure below plots the time series of the bid-ask midpoints of two highly correlated securities:  the E-mini S&P 500 future (ES, in blue) and SPDR S&P 500 ETF (SPY, in green).  The series are shown for an ordinary trading day (08/09/2011), using four different time horizons.  Note the very high correlation between the lines in (a) and the low correlation in (d).  At 10 milliseconds, there is virtually zero correlation.  For details, see original paper.

HFT correlation