Banks as Patient Fixed Income Investors

Hanson, Samuel G., Andrei Schleifer, Jeremy C. Stein, and Robert W. Vishny, “Banks as Patient Fixed Income Investors,” American Finance Association 75th Annual Meeting, Boston (2015).

Purpose:  To introduce a model explaining why asset holdings differ between traditional banks and so-called “shadow banks.”

Findings:

  • Traditional banks have more market share in assets that are illiquid and that are fundamentally safe but have high intertemporal volatility.
    • business loans, MBSs, etc.
  • So-called shadow banks hold assets that are either highly liquid or that have low volatility.
    • equities, Tresauries, etc.

Motivation:

  • Commercial banks’ liability mix is highly homogeneous (mostly customer deposits), both in the cross section and in the time series.
  • Bank assets are far more heterogeneous.
  • Banks’ scale seems to be driven by their ability to attract deposits (liabilities), rather than by their investment opportunities (assets).
  • Banks’ asset portfolio does not appear to be a liquidity buffer; rather, banks hold few Treasury securities and instead hold assets earning a (riskier) spread over Treasuries.
  • Bank risk-taking is not likely to be the result of moral hazard due to deposit insurance.

Model:

  • There are N risky assets
    • Assets are perfectly correlated, differing by their payoff in the bad state of the world.
  • The model has three actors: households, banks, and shadow banks.
    • Households are risk neutral, and invest in bank claims but do not own assets.
    • Banks invest in assets and issue claims on these assets to households. Intermediation is necessary to create safe claims, as no asset is risk-free.
  • The model has three time periods:
    • At time t=0, banks invest in risky assets and issue claims to households.
    • At time t=1, bad news arrives with probability 1-p. Shadow banks must sell assets for less than fundamental value, but traditional banks are not forced to sell.
    • At time t=1, payoffs to asset holdings and household claims are realized. If bad news arrived at time t=2, then the bad state occurs with probability 1-q.
  • Traditional bank intermediation
    • Traditional banks hold assets to maturity, and create safe assets by only issuing claims equal to their asset portfolio payoff in the bad state. The rest of their asset portfolio is financed using costly equity.
  • Shadow bank intermediation
    • A shadow bank is a dual institution–a highly-leveraged institution (HL) and a money-market fund (MMF).
    • The HL purchases risky assets and enters a short-term repo agreement with the MMF.
    • The MMF creates safe assets through its ability to seize the HL’s assets and sell them at fire-sale prices if bad news occurs.

Results:

  • In an interior equilibrium where both types of banks hold an asset type, the marginal benefit of stable funding (the avoidance of fire-sale losses) equals the marginal cost of stable funding (limits on the amount of claims that can be created—money creation).
  • Corner solutions are also possible, where an asset type is held exclusively by one type of institution.
  • Traditional bank asset ownership increases in asset illiquidity and in expected bad-state payoff.
    • High asset illiquidity and bad-state asset payoff diminishes the money-creating advantage of shadow banks.
    • Equities are not suitable for traditional bank ownership (bad-state payoffs are too low) but are suitable for shadow bank ownership (liquidity is high).
  • An increase in the premium households pay for safe assets lowers traditional bank ownership for all (risky) assets.
  • A decrease in the probability of bad news lowers traditional bank ownership for all assets.

Empirical Evidence

  • Banks with stickier liabilities hold more illiquid assets.
    • “Sticky” liabilities mean that depositors are less likely to withdraw them upon bad news.
  • Banks hold very little of either Treasury securities or equities.

The “Greatest” Carry Trade Ever? Understanding Eurozone Bank Risks

Acharya, Viral V. and Sascha Steffen, “The “Greatest” Carry Trade Ever?  Understanding Eurozone Bank Risks,” The American Finance Association 75th Annual Meeting, Boston (2015).

Purpose:  To investigate the risks assumed by Eurozone banks as a form of carry trade, where banks loaded positively on GIPSI (Greece, Ireland, Portugal, Spain, and Italy) bonds and negatively on German government bonds.

Findings:

  • Banks’ stock returns were positively correlated with GIPSI bond returns, and negatively correlated with German bond returns, for the period January 2007 to June 2012.
  • Banks were effectively financing long-term GIPSI bond holdings with short-term German debt.
  • This carry trade behavior was more notable at large banks, banks with lower capital ratios, and banks with riskier asset portfolios.
  • Banks increased the magnitude of their carry trade between March and December 2010, ruling out the possibility that they were passively caught up in the crisis.
  • Banks’ carry trade exposure was related to actual bond holdings rather than to holdings of other asset classes.
  • Both GIPSI and non-GIPSI banks were involved, so the carry trade is not a case of GIPSI banks suffering from the weakness of their home countries.
  • Regulatory arbitrage was a likely contributor to the carry trade of both GIPSI and non-GIPSI banks.
    • Governments had incentives to maintain the Basel II zero risk-weighting on sovereign bonds so they could continue to borrow.
    • Banks with low capital ratios were incentivized to buy these zero risk-weighted sovereign bonds.
  • Risk-shifting by GIPSI banks may also have been a contributor to the carry trade of GIPSI banks.
    • An Italian bank, say, might have wanted to shift risk to a state of the world where they would be in trouble anyway (an Italian default) by buying Italian bonds.
  • Moral hazard may have also played a role, where banks in strong sovereigns might assume the risk of the carry trade with an implicit expectation of being bailed out in a worst-case scenario.
  • Moral suasion may have occurred where weak sovereigns convinced their home banks to buy own-sovereign debt.
  • U.S. money-market funds played an important role in providing or not providing liquidity to European banks.
    • After November 2010, they withdrew 60% of their investment in weakly capitalized banks and doubled their investments in well-capitalized banks.

Data:

  • Data on stock prices, bond yields, and CDS credit spreads comes from Bloomberg.
  • Banks’ portfolio holdings data is from the European Banking Authority (EBA).

Methods:

  • Regress each bank’s daily stock returns on the daily returns of 10-year GIPSI government bonds (all five countries), the daily return on 10-year German government bonds, and the daily return of bank’s home equity market (orthogonalized to the sovereign bond returns of Germany and of the home country).
    • \beta_{Greece} estimates each bank’s exposure to Greek sovereign debt, etc.
    • \beta_{Germany} is the estimate of each bank’s exposure to German bonds (a negative value indicates the bank is “short” German government debt).
  • Estimate the factor loadings quarterly.

Conclusions:

  • European regulators should not consider the sovereign debt of all Euro countries risk-free (or even high-liquidity).
  • ECB lending should probably be used to recapitalize banks rather than to guarantee their debt and encourage them to increase asset exposures.
    • While this props up the financial markets, especially in the banks’ home countries (due to home bias), it potentially makes a future sovereign debt crisis even more dangerous.
  • When banks hold long-term risky assets and rely on short-term funding, they are likely to encounter liquidity shortages.

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.