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Discussion of Financial Innovation, Macroeconomic Stability and Systemic Crises

Discussion of Financial Innovation, Macroeconomic Stability and Systemic Crises. Arvind Krishnamurthy Northwestern University. Questions: Effects of financial innovation on macroeconomic stability. Systemic risk/crises. Costs and benefits of innovation Relevant questions

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Discussion of Financial Innovation, Macroeconomic Stability and Systemic Crises

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  1. Discussion ofFinancial Innovation, Macroeconomic Stability and Systemic Crises Arvind Krishnamurthy Northwestern University

  2. Questions: • Effects of financial innovation on macroeconomic stability. • Systemic risk/crises. • Costs and benefits of innovation • Relevant questions • Geithner quote about credit derivatives

  3. Outline • Paper writes down a stylized model to help think about these questions • My discussion • Explain the mechanics that drive the model • Externality/ policy application • Non-monotonic effect of maximum loan to value/ leverage ratio ( ) on probability of crises • How does the model shed light on the effect of innovations, such as credit derivatives

  4. Liquidity at date 1 • In order to avoid liquidation at date 1, intermediaries/firms need to raise at least ( - xs + b1s ) i0 xs < 0 is cash need for business operation b1s > 0 is debt repayment • Raise money in two ways: • Sell capital ( ) • Borrow against capital ( )

  5. Selling Capital: Fire Sales Raise ( - xs + b1s ) i0 by selling ks units at price q ks = ( - xs + b1s ) i0 / q q kD ()

  6. Fire Sales and Financial Depth () • Less financial depth: Demand curve not perfectly elastic • Multiple equilibria / liquidation externality • Policy: Role for the Fed LLR to rule out bad equilibrium? • Same downward sloping supply idea has been used to discuss Bank Runs, 1987 market crash, 1997 Asian Crisis, 1998 LTCM episode …

  7. Fire Sales and Financial Innovation ( ) • Constraint on raising debt: (at Date 0) b1s <  q1s (at Date 1) b2s <  q2s • Raising  loosens date 1 constraint, ks down • Raising  also allows for more borrowing at date 0: b1s up and i0 up Cash need is ( - xs + b1s ) i0

  8. Non-monotonicity

  9. Date 0 • Agents don’t internalize liquidity externality in date 0 decisions • Over-leveraging: too much i0 / too much b1s • Policy: Restrict ex-ante leverage

  10. Financial Innovation: Raising  • Raises probability of crises over part of the range • Improvements in automobile safety: • Air bags • Crumple zones • Anti-lock brakes • People drive faster … • Perhaps more accidents • But we may on net be better off

  11. Is Raising  beneficial for Welfare? • Something the paper should compute • Unclear, for two reasons • On average beneficial (borrowing at date 1) • Paper shows externality, but does not show whether externality increases with  • My guess: In model, the Date 1 effect dominates because it helps across all states of the world, whereas date 0 effects are only in the crisis/liquidation states.

  12. Raising : Crisis costs vs. average benefits • Conjecture: I think the model could say that an LLR can solve the date 1 externality, so that LLR plus  increase is beneficial • Possibly unambiguous in this case, which would give a different angle on the Fed’s role.

  13. Model and Reality • Financial innovation in the model is about changing  • Reality: options, credit derivatives, MBS, etc., • Problem model identifies is that agents over-leverage, not internalizing crisis costs • Reality: Risk management of financial firms. ``Insufficient” risk management. • Let us think about the risk management of a new financial asset, i.e. credit derivatives

  14. Financial Innovation is about the New • Financial innovations are complex even to sophisticated market participants • Risk management of an unknown product • Learning • Model risk • Interpreting and acting on outcomes that the model does not expect • Examples • 1987 Stock Market Crash • 1997 Asian Crises • 1998 LTCM Crisis

  15. 1987 Stock Market Crash • Insurance Strategies: Synthetic Puts/ Portfolio Insurance • Pre-1987: Compare implied volatilities on out-of-the-money put options to at-the money options (Bates 2000) • Out-of-the moneys have 3% higher implied vols • Post-1987: Same comparison • Out-of-the moneys have 10% higher implied vols • The crisis led agents to better understand the cost of crises

  16. 1997 Asian Crises • Integration into world capital markets: Foreign borrowing, capital inflows • Pre-1997: Liability composition tilted towards • Dollar-denominated debt • Short-term debt • Portfolio inflows • Post-1997: • Reserve accumulation • Long-term debt • FDI flows • Post-crises, countries adopted insurance strategies

  17. 1998 Hedge Fund Crisis • Hedge funds become the central liquidity providers in many specialized asset markets (MBS, sovereign debt, equity vol, …) • Pre-crisis risk management: Stress testing based on historical correlations • Crisis: Liquidity shortages cause unusual comovement. • Post-crisis risk management: Stress testing scenarios include liquidity events

  18. Credit Derivatives • Geithner (2006) quote that begins this paper • “…uncertainty about how exposures will evolve and markets will function in less favorable circumstances” • Example: GM and Ford downgrades from last year • What are the true exposures and how will markets play out?

  19. Average Benefits versus Crisis CostsModel and My Take on Reality • In the model, innovation is an increase in allowable leverage () • Financial innovation is about the new • Realized events do not span the entire event space • Uncertainty about outcomes • What is the best crisis risk management strategy? • Conjecture: Systemic risk is always in the new assets, and not in the old assets (i.e. portfolio insurance is well understood at this point).

  20. Average Benefits versus Crisis CostsModel and My Take on Reality • Date 1 policy in model: Eliminate liquidation equilibrium (role for an LLR) • I agree, but with a slightly different angle: • The liquidation externalities arise when bad outcomes are realized, and agents are not prepared • Perhaps also some “over-reaction” by agents • As in this paper: Role for an LLR

  21. Average Benefits versus Crisis CostsModel and My Take on Reality • Date 0 policy in model: Reduce over-leveraging • More generally, this translates to: Improve date 0 risk management • What is date 0? • Unclear if anyone (including the Fed) knows the best strategy

  22. Conclusion • The relevant calculation is average benefits versus crisis costs • There are liquidation externalities • Innovation is about the new…

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