Nobel Symposium 2018: Money and Banking
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1 Nobel Symposium 2018: Money and Banking Markus K. Brunnermeier Princeton University Stockholm, May 27 th 2018
2 Types of Distortions Belief distortions Match belief surveys (BGS) Incomplete markets natural leverage constraint (BruSan) Costly state verification (BGG) + Leverage constraints (no liquidity creation ) Exogenous limit (Bewley/Ayagari) Collateral constraints Next period s price RRbb tt qq tt+1 kk tt Next periods volatility Current price Search Friction (KM) (VaR, JG) (DGP) state 2 state 1 Debt limit can depend on prices/volatilit 2
3 Overview Types of distortions/frictions Run-up phase Distorted beliefs Concentration of risk Crash phase Fire-sales Paradox of Prudence Spillovers Recovery phase Endogenous volatility dynamics volatility paradox Welfare/Regulation 3
4 Run-up 1: Beliefs Distortions Representativeness heuristic/diagnostic beliefs: As if error terms followed AR(1) Overestimate of productivity after good shock overshooting Bubbles/overinvestment driven by level of beliefs a la Miller (1977) AS: Surveys consistent with each other, mutual fund flows tt Local thinking neglect of tail risk VaR Heterogeneous beliefs: optimists and pessimists + limited commitment Leverage cycle Marginal buyer vary with shocks Surveys elicit consensus beliefs marginal buyer s beliefs Switching heterogeneous beliefs Speculation (Resale option a la Harrison-Kreps/Scheinkman-Xiong): optimist/pessimist switching + short-sale constraint Bubbles, volatility, and trading volume 4
5 Run-up 1: Beliefs Distortions Representativeness heuristic/diagnostic beliefs: As if error terms followed AR(1) Overestimate of productivity after good shock overshooting Bubbles/overinvestment driven by level of beliefs a la Miller (1977) AS: Surveys consistent with each other, mutual fund flows tt Local thinking neglect of tail risk VaR Heterogeneous beliefs: optimists and pessimists + limited commitment Leverage cycle Marginal buyer vary with shocks Surveys elicit consensus beliefs marginal buyer s beliefs Switching heterogeneous beliefs Speculation (Resale option a la Harrison-Kreps/Scheinkman-Xiong): optimist/pessimist switching + short-sale constraint Bubbles, volatility, and trading volume VaR 5
6 Run-up 1: Beliefs Distortions Representativeness heuristic/diagnostic beliefs: As if error terms followed AR(1) Overestimate of productivity after good shock overshooting Bubbles/overinvestment driven by level of beliefs a la Miller (1977) AS: Surveys consistent with each other, mutual fund flows tt Local thinking neglect of tail risk VaR Heterogeneous beliefs: optimists and pessimists + limited commitment Leverage cycle Marginal buyer vary with shocks Surveys elicit consensus beliefs marginal buyer s beliefs Switching heterogeneous beliefs Speculation (Resale option a la Harrison-Kreps/Scheinkman-Xiong): optimist/pessimist switching + short-sale constraint Bubbles, volatility, and trading volume VaR 6
7 Run-up 1: Beliefs Distortions Representativeness heuristic/diagnostic beliefs: As if error terms followed AR(1) Overestimate of productivity after good shock overshooting Bubbles/overinvestment driven by level of beliefs a la Miller (1977) AS: Surveys consistent with each other, mutual fund flows tt Local thinking neglect of tail risk VaR Heterogeneous beliefs: optimists and pessimists + limited commitment Leverage cycle Marginal buyer vary with shocks Surveys elicit consensus beliefs marginal buyer s beliefs Switching heterogeneous beliefs Speculation (Resale option a la Harrison-Kreps/Scheinkman-Xiong): optimist/pessimist switching + short-sale constraint Bubbles, volatility, and transaction volume VaR 7
8 Run-up 2: Concentration of Risk risk HH Experts 8
9 Run-up 2: Concentration of Risk risk Belief extrapolation: No risk concentration necessary HH Experts Financial frictions models: Experts hold most of aggregate risk in good times Low volatility, but risk builds up in background Credit cycle: (BGG/KM/BruSan) Experts save their way out of constraint after string of good shocks Buffer against crisis Leverage cycle: (JG/BruPed) extreme leverage in cts. time limit Most concentrated risk after string of good shocks 2 key difference (besides hetero beliefs): More than two groups Bubble don t burst, but deflate Worst case moves up Higher debt financing 9
10 Run-up 2: Concentration of Risk risk Belief extrapolation: No risk concentration necessary HH Experts Financial frictions models: Experts hold most of aggregate risk in good times Low volatility, but risk builds up in background Credit cycle: (BGG/KM/BruSan) Experts save their way out of constraint after string of good shocks Buffer against crisis Leverage cycle: (JG/BruPed) extreme leverage in cts. time limit Most concentrated risk after string of good shocks 2 key differences (besides hetero. beliefs): More than two groups Bubble don t burst, but deflate Worst case moves up Higher debt capacity 10
11 Crash 1: Fire Sales Definition: Assets transferred to second-best users BGG: No second-best user KM: Negative shock Experts sell to HH (gatherers), Market illiquidity Shleifer-Vishny (1992): Fire sales GE debt capacity matters Restructuring > Fire sales Are fire-sales good or bad? BruSan: Two competing effects Ex-post: Fire sales stabilize economy in crisis, but misallocation Ex-ante: Lead to excessive leverage (fire-sale/pecuniary externality) (Stein et al., ) 11
12 Crash 2: Paradox of Prudence Micro-prudence of bank is macro-imprudent Two spirals amplify Liquidity spiral (price of capital) Disinflationary spiral (price of money) Banks issue less inside money (& diversify less risk risk) HH demand more money
13 Crash 2: Paradox of Prudence Micro-prudence of bank is macro-imprudent Two spirals amplify Liquidity spiral (price of capital) Disinflationary spiral (price of money) Banks issue less inside money (& diversify less risk risk) HH demand more money
14 Crash 2: Paradox of Prudence Micro-prudence of bank is macro-imprudent Two spirals amplify A A A Money BB 1 AA 1 Liquidity spiral (price of capital) Disinflationary spiral (price of money) Banks issue less inside money (& diversify less risk risk) HH demand more money A Inside equity L L Risky Claim Risky Claim L Outside Money Risky Claim Deleveraging Risky Claim Risky Claim Deleveraging Pass through Inside Money Inside Money (deposits) (deposits) Net worth Losses BruSan The I Theory of Money L A A A A Lower inflation Money AA 1 L HH Net worth L L 14 L
15 Crash 3: Spillovers Across Assets Belief extrapolation: No spillovers Unless baked-in in beliefs Net worth channel: BGG/KM/BruSan: Expert net worth affects all assets Diamond-Rajan (2005) JG-Leverage cycle: Spillovers from crossover investors Margins spike in one market Crossover investors transfer capital from other markets BruPed: Multiple equilibria: Joint jump in price across assets Even assets with uncorrelated payoffs jump together Could also be integrated in a DD-model Measurement: CoVaR 15
16 Speed of Recovery Speed of Recovery KM: deterministic BruSan: Length of recession is stochastic precautionary savings 16
17 Mean & Leverage Dynamics Impulse Response curves: Amplification Credit cycle: (Loss spiral) Constant volatility exog. shocks Countercyclical leverage Underinvestment (second best user problem) Leverage cycle: (Margin spiral/repo run) Exogenously time-varying volatility ARCH/Scary bad news Destabilizing Margins Pro-cyclical leverage Evidence: Pro- vs. countercyclical leverage depends on investor type, book vs. market, new vs. average 17
18 Mean & Leverage Dynamics Impulse Response curves: Amplification Credit cycle: (Loss spiral) Constant volatility exog. shocks Countercyclical leverage Underinvestment (second best user problem) Leverage cycle: (Margin spiral/repo run) Exogenously time-varying volatility ARCH/Scary bad news Destabilizing Margins Pro-cyclical leverage Evidence: Pro- vs. countercyclical leverage depends on investor type, book vs. market, new issuance vs. overall 18
19 Endogenous Volatility & Volatility Paradox Endogenous Risk/Volatility Dynamics in BruSan Beyond Impulse responses Input: Output: constant volatility endogenous risk time-varying volatility Precautionary savings Role for money/safe asset Stochastic steady state Nonlinearities in crisis endogenous fait tails, skewness Volatility Paradox Low exogenous (measured) volatility leads to high build-up of (hidden) endogenous volatility (Minksy) 19
20 Financial Regulation/Welfare Criterion Important macro-prudential tools: Countercyclical buffer, liquidity regulation, LTV, DTI, spillover metric, Belief extrapolation (mean dynamics): Paternalistic: lean against price movements (all the time) Heterogeneous beliefs JG: Financial innovation causes boom & bust (no welfare loss risk-neutral) Speculation: Tobin tax insufficient? Welfare criterion (BSX) Fire-sale externality/spillovers (CoVaR measure) Loosen borrowing constraints in bad times (BGG/KM) Also control concentration of risk in good times (JG/BruSan) 20
21 Conclusion Run-up, Crisis, and Recovery -mechanisms Belief-focused (representative + heterogeneous) Friction-focused, where risk is central Risk concentration, fire-sales, spillovers, Paradox of Prudence Volatility Paradox Mean-Amplification, Exog. ARCH, Endog. Volatility Dynamics Macro/Monetary models with financial sector should include physical investment inside money creation
22 Extra Slides
23 Recovery from Crisis/Resilience Belief extrapolation: Recovery speed determined by belief persistence Perceived AR(1) coefficient on errors BGG/KM: Recovery in tandem with experts balance sheets JG: Recovery with experts balance sheets & disagreement/volatility Less disagreement Lower margins Higher marginal buyer BruSan: Length of recovery is stochastic additional precautionary savings 24
24 Extra Slide: Pro- vs. Counter-cyclical Leverage Adrian-Shin (2014): Book vs. market leverage Intermediaries finance new assets with debt Procyclical Geanakoplos-Pedersen (2014): New vs. old leverage Margins spike in crisis Procyclical He, Kelly, Manela (2017): Different constraints Equity constraint : BGG/BruSan, countercyclical leverage Debt constraint : Leverage cycle, procyclical leverage Book/market leverage positively correlated for dealers Evidence from HFs in Ang et al. (2011) HFs procyclical, investment banks countercyclical 25
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