Global Banks and Systemic Debt Crises Juan Martin Morelli Pablo Ottonello Diego Perez New York University Michigan and NBER New York University September 24, 2019
Motivation • Emerging market debt crises are global in nature ◮ Affect multiple borrowing economies in synchronized fashion ◮ Involve the stability of global financial intermediaries (GFIs) • Policy circles: key role of GFIs shaping systemic debt crises • Most theories abstract from explicit GFIs This paper : Study role of GFIs in international lending • Model of the global economy + New empirical evidence
What We Do 1. Model of world economy ◮ Key ingredients: heterogeneous risky borrowers + GFIs ◮ Role of GFIs depends on degree of financial frictions 2. Empirical evidence: Effects of GFIs’ net worth in bond prices ◮ Exploit bond-level variation in prices during Lehman episode ◮ Larger price drops in EM bonds held by more affected GFIs 3. Relevance of GFIs in global debt market Quantitative analysis: ◮ Infer degree of financial frictions from empirical estimates Main results: ◮ Lenders are key for fluctuations in EM spreads & consumption ◮ GFIs’ exposure to EM debt and debt distribution matter
Outline 1. Model 2. Empirical Analysis 3. Quantitative Analysis
The Global Economy Emerging-Market Developed-Market Economies (EMs) Economies (DMs) • Risk-averse/impatient • Risk-neutral/patient Save in risk-free bonds Borrow without commitment • • • Tradable endowment + • Tradable endowment production technology Financing Risky Global Banks Lending Risky Lending • Financed with risk-free bonds + costly equity Invest in risky securities •
EMs: Environment � ∞ t =0 β t • Preferences of EM household i : E 0 EM u ( c it ) • Endowment: y EMt + z it � �� � ���� systemic idiosyncratic • If repays ( ι t = 1 ), EM can borrow and consumes c it = y EMt + z it + q i EMt ( b it +1 − ξb it ) − b it • If defaults ( ι t = 0 ), EM temporarily looses credit acces & consumes c it = H ( y EMt + z it ) H ( x ) ≤ x � � �� • Partial equilibrium ⇒ q i 1 + ξq i EMt = E m t +1 ι it +1 EMt ◮ This model: m t +1 depends on global banks and distribution of borrowing in world economy Recursive problem
DMs: Households & Non-Financial Firms DM Households • Linear preferences over consumption, inelastically supply labor 1 • Deposit rate R dt = β DM DM Firms • CRS technology using capital and labor, shocks to quality of capital • Expected mg. return DM capital E t R DM,t +1 = E t ω t +1 [ α ( K t +1 ) α − 1 + (1 − δ )]
Global Banks • Objective function ∞ � β s − t max E t DM π jt + s s =0 �� � � a i • Initial portfolio: i ∈I t − 1 , a DMjt − 1 , d jt − 1 EMjt − 1 • Net worth: � R i EMt q i EMt − 1 a i n jt = EMjt − 1 d i + R DMt q DMt − 1 a DMjt − 1 − R d d jt − 1 i ∈I t − 1 • Flow-of-funds constraint: � q i EMt a i EMjt d i + q DMt a DMjt + div jt = n jt + d jt i ∈I t � �� � � �� � � �� � purchase DM securities net worth + new deposits purchase EM securities Recursive problem
Global Banks • Financial frictions: ◮ Limited liability d jt ≤ κn jt � − div � ◮ Cost of raising equity: C ( div, n ) = φ if div < 0 n • Exit with prob 1 − σ , replaced by other entrant with initial n • Optimal portfolio choice ◮ Same expected required return in all risky assets R e EMit = R e DMt ≡ R e t where R e EMit ≡ E t [ v t +1 R i EMt +1 ] , R e DMt ≡ E t [ v t +1 R DMt +1 ] ◮ Under excess returns, deposits given by borrowing const: d jt = κn jt ◮ External finance: equity issuance increasing in required return � div jt � = β DM R e − 2 φ t − 1 n jt Equilibrium
Equilibrium in EM Debt Market Supply of funds A s t ( R e EMt , N t ) R e = N t (1 + κ ) + E ( R EMt , φ ) N t EM Demand � �� � � �� � of Funds Net worth Equity issuance + New Deposits − A DMt ( R EMt , α ) Supply � �� � of Funds Investment in DM Demand of funds A d t ( R e t ) = � A EM � � ι it +1 1 + ξq i b it +1 d EMt +1 R e i ∈I t Low Costs of Equity Issuance EMt
A Decrease in Global Banks’ Net Worth: Financial Frictions & Price Effects High Costs of External Finance Low Costs of External Finance R e R e EM EM Demand Demand of Funds of Funds Supply of Funds R 1 Supply EM R 1 of Funds EM R 0 R 0 EM EM A EM A EM ∆ N (1 + κ ) ∆ N (1 + κ )
Outline 1. Model 2. Empirical Analysis 3. Quantitative Analysis
EM Spreads & US Banks Net Worth 13 2.0 Russian/LTCM Lehman GreekDebt Beginning of the Crisis Bankruptcy Haircut Great Recession 1.5 11 1.0 9 0.5 0.0 7 -0.5 5 -1.0 -1.5 3 -2.0 1 -2.5 Sep-96 Jun-97 Mar-98 Dec-98 Sep-99 Jun-00 Mar-01 Dec-01 Sep-02 Jun-03 Mar-04 Dec-04 Sep-05 Jun-06 Mar-07 Dec-07 Sep-08 Jun-09 Mar-10 Dec-10 Sep-11 Jun-12 Mar-13 Dec-13 Sep-14 Sovereign EM spreads (left axis) Corporate EM spreads U.S. banks' n et Worth (right axis) • Key empirical challenge: ◮ DM shocks affecting GFIs correlated with drivers of EM default Countries included By country Historical default rates
Empirical Strategy • Exploit EM bond-level variation in prices in narrow window around the Lehman episode • Key idea: ◮ During Lehman’s episode GFIs experienced differential changes in their net worth primarily driven by DM factors ◮ Exposure of GFIs to EM debt small ◮ In narrow window: can exploit price differences for bonds with similar characteristics ⇒ Identification: Bonds w similar default risk but different holders
Connection Empirical Strategy & Model • Empirical strategy exploits price variation across EM bonds • In model: all EM bonds priced by aggregate net worth • Model extension with segmented markets Details ◮ Banks specialize in bond varieties (trading networks) ◮ Trade bonds with banks in the same network • Model extension features same price variation as empirical strategy • Elasticity arises due to both financial and trading frictions
Data Data: 1. EM bond prices ◮ Bond characteristics (maturity, liquidity, amount, currency) Selection criteria 2. Share of holdings by individual GFIs for each individual EM bond 3. GFIs’ stock prices around Lehman’s episode Global Banks List Key variables: 1. Bond yield-to-maturity y i 2. Change in GFIs’ avg net worth per bond around Lehman’s episode ∆ e i = � J j =1 θ ij ∆ e j
EM Bond Yields during Lehman Dispersion in Change in Yields ∗ Avg. Change in Yields .025 All bonds All bonds .006 Only Sovereign Only Sovereign .02 .004 .015 .002 .01 0 .005 -.002 -.004 0 -60 -40 -20 0 20 40 60 80 100 120 -60 -40 -20 0 20 40 60 80 100 120 Days (0=Lehman Bankruptcy) Days (0=Lehman Bankruptcy) ∗ Residualized std dev after controlling for bonds observed characteristics
Empirical Model Dynamic effects of global banks’ net worth on bond prices: ∆ h y i = α ksh + α ch + β h ∆ e i + γ ′ h X i + ε i • where ◮ y i : yield-to-maturity of bond i from country-sector k & curr c ◮ ∆ e i : average change in GFIs’ net worth ◮ α ksh : country by sector fixed effects Sector shares ◮ α ch : currency fixed effects • β h : elasticity of bond prices to changes in holders’ net worth • Additional controls: ◮ X i : maturity, liquidity, initial ytm, share of GFIs in holders
EM Bonds Held by More Distressed GFIs Experienced Larger Price Contractions 0 -.05 -.1 Beta -.15 -.2 -.25 -20 0 20 40 60 Days Post ∆ h y ikc = α kh + α ch + β h ∆ e i + γ h X i + ε ikc
Further Analysis • Results robust to alternative specifications Baseline Only Sov. Similar Maturity IV (1) (2) (3) (4) -0.013 ∗∗∗ Impact Effect -0.006 -0.008 0.0029 (0.004) (0.005) (0.018) (0.011) -0.136 ∗∗ -0.054 ∗∗ -0.252 ∗∗∗ -0.195 ∗∗∗ Peak Effect (0.053) (0.024) (0.065) (0.053) N Observations 402 198 70 108 Alt. windows No pre-trends Only Banks Exc. Mkt Makers Only Dollar Bonds • Results not driven by selection ◮ No sorting on observables within country-sector Sorting table By Country By Sector • Results do not capture reverse causality ◮ Low exposure of GFIs to EM debt GFIs Exposure ◮ IV estimates w/ share of bonds held by AIG as instrument
Outline 1. Model 2. Empirical Analysis 3. Quantitative Analysis
Solution and Calibration Strategy Solution method: • Agg+idiosync shocks → Krusell-Smith approach to approx debt distribution Detail Calibration Strategy: 1. Set subset of parameters to predetermined values Functional forms Parameter values 2. Calibrate subset of parameters to match key aggregates ◮ debt level (avg), default rate (avg), spread (avg, vol & cyclicality), portfolio of global banks (avg), global banks net worth (vol) Parameter values Calibrated moments Banks Balance Sheet
Mapping Model and Empirical Estimates • Effect in EM yields of a shock to ω leading to a contraction in GFIs net worth as in the Lehman episode 0.00 Baseline Model n baseline × 0.75 n baseline × 0.62 0.02 Data 0.04 0.06 EM 0.08 0.10 0.12 0.14 0 2 4 6 8 10 Cost of raising equity • Validation in model with secondary market Details
Recommend
More recommend
