Credit Risk and the Macroeconomy

Transcription

1 and the Macroeconomy Evidence From an Estimated Simon Gilchrist 1 Alberto Ortiz 2 Egon Zakrajšek 3 1 Boston University and NBER 2 Oberlin College 3 Federal Reserve Board XXVII Encuentro de Economistas Banco Central de Reserva del Perú Noviembre 2009

2 Macroeconomic-Financial Linkages The information content of credit spreads for the state of the economy suggests important linkages between the financial sector and the real economy. Quantify the importance of financial shocks in U.S. business cycle fluctuations. Construct financial series from security-level data. Use this information to estimate a DSGE model (CEE/SW) with the financial accelerator mechanism emphasized by BGG.

3 Preview of the Results Medium-risk long maturity (M-R/L-M) credit spreads have substantial predictive power for economic activity Estimation of a DSGE model that uses financial information to identify financial market distortions implies important role for credit-supply shocks for investment and output at business cycle frequencies.

4 Data Sources Introduction Credit Spreads: prices of outstanding corporate bonds traded in the secondary market (Lehman/Warga & Merrill Lynch): Sample period: Jan1973 Mar U.S. (nonfarm) nonfinancial issuers 5,661 senior unsecured issues (364,403 bond/month observations) Spreads relative to yields on comparable-maturity Treasuries : Merton (1974) distance-to-default (DD) model

5 Selected Corporate Credit Spreads (1973:Q1 2009:Q1) Quarterly Basis points 600 NBER Peak Medium-Risk Long-Maturity Baa Q

6 Full Sample (1973:Q1 2009:Q1) Comparison of Out-of-Sample Predictive Accuracy (Sample period: 1973:Q1 2009:Q1; 4-quarter forecast horizon) Specification 1: term spread, real funds rate, Baa-Treasury spread Specification 2: medium-risk, long-maturity credit spread Jump-off point: 1989:Q4 Economic Activity Indicator RMSFE-1 RMSFE-2 Ratio Pr > S Private Payroll Employment Unemployment Rate Mfg. Industrial Production Real Business Inventories Real GDP Real Business Fixed Investment

7 s with Financial Frictions Financial market imperfections imply that borrowers face an external finance premium (EFP) when trying to raise funds in capital/credit markets. EFP depends inversely on agents net worth. EFP is larger for agents subject to greater agency problems. The presence of EFP gives rise to the financial accelerator mechanism: During an economic downturn, asset prices fall and quality of borrowers balance sheets deteriorates. Worsening of borrowers balance sheet conditions causes premiums on the various forms of external finance to increase. As EFP jumps, agents cut back on their spending, causing asset prices to drop further, thereby amplifying the economic downturn.

8 Related Literature Introduction Christensen and Dib (2008): Provide quantitative evidence in favor of a financial accelerator mechanism. Queijo von Heideken (2008): Presence of financial frictions improves the fit of the model for both the U.S. and Euro area. De Graeve (2008): Recovers the model-implied EFP and relates it to measures of credit availability (i.e., credit spreads, bank lending standards). Christiano, Motto & Rostagno (2009): Demonstrate that shocks to the financial sector have played an important role in cyclical fluctuations in both the U.S. and Euro area.

9 Estimated Smets & Wouters (2007); Christiano, Motto & Rostagno (2009) Key features of the model: Habit formation in consumption. Higher-order adjustment costs to investment. Shocks to investment technology. Variable capacity utilization. Calvo-style price rigidities with partial indexation New Keynesian Phillips curve. Calvo-style wage rigidities with partial indexation. Nominal interest rate rule responds to inflation, output gap, and output growth.

10 Implementation Introduction Use medium-risk long-horizon credit spreads to measure external finance premium: s t = χ(q t + k t n t ) + ε fd t, χ > 0 n t = K ( ) K N rk t N 1 (s t 1 + r t 1 π t ) + θn t 1 + ε nw t z t q t + k t n t = log of leverage ratio Allow for credit-supply shocks: ε fd ε nw t t : disturbances to credit intermediation : disturbances to asset values that serve as collateral

11 Leverage in the U.S. Nonfinancial Corporate Sector (1973:Q1 2009:Q1) Quarterly NBER Peak Ratio 4.0 Q

12 Shock Processes Introduction 9 structural shocks: Preferences: ε b t = ρ b ε b t 1 + η b t Technology: ε a t = ρ a ε a t 1 + η a t Government Spending: ε g t = ρ g ε g t 1 + ηg t + ρ gaη a t Price Mark-Up: ε p t = ρ p ε p t 1 + ηp t µ pη p t 1 Wage Mark-Up: ε w t = ρ w ε w t 1 + η w t µ w η w t 1 Monetary Policy: ε r t = ρ r ε r t 1 + ηt r Investment-Specific Technology: ε qs t = ρ qs ε qs t 1 + ηqs t Credit Spread: Net Worth: ε fd t ε nw t = ρ fd ε fd t 1 + ηfd t = ηt nw

13 Data Introduction 9 macroeconomic/financial series: GDP t = real GDP per capita CONS t = real PCE per capital (nondurable goods & services) INV t = real investment per capita (private fixed investment & durable goods PCE) W t = real wage per capita (nonfarm business sector) HRS t = average hours worked per capita (nonfarm business sector) P t = GDP price deflator FF t = effective federal funds rate LEV t = average leverage (nonfinancial corporate sector) CS t = M-R/L-M credit spread Sample period: 1973:Q1 2009:Q1

14 Contractionary Monetary Policy Shock (1-standard-deviation shock to federal funds rate) Output 0.1 Consumption 0.05 Investment Hours worked 0.05 Wages Inflation Federal funds rate 1.0 Leverage 0.8 Credit spread

15 Adverse Credit Spread Shock (1-standard-deviation shock to credit spreads) Output 0.05 Consumption 0.04 Investment Hours worked 0.04 Wages 0.01 Inflation Federal funds rate 0.02 Leverage 0.4 Credit spread

16 Adverse Net Worth Shock (1-standard-deviation shock to net worth) Output -0.1 Consumption 0.5 Investment Hours worked 0.1 Wages 0.1 Inflation Federal funds rate 0.2 Leverage 10 Credit spread

17 Horizon Net Worth Fin. Distress Investment Monetary Price markup Wage markup Technology Preference Government Output Growth 1Q Q Q Q Q Asymptotic Consumption Growth 1Q Q Q Q Q Asymptotic Investment Growth 1Q Q Q Q Q Asymptotic

18 Horizon Net Worth Fin. Distress Investment Monetary Price markup Wage markup Technology Preference Government Hours 1Q Q Q Q Q Asymptotic Wage Growth 1Q Q Q Q Q Asymptotic Inflation 1Q Q Q Q Q Asymptotic

19 Horizon Net Worth Fin. Distress Investment Monetary Price markup Wage markup Technology Preference Government Interest rate 1Q Q Q Q Q Asymptotic Leverage 1Q Q Q Q Q Asymptotic External Finance Premium 1Q Q Q Q Q Asymptotic

20 Decomposition of Investment Growth (Percentage point deviation (annual rate) from steady state) Financial Price mark-up Preference Investment Wage mark-up Government -40 Monetary Technology Data

21 Decomposition of Consumption Growth (Percentage point deviation (annual rate) from steady state) Financial Investment Price mark-up Wage mark-up Preference Government -10 Monetary Technology Data

22 Decomposition of Output Growth (Percentage point deviation (annual rate) from steady state) 20 Financial Price mark-up Preference 15 Investment Wage mark-up Government Monetary Technology Data

23 Decomposition of Federal Funds Rate (Annualized percentage point deviation from steady state) Financial Price mark-up Preference 14 Investment Wage mark-up Government 12 Monetary Technology Data

24 Decomposition of Leverage (Percentage point deviation from steady state) Financial Price mark-up Preference 100 Investment Wage mark-up Government Monetary Technology Data

25 Decomposition of M-R/L-M Credit Spread (Annualized percentage point deviation from steady state) 5 Financial Price mark-up Preference Investment Monetary Wage mark-up Technology Government Data

26 Decomposition of Price Inflation (Percentage point deviation (annual rate) from steady state) Financial Price mark-up Preference 10 Investment Wage mark-up Government Monetary Technology Data

27 Decomposition of Wage Inflation (Percentage point deviation (annual rate) from steady state) Financial Price mark-up Preference 12 Investment Wage mark-up Government 10 Monetary Technology Data

28 Decomposition of Hours Worked (Percentage point deviation from steady state) Financial Price mark-up Preference -10 Investment Wage mark-up Government Monetary Technology Data

29 Concluding Remarks Medium-risk long-maturity (M-R/L-M) credit spreads have substantial predictive power for economic activity. Estimation of a DSGE model that uses M-R/L-M credit spreads to identify financial market distortions implies important role for credit-supply shocks for investment and output at business cycle frequencies. Directions for future research: Addition of frictions in the intermediation process linking household borrowing to asset prices. Explicit modeling of the financial sector.

30 Priors & Posteriors: Structural Parameters Prior Posterior Parameter Distr. Mean SD Mode Mean P5 P95 χ Beta ϕ Normal σ c Normal h Beta ξ w Beta σ l Normal ξ p Beta ι w Beta ι p Beta ψ Beta φ p Normal

31 Priors & Posteriors: Structural Parameters (cont.) Prior Posterior Parameter Distr. Mean SD Mode Mean P5 P95 r π Normal ρ Beta r y Normal r y Normal π Gamma (β 1 1) Gamma l Normal γ Normal α Normal z Normal s Normal

32 Priors & Posteriors: Shock Processes Volatility Parameters Prior Posterior Parameter Distr. Mean SD Mode Mean P5 P95 σ n Invgamma σ fd Invgamma σ qs Invgamma σ a Invgamma σ b Invgamma σ g Invgamma σ r Invgamma σ p Invgamma σ w Invgamma

33 Priors & Posteriors: Shock Processes (cont.) Persistence Parameters Prior Posterior Parameter Distr. Mean SD Mode Mean P5 P95 ρ fd Beta ρ qs Beta ρ a Beta ρ b Beta ρ g Beta ρ r Beta ρ p Beta ρ w Beta µ p Beta µ w Beta ρ ga Beta