A Macroeconomic Model of Equities and Real, Nominal, and ...swanson2/pres/nbersi2016ezap.pdf · A Macroeconomic Model of Equities and Real, Nominal, and Defaultable Debt Eric T. Swanson

Introduction Model Asset Prices Discussion Conclusions

A Macroeconomic Model of Equities and Real,Nominal, and Defaultable Debt

Eric T. SwansonUniversity of California, Irvine

Impulse and Propagation Mechanisms WorkshopNBER Summer Institute

July 11, 2016


Motivation

Goal: Show that a simple macroeconomic model (with Epstein-Zinpreferences) is consistent with a wide variety of asset pricing facts

equity premium puzzlelong-term bond premium puzzle (nominal and real)credit spread puzzle

Reduces separate puzzles in finance to a single, unifying puzzle:Why does risk aversion in the model need to be so high?

uncertainty: Weitzman (2007), Barillas-Hansen-Sargent(2010), et al.rare disasters: Rietz (1988), Barro (2006), et al.long-run risks: Bansal-Yaron (2004) et al.heterogeneous agents: Mankiw-Zeldes (1991), Guvenen(2009), Schmidt (2015), et al.financial intermediaries: Adrian-Etula-Muir (2013)


Motivation






Motivation




uncertainty: Weitzman (2007), Barillas-Hansen-Sargent(2010), et al.rare disasters: Rietz (1988), Barro (2006), et al.long-run risks: Bansal-Yaron (2004) et al.

heterogeneous agents: Mankiw-Zeldes (1991), Guvenen(2009), Schmidt (2015), et al.financial intermediaries: Adrian-Etula-Muir (2013)


Motivation






Motivation

Implications for Finance:unifying explanation for asset pricing puzzlesstructural model of asset prices (provides intuition, robustnessto breaks and policy interventions)

Implications for Macro:show how to match risk premia in DSGE frameworkstart to endogenize asset price–macroeconomy feedback

Secondary theme: Keep the model as simple as possible

Two key ingredients:Epstein-Zin preferencesnominal rigidities


Motivation






Motivation






Motivation






Households

Period utility function:

u(ct , lt ) ≡ log ct − ηl1+χt

1 + χ

additive separability between c and lSDF comparable to finance literaturelog preferences for balanced growth, simplicity

Flow budget constraint:

at+1 = eit at + wt lt + dt − ct

Calibration: (IES = 1), χ = 3, l = 1 (η = .54)


Generalized Recursive Preferences

Household chooses state-contingent {(ct , lt )} to maximize

V (at ; θt ) = max(ct ,lt )

u(ct , lt )− βα−1 log [Et exp(−αV (at+1; θt+1))]

Calibration: β = .992, RRA (Rc) = 60 (α = 59.15)


Firms

Firms are very standard:continuum of monopolistic firms (gross markup λ)Calvo price setting (probability 1− ξ)Cobb-Douglas production functions, yt (f ) = Atk1−θ lt (f )θ

fixed firm-specific capital stocks k

Random walk technology: log At = log At−1 + εt

simplicitycomparability to finance literaturehelps match equity premium

Calibration: λ = 1.1, ξ = 0.8, θ = 0.6, σA = .007, (ρA = 1), k4Y = 2.5


Fiscal and Monetary Policy

No government purchases or investment:

Yt = Ct

Taylor-type monetary policy rule:

it = r + πt + φπ(πt − π) + φy (yt − y t )

“Output gap” (yt − y t ) defined relative to moving average:

y t ≡ ρyy t−1 + (1− ρy )yt

Rule has no inertia:simplicityRudebusch (2002, 2006)

Calibration: φπ = 0.5, φy = 0.75, π = .008, ρy = 0.9


Solution Method

Write equations of the model in recursive form

Divide nonstationary variables (Yt , Ct , wt , etc.) by At

Solve using perturbation methods around nonstoch. steady state

first-order: no risk premiasecond-order: risk premia are constantthird-order: time-varying risk premiahigher-order: more accurate over larger region

Model has 2 state variables (yt , ∆t ), one shock (εt )


Solution Method

Write equations of the model in recursive form

Divide nonstationary variables (Yt , Ct , wt , etc.) by At

Solve using perturbation methods around nonstoch. steady statefirst-order: no risk premiasecond-order: risk premia are constantthird-order: time-varying risk premiahigher-order: more accurate over larger region

Model has 2 state variables (yt , ∆t ), one shock (εt )


Impulse Responses

0 10 20 30 40 500.0

0.2

0.4

0.6

0.8

1.0percent

TechnologyAt


Impulse Responses

0 10 20 30 40 500.0

0.2

0.4

0.6

0.8

1.0percent

ConsumptionCt


Impulse Responses

10 20 30 40 50

-1.0

-0.8

-0.6

-0.4

-0.2

0.0ann. pct.

Inflationπt


Impulse Responses

10 20 30 40 50

-0.5

-0.4

-0.3

-0.2

-0.1

0.0ann. pct.

Short-term nominal interest rate it


Impulse Responses

0 10 20 30 40 500.0

0.1

0.2

0.3

0.4

0.5ann. pct.

Short-term real interest rate rt


Nonlinear vs. Linear Impulse Responses

0 10 20 30 40 500.0

0.2

0.4

0.6

0.8

1.0percent

Price DispersionΔt



1st-order solution

5th-order solution

0 10 20 30 40 500.0

0.2

0.4

0.6

0.8

1.0

percentConsumptionCt


Firms’ Optimal Price and Price Dispersion

0AB

0

log Δt

pt*

Ptlog



0 10 20 30 40 500.0

0.2

0.4

0.6

0.8

1.0percent

Price DispersionΔt



1st-order solution

5th-order solution

0 10 20 30 40 500.0

0.2

0.4

0.6

0.8

1.0




1st-order solution

5th-order solution

0 10 20 30 40 500.0

0.2

0.4

0.6

0.8

1.0




10 20 30 40 50

-0.4

-0.2

0.0

0.2

0.4

percentLabor Lt



1st-order solution

5th-order solution

0 10 20 30 40 500.0

0.2

0.4

0.6

0.8

1.0



Equity: Levered Consumption Claim

Equity pricepe

t = Etmt+1(Cνt+1 + pe

t+1)

where ν is degree of leverage

Realized gross return:

Ret+1 ≡

Cνt+1 + pe

t+1

pet

Equity premiumψe

t ≡ EtRet+1 − ert

Calibration: ν = 3



Equity pricepe


t+1)



Ret+1 ≡

Cνt+1 + pe

t+1

pet

Equity premiumψe


Calibration: ν = 3



Equity pricepe


t+1)



Ret+1 ≡

Cνt+1 + pe

t+1

pet

Equity premiumψe


Calibration: ν = 3



Equity pricepe


t+1)



Ret+1 ≡

Cνt+1 + pe

t+1

pet

Equity premiumψe


Calibration: ν = 3


Table 2: Equity Premium

In the data: 3–6.5 percent per year (e.g., Campbell, 1999,Fama-French, 2002)

Risk aversion Rc Shock persistence ρA Equity premium ψe

10 1 0.6230 1 1.9660 1 4.1990 1 6.70

60 .995 1.8660 .99 1.0860 .98 0.5360 .95 0.17





10 1 0.6230 1 1.9660 1 4.1990 1 6.70

60 .995 1.8660 .99 1.0860 .98 0.5360 .95 0.17





10 1 0.6230 1 1.9660 1 4.1990 1 6.70

60 .995 1.8660 .99 1.0860 .98 0.5360 .95 0.17





10 1 0.6230 1 1.9660 1 4.1990 1 6.70

60 .995 1.8660 .99 1.0860 .98 0.5360 .95 0.17


Equity Premium

10 20 30 40 50

-80

-60

-40

-20

0ann. bp

Equity premiumψte


Real Government Debt

Real n-period zero-coupon bond price:

p(n)t = Et mt+1p(n−1)

t+1 ,

p(0)t = 1, p(1)

t = e−rt

Real yield:

r (n)t = −1

nlog p(n)

t

Real term premium:ψ

(n)t = r (n)

t − r (n)t

wherer (n)t = −1

nlog p(n)

t

p(n)t = e−rt Et p

(n−1)t+1





t+1 ,

p(0)t = 1, p(1)

t = e−rt

Real yield:

r (n)t = −1

nlog p(n)

t


(n)t = r (n)

t − r (n)t

wherer (n)t = −1

nlog p(n)

t

p(n)t = e−rt Et p

(n−1)t+1





t+1 ,

p(0)t = 1, p(1)

t = e−rt

Real yield:

r (n)t = −1

nlog p(n)

t


(n)t = r (n)

t − r (n)t

wherer (n)t = −1

nlog p(n)

t

p(n)t = e−rt Et p

(n−1)t+1





t+1 ,

p(0)t = 1, p(1)

t = e−rt

Real yield:

r (n)t = −1

nlog p(n)

t


(n)t = r (n)

t − r (n)t

wherer (n)t = −1

nlog p(n)

t

p(n)t = e−rt Et p

(n−1)t+1





t+1 ,

p(0)t = 1, p(1)

t = e−rt

Real yield:

r (n)t = −1

nlog p(n)

t


(n)t = r (n)

t − r (n)t

wherer (n)t = −1

nlog p(n)

t

p(n)t = e−rt Et p

(n−1)t+1


Nominal Government Debt

Nominal n-period zero-coupon bond price:

p$(n)t = Et mt+1e−πt+1p$(n−1)

t+1 ,

p$(0)t = 1, p$(1)

t = e−it

Nominal yield:

i(n)t = −1

nlog p$(n)

t

Nominal term premium:

ψ$(n)t = i(n)

t − i(n)t

wherei(n)t = −1

nlog p$(n)

t

p$(n)t = e−it Et p

$(n−1)t+1


Nominal Government Debt

Nominal n-period zero-coupon bond price:

p$(n)t = Et mt+1e−πt+1p$(n−1)

t+1 ,

p$(0)t = 1, p$(1)

t = e−it

Nominal yield:

i(n)t = −1

nlog p$(n)

t

Nominal term premium:

ψ$(n)t = i(n)

t − i(n)t

wherei(n)t = −1

nlog p$(n)

t

p$(n)t = e−it Et p

$(n−1)t+1


Real Yield Curve

Table 3: Real Zero-Coupon Bond Yields

2-yr. 3-yr. 5-yr. 7-yr. 10-yr. (10y)−(3y)

US TIPS, 1999–2015a 1.29 1.55 1.82US TIPS, 2004–2015a 0.14 0.28 0.60 0.89 1.22 0.94US TIPS, 2004–2007a 1.39 1.52 1.74 1.91 2.09 0.57UK indexed gilts, 1983–1995b 6.12 5.29 4.34 4.12 −1.17UK indexed gilts, 1985–2015c 1.91 2.05 2.16 2.25 0.34UK indexed gilts, 1990–2007c 2.79 2.78 2.79 2.80 0.01

macroeconomic model 1.94 1.93 1.93 1.93 1.93 0.00

aGürkaynak, Sack, and Wright (2010) online datasetbEvans (1999)cBank of England web site


Real Yield Curve

Table 3: Real Zero-Coupon Bond Yields

2-yr. 3-yr. 5-yr. 7-yr. 10-yr. (10y)−(3y)

US TIPS, 1999–2015a 1.29 1.55 1.82US TIPS, 2004–2015a 0.14 0.28 0.60 0.89 1.22 0.94US TIPS, 2004–2007a 1.39 1.52 1.74 1.91 2.09 0.57UK indexed gilts, 1983–1995b 6.12 5.29 4.34 4.12 −1.17UK indexed gilts, 1985–2015c 1.91 2.05 2.16 2.25 0.34UK indexed gilts, 1990–2007c 2.79 2.78 2.79 2.80 0.01

macroeconomic model 1.94 1.93 1.93 1.93 1.93 0.00

aGürkaynak, Sack, and Wright (2010) online datasetbEvans (1999)cBank of England web site


Nominal Yield Curve

Table 4: Nominal Zero-Coupon Bond Yields

1-yr. 2-yr. 3-yr. 5-yr. 7-yr. 10-yr. (10y)−(1y)

US Treasuries, 1961–2015a 5.27 5.50 5.68 5.97 6.18US Treasuries, 1971–2015a 5.42 5.66 5.86 6.18 6.43 6.71 1.29US Treasuries, 1990–2007a 4.56 4.84 5.06 5.41 5.68 5.98 1.42UK gilts, 1970–2015b 6.92 7.10 7.26 7.51 7.70 7.89 0.96UK gilts, 1990–2007b 6.20 6.29 6.38 6.47 6.50 6.48 0.28

macroeconomic model 5.35 5.59 5.80 6.09 6.27 6.44 1.09

aGürkaynak, Sack, and Wright (2007) online datasetbBank of England web site

Supply shocks make nominal long-term bonds risky: inflation risk


Nominal Yield Curve


1-yr. 2-yr. 3-yr. 5-yr. 7-yr. 10-yr. (10y)−(1y)






Nominal Yield Curve


1-yr. 2-yr. 3-yr. 5-yr. 7-yr. 10-yr. (10y)−(1y)






Nominal Term Premium

10 20 30 40 50

-10

-8

-6

-4

-2

0ann. bp

Nominal term premiumψt$(40)


Defaultable Debt

Default-free depreciating nominal consol:

pct = Et mt+1e−πt+1(1 + δpc

t+1)

Yield to maturity:

ict = log( 1

pct

+ δ)

Nominal consol with default:

pdt = Et mt+1e−πt+1

[(1− 1d

t+1)(1 + δpdt+1) + 1d

t+1 ωt+1 pdt

]Yield to maturity:

idt = log( 1

pdt

+ δ)

The credit spread is idt − ict


Defaultable Debt



t+1)

Yield to maturity:

ict = log( 1

pct

+ δ)



[(1− 1d

t+1)(1 + δpdt+1) + 1d

t+1 ωt+1 pdt

]Yield to maturity:

idt = log( 1

pdt

+ δ)



Defaultable Debt



t+1)

Yield to maturity:

ict = log( 1

pct

+ δ)



[(1− 1d

t+1)(1 + δpdt+1) + 1d

t+1 ωt+1 pdt

]

Yield to maturity:

idt = log( 1

pdt

+ δ)



Defaultable Debt



t+1)

Yield to maturity:

ict = log( 1

pct

+ δ)



[(1− 1d

t+1)(1 + δpdt+1) + 1d

t+1 ωt+1 pdt

]Yield to maturity:

idt = log( 1

pdt

+ δ)



Defaultable Debt



t+1)

Yield to maturity:

ict = log( 1

pct

+ δ)



[(1− 1d

t+1)(1 + δpdt+1) + 1d

t+1 ωt+1 pdt

]Yield to maturity:

idt = log( 1

pdt

+ δ)



Table 5: Credit Spread

average ann. cyclicality of average cyclicality of creditdefault prob. default prob. recovery rate recovery rate spread (bp)

.006 0 .42 0 34.0

If default isn’t cyclical, then it’s not risky




.006 0 .42 0 34.0

If default isn’t cyclical, then it’s not risky


Default Rate is Countercyclical

Macroeconomic Conditions and the Puzzles

A. Default rates and credit spreads

O Moody's Recovery Rates - • - Altman Recovery Rates (

Long-Term Mean

1985 2005

B. Recovery rates

1990 1995 2000

Figure 1. Default rates, credit spreads, and recovery rates over the business cy cle. Panel A plots the Moody's annual corporate default rates during 1920 to 2008 and the monthly Baa-Aaa credit spreads during 1920/01 to 2009/02. Panel B plots the average recovery rates during 1982 to 2008. The "Long-Term Mean" recovery rate is 41.4%, based on Moody's data. Shaded areas

are NBER-dated recessions. For annual data, any calendar year with at least 5 months being in a

recession as defined by NBER is treated as a recession year.

default component of the average 10-year Baa-Treasury spread in this model rises from 57 to 105 bps, whereas the average optimal market leverage of a Baa-rated firm drops from 50% to 37%, both consistent with the U.S. data.

Figure 1 provides some empirical evidence on the business cycle movements in default rates, credit spreads, and recovery rates. The dashed line in Panel A plots the annual default rates over 1920 to 2008. There are several spikes in the default rates, each coinciding with an NBER recession. The solid line plots the monthly Baa-Aaa credit spreads from January 1920 to February 2009. The

spreads shoot up in most recessions, most visibly during the Great Depression, the savings and loan crisis in the early 1980s, and the recent financial crisis in 2008. However, they do not always move in lock-step with default rates

(the correlation at an annual frequency is 0.65), which suggests that other

factors, such as recovery rates and risk premia, also affect the movements

in spreads. Next, business cycle variation in the recovery rates is evident in

This content downloaded from 67.98.229.10 on Fri, 11 Apr 2014 19:22:23 PMAll use subject to JSTOR Terms and Conditions

source: Chen (2010)


Recovery Rate is Procyclical

Macroeconomic Conditions and the Puzzles

A. Default rates and credit spreads

O Moody's Recovery Rates - • - Altman Recovery Rates (

Long-Term Mean

1985 2005

B. Recovery rates

1990 1995 2000

Figure 1. Default rates, credit spreads, and recovery rates over the business cy cle. Panel A plots the Moody's annual corporate default rates during 1920 to 2008 and the monthly Baa-Aaa credit spreads during 1920/01 to 2009/02. Panel B plots the average recovery rates during 1982 to 2008. The "Long-Term Mean" recovery rate is 41.4%, based on Moody's data. Shaded areas

are NBER-dated recessions. For annual data, any calendar year with at least 5 months being in a

recession as defined by NBER is treated as a recession year.

default component of the average 10-year Baa-Treasury spread in this model rises from 57 to 105 bps, whereas the average optimal market leverage of a Baa-rated firm drops from 50% to 37%, both consistent with the U.S. data.

Figure 1 provides some empirical evidence on the business cycle movements in default rates, credit spreads, and recovery rates. The dashed line in Panel A plots the annual default rates over 1920 to 2008. There are several spikes in the default rates, each coinciding with an NBER recession. The solid line plots the monthly Baa-Aaa credit spreads from January 1920 to February 2009. The

spreads shoot up in most recessions, most visibly during the Great Depression, the savings and loan crisis in the early 1980s, and the recent financial crisis in 2008. However, they do not always move in lock-step with default rates

(the correlation at an annual frequency is 0.65), which suggests that other

factors, such as recovery rates and risk premia, also affect the movements

in spreads. Next, business cycle variation in the recovery rates is evident in

This content downloaded from 67.98.229.10 on Fri, 11 Apr 2014 19:22:23 PMAll use subject to JSTOR Terms and Conditions

source: Chen (2010)




.006 0 .42 0 34.0

.006 −0.3 .42 0 130.9

.006 −0.3 .42 2.5 143.1

.006 −0.15 .42 2.5 78.9

.006 −0.6 .42 2.5 367.4

.006 −0.3 .42 1.25 137.0

.006 −0.3 .42 5 155.2




.006 0 .42 0 34.0

.006 −0.3 .42 0 130.9

.006 −0.3 .42 2.5 143.1

.006 −0.15 .42 2.5 78.9

.006 −0.6 .42 2.5 367.4

.006 −0.3 .42 1.25 137.0

.006 −0.3 .42 5 155.2




.006 0 .42 0 34.0

.006 −0.3 .42 0 130.9

.006 −0.3 .42 2.5 143.1

.006 −0.15 .42 2.5 78.9

.006 −0.6 .42 2.5 367.4

.006 −0.3 .42 1.25 137.0

.006 −0.3 .42 5 155.2


Discussion

1 IES ≤ 1 vs. IES > 1

2 Volatility shocks

3 Endogenous conditional heteroskedasticity

4 Monetary and fiscal policy shocks

5 Financial accelerator


Intertemporal Elasticity of Substitution

Long-run risks literature typically assumes IES > 1, for tworeasons:

ensures equity prices rise (by more than consumption) inresponse to an increase in technologyensures equity prices fall in response to an increase involatility

However, IES > 1 is not necessary for these criteria to be satisfied,particularly when equity is a levered consumption claim.

Model here satisfies both criteria with IES = 1 (or even < 1).




















Impulse Responses to Technology Shock

10 20 30 40 50

-80

-60

-40

-20

0ann. bp

Equity premiumψte


Impulse Responses to Technology Shock

0 10 20 30 40 500.0

0.5

1.0

1.5

2.0

2.5

percentEquity price pt

e







Extend model above to include volatility shocks:

logσA,t = (1− ρσ) log σA + ρσ logσA,t−1 + εσt

Calibration: ρσ = .98, Var(εσt ) = (0.1)2




















Impulse Responses to Volatility Shock

10 20 30 40 500.0070

0.0072

0.0074

0.0076

0.0078

0.0080VolatilityσA ,t



10 20 30 40 50

-0.5

-0.4

-0.3

-0.2

-0.1

0.0percent

ConsumptionCt



10 20 30 40 50

-0.5

-0.4

-0.3

-0.2

-0.1

0.0ann. pct.

Inflationπt



0 10 20 30 40 500

20

40

60

80

100ann. bp

Equity premiumψte



10 20 30 40 50

-5

-4

-3

-2

-1

0percent

Equity price pte



0 10 20 30 40 500

5

10

15

20

25ann. bp

Nominal term premiumψt$ (40)


Endogenous Conditional Heteroskedasticity

Note that

ψet ≡ EtRe

t+1 − ert

=Et (Cν

t+1 + pet+1)

pet

− ert

=Et (Cν

t+1 + pet+1)

Etmt+1(Cνt+1 + pe

t+1)− 1

Etmt+1

=Etmt+1 Et (Cν

t+1 + pet+1)− Etmt+1(Cν

t+1 + pet+1)

Etmt+1 pet

=−Covt

(mt+1,Re

t+1)

Etmt+1

= −Covt

( mt+1

Etmt+1, re

t+1

)



Note that

ψet ≡ EtRe

t+1 − ert

=Et (Cν

t+1 + pet+1)

pet

− ert

=Et (Cν

t+1 + pet+1)

Etmt+1(Cνt+1 + pe

t+1)− 1

Etmt+1

=Etmt+1 Et (Cν


t+1 + pet+1)

Etmt+1 pet

=−Covt

(mt+1,Re

t+1)

Etmt+1

= −Covt

( mt+1

Etmt+1, re

t+1

)



Note that

ψet ≡ EtRe

t+1 − ert

=Et (Cν

t+1 + pet+1)

pet

− ert

=Et (Cν

t+1 + pet+1)

Etmt+1(Cνt+1 + pe

t+1)− 1

Etmt+1

=Etmt+1 Et (Cν


t+1 + pet+1)

Etmt+1 pet

=−Covt

(mt+1,Re

t+1)

Etmt+1

= −Covt

( mt+1

Etmt+1, re

t+1

)



ψet = −Covt

( mt+1

Etmt+1, re

t+1

)

Risk premium can only vary over time if SDF or asset return isconditionally heteroskedastic

Traditional finance approach: assume shocks are heteroskedastic

Here, conditional heteroskedasticity is endogenous

Nonlinear solution contains terms of form

xtεt+1

so covariance Covt depends on state xt



ψet = −Covt

( mt+1

Etmt+1, re

t+1

)Risk premium can only vary over time if SDF or asset return is

conditionally heteroskedastic




xtεt+1




ψet = −Covt

( mt+1

Etmt+1, re

t+1






xtεt+1




ψet = −Covt

( mt+1

Etmt+1, re

t+1






xtεt+1




ψet = −Covt

( mt+1

Etmt+1, re

t+1






xtεt+1



Impulse Responses for Conditional Variance

10 20 30 40 50

-50

-40

-30

-20

-10

0percent

Conditional Variance Vart [(Ct+1/Ct)-1]


Impulse Responses for Conditional Variance

10 20 30 40 50

-50

-40

-30

-20

-10

0percent

Conditional Variance Vart [exp(-αVt+1)/Etexp(-αVt+1)]


Impulse Responses to Pos. and Neg. Tech. Shocks

no previous shock in period 0

previous shock of .007 in period 0

10 20 30 40 50

-0.4

-0.2

0.2

0.4

percentPrice DispersionΔt

no previous shock in period 0

previous shock of .007 in period 0

10 20 30 40 50

-0.4

-0.2

0.2

0.4

percentPrice DispersionΔt

0 10 20 30 40 500.0

0.2

0.4

0.6

0.8

1.0percent

ConsumptionCt

10 20 30 40 50

-1.0

-0.8

-0.6

-0.4

-0.2

0.0percent

ConsumptionCt


Price Dispersion

0AB

0

log Δt

pt*

Ptlog


Monetary and Fiscal Policy Shocks

Rudebusch and Swanson (2012) consider similar model withtechnology shockgovernment purchases shockmonetary policy shock

All three shocks help the model fit macroeconomic variables

But technology shock is most important (by far) for fitting assetprices:

technology shock is more persistenttechnology shock makes nominal assets risky














No Financial Accelerator

With model-implied stochastic discount factor mt+1, we can priceany asset

Economy affects mt+1 ⇒ economy affects asset prices

However, asset prices have no effect on economy

Clearly at odds with financial crisis

To generate feedback, want financial intermediaries whose networth depends on assets

...but not in this paper


















Conclusions

1 The standard textbook New Keynesian model (with Epstein-Zinpreferences) is consistent with a wide variety of asset pricingfacts/puzzles

2 Unifies asset pricing puzzles into a single puzzle—Why does riskaversion in macro models need to be so high?(Literature provides good answers to this question)

3 Provides a structural framework for intuition about risk premia

4 Suggests a way to model feedback from risk premia tomacroeconomy

Documents

A Macroeconomic Model of Equities and Real, Nominal, and ...swanson2/pres/nbersi2016ezap.pdf · A Macroeconomic Model of Equities and Real, Nominal, and Defaultable Debt Eric T. Swanson