Financial Factors in Economic
Fluctuations
Lawrence Christiano
Roberto Motto
Massimo Rostagno
What we do
• IIntegrate
t
t financial
fi
i l frictions
f i ti
into
i t a standard
t d d equilibrium
ilib i
model and estimate the model using Euro Area and
US data.
– Asymmetric information and costly state verification
(Townsend (1978), Bernanke-Gertler-Gilchrist (1999))
– Endogenous determination of financial liabilities, like M1
and M3 (Chari-Christiano-Eichenbaum (1995))
• Decompose 14 aggregate data series into shocks and
propagation mechanisms:
– A new shock, a ‘risk’ shock
– A new source of propagation: non-state contingent
nominal rates of interest.
Outline
• Sketch the basic ingredients of the model.
• Results
Standard Model
Firms
consumption
Yt 
 0 Yjt
1
1
 f,t
 f,f,t
dj
Investment goods
, 1 ≤  f,t  ,
Yjt   t Kjt z t l jt  1−
Supply labor
It
̄
other   oil
t au t Kt  G t  C t   ,t ≤ Y t .
Rent capital
Households
Backyard capital accumulation:
u c,t 
R kt1
E t  c,t u c,t1  t1
̄ t1  1 − K
̄ t  G i,t , I t , I t−1 
K
R kt1
u t11 r kt1  1 − P k ′,t1

P k ′,t
Financial Sector
• Assets:
A
t
– Working capital loans
– Loans to entrepreneurs for purchasing capital
• Liabilities:
– Provide utility to households
– Neoclassical bank production function
• Results:
– features on the liability side of banks do not add
shocks or propagation to business cycle
dynamics
– Features on the asset side do
matter…particularly ‘loans to entrepreneurs’
Entrepreneur
• Buys capital from capital producers using
own resources and loans from bank
• Rents capital to intermediate good
producers.
d
Banks Households
Banks,
Households, Entrepreneurs
~F,  t , E  1
entrepreneur
entrepreneur
entrepreneur
Households
Bank
entrepreneur
entrepreneur
Standard debt contract
Economic Impact of Risk Shock
llognormall di
distribution:
t ib ti
20 percent jump in standard deviation
1
0.9
σ
σ *1.2
0.8
Larger number of
entrepreneurs in left
tail problem for bank
density
0.7
0.6
Banks must raise
interest rate on entrepreneur
0.5
Entrepreneur borrows less
0.4
0.3
Entrepreneur buys less capital,
investment drops, economy tanks
0.2
01
0.1
0.5
1
1.5
2
2.5
idiosyncratic shock
3
3.5
4
Risk Shocks and News
• Finding:
– Economic effects of risk shocks operate
primarily via advance information, or news.
Risk Shock and News
• Assume
iid Wold innovation in log  t
iid,
log t   log t−1 

ut
• A
Agents
t have
h
advance
d
iinformation
f
ti about
b t
pieces of u t
u t   0t   1t−1 . . .  8t−8
2
 it−i ~iid, E it−i    2i
 it−i ~piece of u t observed at time t − i
Monetary Policy
• Nominal rate of interest function of:
– Anticipated
A ti i t d llevell off iinflation
fl ti and
d change.
h
– Slowly moving inflation target.
– Deviation off output growth from
f
ss path.
– Growth of credit in case of EA.
– Monetary policy shock.
Estimation
• EA and US data covering 1985Q1-2008Q2
Δ log
per capita stock market indext
Pt
GDP deflator inflationt
logper capita hourst 
per capita credit t
Pt
Δ log
Δ logper capita GDP t 
Δ log
Hourly compensationt
Pt
Δ logper capita investment t 
Xt 
Δ log
per capita M1 t
Pt
Δ log
per capita M3 t
Pt
Δ logper capita consumptiont 
Risk Spread t
R long
− R et
t
R et
Δ logPIt )
Δ logreal oil price t 
Δ log
per capita Bank Reserves t
Pt

• Standard Bayesian methods
Summary of results
• Risk shocks:
– important source of fluctuations.
fluctuations
– news on the risk shock important.
• Assumption of state non
non-contingent
contingent interest rates has
an important impact on shock propagation.
• Money
M
demand
d
d and
d iinside
id money:
– relatively unimportant as a source of shocks
– modest contribution to forecast ability
• Out-of-Sample
Out of Sample evidence suggests the model deserves
to be taken seriously.
Risk Shocks
• Important
• Why
Wh are th
they iimportant?
t t?
• What shock do they displace, and why?
Figure: Year-over-year GDP Growth Rate - Data (black) versus what data
would have been with only the risk shock
US
Risk shock important
EA
Variance Decomposition of
Financial Shocks, US Data
risk shock,  t
Output
IImportant
t t for
f
output and investment
19
Investment
38
Consumption
5
Very important for
financial variables
Risk Spread
97
R l Value
Real
V l off Stock
St k Market
M k t
80
Although the data like signals on standard technology
shocks they prefer signals on risk
shocks,
risk.
Why
y Risk Shock is so Important
p
• A. Our econometric estimator ‘thinks’
risk spread
p
~ risk shock.
• B
B. In the data: the risk spread is strongly
negatively correlated with output.
• C. In the model: bad risk shock generates
a response that resembles a recession.
recession
• A+B+C suggests risk shock important
important.
Correlation (risk spread(t),output(t-j)), HP filtered data, 95% Confidence Interval
The risk spread is significantly negatively correlated with output and leads a little.
Notes: Risk spread is measured by the difference between the yield on the lowest rated corporate bond (Baa) and the highest rated
corporate bond (Aaa). Bond data were obtained from the St. Louis Fed website. GDP data were obtained from Balke and Gordon
(1986). Filtered output data were scaled so that their standard deviation coincide with that of the spread data.
Another Reason the Risk Shock is
so Important
• Positive shock to risk triggers what looks
like a recession
US, Impulse Response to Riskiness Shock (contemporaneous)
Credit procyclical,
procyclical risk spread countercyclical
Output
Investment
Consumption
0
0.05
0.2
-0.1
01
0
percent
p
percent
p
-0.05
-0.2
-0.05
-0.4
-0.15
-0.1
-0.6
Risk spread
(AR)
Premium
(Annual
Rate)
Real Net Worth
0
Total Loans (Real)
-0
0.5
5
-1
-2
percent
100
basis points
b
percent
percent
0
50
-1
-1.5
-2
-3
-2.5
0
10
20
30
10
20
30
10
20
30
What Shock Does the Risk
Shock Displace, and why?
• Th
The risk
i k shock
h k crowds
d outt some off th
the
role of the marginal efficiency of
i
investment
t
t shock.
h k
Business Cycle Frequencies
Model
risk shock,  t survival,  t marginal efficiency of investment,  it
Output
Baseline
19
2
22
CEE-SW
CEE
SW
na
na
46
Investment
Baseline
38
5.2
53
CEE-SW
na
na
91
Consumption
Baseline
5
1
2
CEE-SW
na
na
3
ExternaltoFinance
Premium
Risk shock appears
crowd out
the
marginal
9 efficiency 3of investment shock
97
0
Baseline
li
CEE-SW
na
na
na
Real Value of Stock Market
Baseline
80
10
2
CEE-SW
na
na
na
Why
y does Risk Crowd out
Marginal Efficiency of
I
Investment?
t
t?
Price of capital
Supply shifter:
marginal efficiency
of investment,  i,t
Demand shifters:
risk shock,  t ;
wealth shock,
shock  t
Quantity of capital
• Marginal efficiency of investment shock
can account well for the surge in
investment and output in the 1990s
1990s, as
long as the stock market is not included in
the analysis.
analysis
• Wh
When the
th stock
t k market
k t iis iincluded,
l d d th
then
explanatory power shifts to financial
market
k t shocks.
h k
Variance Decomposition of
Inside Money Shocks
Business Cycle
y Frequencies
q
Bank demand for excess reserves,  t Bank technology shock, x bt Household money demand shock,  t
Output
0
05
0.5
0
Investment
0
0
0
Consumption
0
1
0
Risk spread
0
0
0
Real value of stock market
0
0
Contribution to variance: trivial
0
Out of Sample RMSEs
• There is not a loss of forecasting power with
the additional complications of the model.
• The model does well on everything, except
the risk spread.
• Calculations for 1,2,…,12 ahead forecasts:
– First date of forecast,
forecast 2001Q3
– DSGE models re-estimated every other quarter
– BVAR re-estimated each quarter (standard
Minnesota priors).
Other support for the model
• Model predicted default rates positively
correlated with measures of default in the
data.
0.0235
4
Default Probability (lhs)
Expected Default Probability, NFC (rhs)
0.0185
2
0.0135
0.0085
1990Q1
0
1993Q1
1996Q1
1999Q1
2002Q1
2005Q1
2008Q1
Our Measure of Idiosyncratic
Risk versus Bloom,
Risk,
Bloom et al
1.15
1.05
0.5
Series1
Series4
Ri ki
Riskiness
Sh
Shockk (3-quarter
(3
t MA)
Cross-firms Sale Growth Spread (3-quarter MA)
04
0.4
0.95
0.85
0.3
0.75
0.65
1981Q2
0.2
1986Q2
1991Q2
1996Q2
2001Q2
2006Q2
Conclusion
• Incorporating financial frictions changes inference
about the sources of shocks and of propagation
– risk shock
shock.
– New nominal rigidity: nominal non-state contingent
interest rate
• Models with financial frictions can be used to ask
i t
interesting
ti policy
li questions:
ti
– When there is an increase in risk spreads, how
policy
y respond?
p
should monetaryy p
– How should monetary policy be structured to avoid
excess asset market volatility?
– What are the pro’s
pro s and con’s
con s of ‘unconventional
unconventional
monetary policy’?