Teaching Notes 4. Governments and Asset Prices Pietro...

Teaching Notes 4.

Governments and Asset Prices

Pietro Veronesi

The University of Chicago

Booth School of Business

CEPR, NBER

Pietro Veronesi Governements and Asset Prices page: 2

Growing Role of Government

• U.S. federal government:


Uncertainty about Government Policy

• We do not know exactly what the government is going to do,nor what the impact of its actions is going to be

“Some of today’s nervousness comes from “policy risk.” Nobody—neither firms, banks nor

individuals—is quite sure where government policy is going.” The Economist, Feb 13 2010

• Government’s actions sometimes have unintended consequences

– Example: Affordable housing


“Fannie Mae and Freddie Mac committed to increased financing of ‘af-fordable housing.’ They became the largest buyers of subprime and Alt-Amortgages between 2004 and 2007, with total GSE exposure eventually ex-ceeding $1 trillion.”

Charles Calomiris and Peter Wallison, Wall Street Journal, September 23, 2008.








– Example: Managerial compensation limits


• Clinton’s law aimed at restricting CEO compensation came in 1993

“The law contains so many obvious loopholes that in 10 minutes evenForrest Gump could think up five ways around it.”

Business Week, November 27, 2006








– Example: Managerial compensation limits

– Example: Biofuel subsidies


“The number of hungry people increased by about 50 million in 2007 as aresult of high food prices”, Jacques Diouf, FAO Director-General, July 2008.

“Analysts from the OECD to the World Bank argue that biofuel demand isthe biggest single reason why food prices have soared in the past couple ofyears”, The Economist, October 9, 2008




“Some of today’s nervousness comes from “policy risk.” Nobody—neither firms, banks

nor individuals—is quite sure where government policy is going.”

The Economist, February 13, 2010

• Real effects

“Faced with a highly uncertain policy environment, the prudent course is to set aside

or delay costly commitments that are hard to reverse.”

The Wall Street Journal, January 4, 2010

• Financial effects?


What We Do

• We analyze how changes in government policy affect stock prices

• We develop a general equilibrium model featuring

– Government with economic and non-economic motives

– Uncertainty about government policy

1. “Policy” uncertainty

2. “Political” uncertainty


Main Results

• Government tends to change its policy after downturns

• Policy changes increase volatilities, risk premia, correlations

• Stock prices fall at announcements of policy changes, on average

– Prices rise if the old policy was sufficiently unproductive,but they fall on average (in expectation)

– Expected stock price drop at the announcement is large

∗ when policy/political uncertainty is large

∗ when policy change is induced by a short or shallow downturn

– Distribution of announcement returns is left-skewed

• Prices rise at announcements of policy decisions, on average


Model

• Finite horizon [0, T ]; continuum of equity-financed firms i ∈ [0, 1]

• Firm i’s capital Bit follows dBi

t = Bit dΠi

t

• Firm i’s profitability:

dΠit = (µ + gt) dt + σdZt + σ1dZi,t

gt = impact of government policy on average profitability

• Government can change policy at time τ , 0 < τ < T

– Change ⇒ gt changes from gold to gnew

– No change ⇒ gt stays at gold


Policy Uncertainty

• Both gold and gnew are unknown

• Prior beliefs:

gold ∼ N(0, σ2

g

)

gnew ∼ N(0, σ2

g

)

σg = policy uncertainty

• Posterior beliefs: Effectively, agents observe signal

dst = gtdt + σdZt =⇒ gt ∼ N(gt, σ

2t

)

– with

dgt = σ2tσ

−1dZt; σ2t =

11σ2

g+ 1

σ2t

• Policy change ⇒ Beliefs reset from posterior to prior


Political Uncertainty

• Investors maximize E

{W

1−γT

1−γ

}, where γ > 1, WT =

1∫

0

BiTdi

• “Quasi-benevolent” government changes policy at τ iff

Eτ

[CW 1−γ

T

1 − γ| policy change

]> Eτ

[W 1−γ

T

1 − γ| no policy change

]

C = “political cost” of changing policy

(C > 1 ⇒ cost; C < 1 ⇒ benefit)

• Investors view C as random with E[C] = 1:

c = log (C) ∼ N

(−

1

2σ2

c , σ2c

)

σc = political uncertainty


Optimal Changes in Government Policy

• Result: A policy change occurs iff

gτ < g(c)

where

g(c) = −

(σ2

g − σ2τ

)(γ − 1) (T − τ )

2−

c

(T − τ ) (γ − 1)

• Investors don’t know c ⇒ cannot fully anticipate a policy change

• E{g(c)

}< 0 ⇒ Policy changes tend to occur after “downturns”


Parameter Choices

Table 1: Parameter Choices

σg σc µ σ σi T τ γ

0.02 0.10 0.10 0.05 0.10 20 10 5


0 2 4 6 8 10 12 14 16 18 20−3

−2

−1

0

1

2

3

Time

Pro

fita

bility (

%)

Panel A. Policy Change

Realized Profitability

Expected Profitability

Threshold

0 2 4 6 8 10 12 14 16 18 20−3

−2

−1

0

1

2

3

Time

Pro

fita

bility (

%)

Panel B. No Policy Change

Realized Profitability

Expected Profitability

Threshold

Figure 1. Profitability dynamics and the policy decision.


Stock Prices

• Firm i’s stock is a claim on the firm’s liquidating dividend BiT

• Market value of stock i:

M it = Et

[πT

πt

BiT

]

• Complete markets ⇒ State price density:

πt =1

λEt

[W−γ

T

], where WT =

1∫

0

BiTdi

• Risk-free bond as numeraire (or risk-free rate = 0)


Stock Price Reaction to the Announcement of a Policy Change

• The price right after the announcment is

M iτ+ =

M i,yesτ+ = Bi

τ+ e(µ−γσ2)(T−τ )+1−2γ2 (T−τ )2σ2

g if policy changes

M i,noτ+ = Bi

τ+ e(µ−γσ2+gτ)(T−τ )+1−2γ2 (T−τ )2σ2

τ if policy does not change

• The price right before the announcment is

Mτ =pτEτ

[πTBi

T |yes]

+ (1 − pτ )Eτ

[πTBi

T |no]

pτEτ [πT |yes] + (1 − pτ )Eτ [πT |no]

= ωM i,yesτ+ + (1 − ω) M i,no

τ+ ,

– where

ω =pτ

pτ + (1 − pτ ) e−γgτ (T−τ )−12γ2(T−τ )2(σ2

g−σ2τ)


Stock Price Reaction to the Announcement of a Policy Change

• R(gτ ) ≡M

i,yesτ+

M iτ

− 1 = stock return at the announcement of a policy change

• Result: R(gτ ) < 0 iffgτ > g∗

where

g∗ = −(σ2

g − σ2τ

)(T − τ )

(γ −

1

2

)

– Cash flow vs. discount rate effects


• Distribution of g(c), as perceived by investors just before τ :

0

50

100

150

200

g∗ g(0)gτ 0

Fre

qu

en

cy d

istr

ibu

tio

n

Panel A.

0

50

100

150

200

g∗ g(0)gτ 0

Panel B.

0

50

100

150

200

g∗ g(0) gτ 0

Fre

qu

en

cy d

istr

ibu

tio

n

Panel C.

0

50

100

150

200

g∗ g(0) gτ0

Panel D.

• Shaded area: Probability of a policy change = Prob(gτ < g(c)

)

• Recall: R(gτ ) < 0 iff gτ > g∗


Distribution of Returns at the Announcement of a Policy Change

−20 −15 −10 −5 00

10

20

30

40

50

Return (%)

Fre

quency

Dis

trib

utio

n

Panel A. σc = 10%

−20 −15 −10 −5 00

10

20

30

40

Return (%)

Fre

quency

Dis

trib

utio

n

Panel B. σc = 20%

σg = 1 %

σg = 2 %

σg = 3 %

σg = 1 %

σg = 2 %

σg = 3 %


Expected Return at the Announcement of a Policy Change

• Result: E {R(gτ ) | policy change} < 0




0 2 4 6 8 10 12 14 16 18 20−2

−1.8

−1.6

−1.4

−1.2

−1

−0.8

−0.6

−0.4

−0.2

0

σc (%)

Re

turn

(%

)

σg = 1%

σg = 2%

σg = 3%


Determinants of the Announcement Return

• We relate the announcement return to the length and depthof the downturns that induce policy changes

• Let t0 mark the beginning of a downturn: gt0 = 0

LENGTH = τ − t0

DEPTH =gτ

Std(gτ ). . . number of std dev’s by which gt drops

• Note that

gτ |gt0 = 0 ∼ N (0, Std(gτ )) , where Std(gτ ) =√

σ2t0− σ2

τ


The Role of Downturn Length

0 2 4 6 8 10 12 14 16 18 20−6

−5

−4

−3

−2

−1

0

σc (%)

Re

turn

(%

)

Panel A. Expected Announcement Return. Length = 5 years

σg = 1%

σg = 2%

σg = 3%

0 2 4 6 8 10 12 14 16 18 20−20

−15

−10

−5

0

σc (%)

Re

turn

(%

)

Panel B. Expected Announcement Return. Length = 1 years

σg = 1%

σg = 2%

σg = 3%


The Role of Downturn Depth

−2.5 −2 −1.5 −1 −0.5 0 0.5−20

−15

−10

−5

0

Depth

Re

turn

(%

)

Panel A. Announcement Return

−2.5 −2 −1.5 −1 −0.5 0 0.50

0.2

0.4

0.6

0.8

1

Depth

Pro

ba

bili

ty

Panel B. Probability of a Policy Change

Length = 10

Length = 5

Length = 1

Length = 10

Length = 5

Length = 1


Expected Return at the Announcement of a Policy Decision

0 2 4 6 8 10 12 14 16 18 200

0.1

0.2

0.3

0.4

σc (%)

Re

turn

(%

)Panel A. Expected Return at Announcement of a Policy Decision

0 2 4 6 8 10 12 14 16 18 20−5

−4

−3

−2

−1

0

σc (%)

Re

turn

(%

)

Panel B. Expected Return at Announcement of Policy Change

σg = 1%

σg = 2%

σg = 3%

0 2 4 6 8 10 12 14 16 18 200

1

2

3

4

5

σc (%)

Re

turn

(%

)

Panel C. Expected Return at Announcement of No Policy Change

σg = 1%

σg = 2%

σg = 3%

σg = 1%

σg = 2%

σg = 3%


Moments of Stock Returns

9 9.5 10 10.5 1110

15

20

25

30

Time

Pe

rce

nt

pe

r ye

ar

Panel C. Return Volatility

σg=1%

σg=2%

σg=3%

9 9.5 10 10.5 1120

40

60

80

100

Time

Pe

rce

nt

Panel D. Correlation

σg=1%

σg=2%

σg=3%

9 9.5 10 10.5 1120

40

60

80

100

120

Time

Pe

rce

nt

pe

r ye

ar

Panel A. SDF Volatility

σg=1%

σg=2%

σg=3%

9 9.5 10 10.5 110

5

10

15

20

25

30

Time

Pe

rce

nt

pe

r ye

ar

Panel B. Expected Return

σg=1%

σg=2%

σg=3%


Policy Changes Allowed vs. Precluded

9 9.5 10 10.5 112

2.1

2.2

2.3

2.4

2.5Panel A. Market Value, Length = 1

Time

Policy change allowed

Policy change precluded

9 9.5 10 10.5 1112

13

14

15

16

17

18Panel C. Volatility, Length = 1

Time

Pe

rce

nt

pe

r ye

ar

9 9.5 10 10.5 112.25

2.3

2.35

2.4

2.45

2.5Panel B. Market Value, Length = 5

Time

9 9.5 10 10.5 1110

12

14

16

18Panel D. Volatility, Length = 5

Time

Pe

rce

nt

pe

r ye

ar


Model Extensions

• Endogenous timing of the policy change

• Investment adjustment

• Different Policy Exposure


Extension: Endogenous Timing of Policy Change

• We extend the model by endogenizing the timing of policy change

– No closed-form solutions; solve numerically

• Government can change policy at any time τ ∈ [1, 2, . . . , 19]

• Each year i, a new value of Ci is drawn; Ci are iid

• Value function reflects option value of waiting

• We find our results continue to hold when τ is endogenous


Extension: Endogenous Timing of Policy Change

0 5 10 15 20

−4

−3

−2

−1

0Panel A. Announcement Return

σc (%)

Pe

rce

nt

σg = 1%

σg = 2%

σg = 3%

5 10 15 20−10

−8

−6

−4

−2

0Panel B. Announcement Return

Policy Announcement Date

Pe

rce

nt

σg = 1%

σg = 2%

σg = 3%

−1 −0.5 0 0.5 110

12

14

16

18

20Panel C. Return Volatility

Time Relative to Policy Announcement Date

Pe

rce

nt

pe

r ye

ar

σg = 1%

σg = 2%

σg = 3%

−1 −0.5 0 0.5 120

30

40

50

60

70Panel D. Correlation

Time Relative to Policy Announcement Date

Pe

rce

nt

σg = 1%

σg = 2%

σg = 3%


Extension: Investment Adjustment

• We extend the model by allowing firms to disinvest

• At time τ , each firm can disinvest and switch capital into cash

• Firms make investment decisions at the same time as government makes thepolicy decision

• Proposition: In Nash equilibrium, a fraction ατ ∈ [0, 1] of firms continueinvesting. The government changes its policy iff

gτ < g (c, ατ )

• We solve the problem numerically

– The threshold g (c, ατ) depends on ατ , which depends on gτ



• For parameter values in Table 1, the equilibrium has ατ = 1(no disinvestment), so all results continue to hold

– To obtain disinvestment, we reduce µ from 10% to 2%

• We find:

– Both policy and political uncertainty reduce investment

– Our key asset pricing results continue to hold



0 5 10 15 20−1.5

−1

−0.5

0

σc (%)

Retu

rn (

%)

Panel B. Announcement Return

σg = 1%

σg = 2%

σg = 3%

0 5 10 15 2070

75

80

85

90

95

100

σc (%)

α (

%)

Panel A. Equilibrium α

σg = 1%

σg = 2%

σg = 3%

9 9.5 10 10.5 1110

15

20

25

Time

Perc

ent per

year

Panel C. Return Volatility

σg=1%

σg=2%

σg=3%

9 9.5 10 10.5 1120

40

60

80

100

Time

Perc

ent

Panel D. Correlation

σg=1%

σg=2%

σg=3%


Extension: Different Policy Exposure

• N sectors. Firm i in each sector n has “government beta” βn

dΠit = (µ + βngt) dt + σdZn

t + σ1dZit ,

• All agents effectively receive N signals

dsnt = (µ + βngt) dt + σdZn

t , n = 1, . . . ,N.

• Bt = total capital; Bnt = total capital of all firms in sector n. Define state

variables

wnt =

Bnt

Bt

, n = 1, . . . ,N.

• Proposition. The government changes its policy at τ iff

gτ < g (c,wτ ) ,


Extension: Different Policy Exposures

0 0.5 1 1.5 2−1

−0.8

−0.6

−0.4

−0.2

0

βH

− βL

Perc

ent

B. EAR and β Heterogeneity

w0 = 0.2

w0 = 0.5

w0 = 0.8

0 5 10 15 20−2

−1.5

−1

−0.5

0

σc (%)

Perc

ent

A. Expected Announcement Return

σg = 1%

σg = 2%

σg = 3%

0 0.5 1 1.5 2

0

2

4

6

8

10

βH

− βL

Perc

ent

C. µH − µL for gt = 2%

wt = 0.2

wt = 0.5

wt = 0.8

0 0.5 1 1.5 2

0

2

4

6

8

10

βH

− βL

Perc

ent

D. µH − µL for gt = −2%

wt = 0.2

wt = 0.5

wt = 0.8


Key Empirical Predictions

• Stock returns at announcements of policy changes should

– be negative, on average

– be more negative, on average, if

∗ policy/political uncertainty is high

∗ policy change is induced by a short or shallow downturn

– have a left-skewed probability distribution

• Stock returns at announcements of policy decisions should bepositive, on average


Political Uncertainty and Volatility

Source: Bloom “The Impact of Uncertainty Shocks”, Econometrica, 2009


Political Uncertainty and Volatility: Germany 1920 - 1940

Source: Bittlingmayer “Output, Stock Volatility, and Political Uncertainty in a Natural Experiment: Germany, 1880 - 1940”, Journal of Finance, 1998


Political Cycles and Stock Prices

• Stock market prefers Republicans. Kerry vs Bush close election.

FIGURE IThe S&P 500 is Higher under a Bush versus Kerry Presidency

Source: Snowberg, Wolfers, Zitzewitz “Partisan Impacts on the Economy: Evidence From Prediction Markets and Close Elections” QJE, 2007



• Stock market prefers Republicans....

FIGURE III

Equity Markets have Historically Preferred Republican Presidents

Source: Snowberg, Wolfers, Zitzewitz “Partisan Impacts on the Economy: Evidence From Prediction Markets and Close Elections” QJE, 2007



• ....but excess return are higher during Democratic presidencies

Sample: 1927 - 2009Rep Dem t-diff

Average Excess Returns (%/year) 0.79 10.37 2.30Average Real Div Growth (%/year) 4.17 5.93 1.29Average P/D Ratio 32.00 28.95 1.4 (logs)Average Volatility (%/year) 15.48 14.39 1.67Median Excess Return (%/year) 7.75 16.11 -Median Nominal Dividend Growth (%/year) 7.00 7.92 -Median P/D Ratio 26.83 23.62 -Median Volatility (%/year) 12.08 11.66 -

See also: Santa Clara and Valkanov “Political Cycles and the Stock Market” Journal of Finance, 2003


Political Cycles and Stock Prices: Belo, Gala, and Li (2013, JFE)

• BGL sort stocks depending on a their industry exposure to government spending.

• Use Benchmark Input-Output Accounts released by Bureau of Economic Anal-ysis to compute

GOVi =Sales in industry i generated by government spending

Total sales in industry i

– Captures both direct and indirect effects

• Compute average returns over democratic and republican presidents across firmswith large and small exposure to government.

• Perform standard asset pricing tests.


Table 2: Government Spending Across Presidencies

This table reports the mean and standard deviation (Std) of selected macroeconomic variables across Democratic(Dem) and Republican (Rep) presidencies. ∆GDP is the growth rate of real per capita gross domestic product and∆G is the growth rate of per capita total government spending. Dif is the difference between the correspondingstatistic under Democratic and Republican presidencies, and Dif p-value is the corresponding p-value (in parenthe-sis). The annual data ranges from 1955 to 2009 (top panel), 1929 to 2009 (mid panel), and 1947 to 2009 (bottompanel).

Mean StdDif Dif Dif Dif

Dem Rep Dem-Rep p-value Dem Rep Dem-Rep p-value

Main Sample 1955-2009∆GDP 2.54 1.50 1.04 (0.05) 2.06 2.19 −0.13 (0.79)∆G 1.53 0.75 0.78 (0.40) 2.26 2.13 0.13 (0.74)

Extended Sample 1929-2009∆GDP 3.50 0.58 2.91 (0.04) 5.30 4.06 1.24 (0.10)∆G 4.15 0.61 3.54 (0.45) 24.80 3.19 21.61 (0.00)

Post WWII Sample 1947-2009∆GDP 2.62 1.53 1.09 (0.02) 2.33 2.29 0.04 (0.91)∆G 3.53 0.44 3.10 (0.10) 6.64 2.89 3.75 (0.00)


Table 4: Industries with High and Low Exposure to the Government Sector

This table reports a sample of industries in the high and low government exposure portfolios. Gov Expo is the

industry level measure of exposure to government spending (in percent). The data and the industry classification

is based on the 2002 Input-Output (IO) table.

Gov

IO Code Industry Expo Portfolio

Industries with High Exposure to the Government Sector

336414 Guided missile and space vehicle manufacturing 94.7 High

336611 Ship building and repairing 67.3 High

515100 Radio and television broadcasting 54.7 High

541700 Scientific research and development services 47.0 High

335110 Electric lamp bulb and part manufacturing 45.9 High

211000 Oil and gas extraction 39.9 High

511110 Newspaper publishers 28.3 High

334418 Printed circuit assembly manufacturing 24.0 High

334220 Broadcast and wireless communications equipment 22.6 High

322120 Paper mills 20.6 High

Industries with Low Exposure to the Government Sector

311225 Fats and oils refining and blending 2.9 Low

314110 Carpet and rug mills 2.9 Low

311410 Frozen food manufacturing 1.8 Low

311820 Cookie, cracker, and pasta manufacturing 1.7 Low

339910 Jewelry and silverware manufacturing 1.2 Low

312110 Soft drink and ice manufacturing 1.0 Low

335224 Household laundry equipment manufacturing 0.8 Low

312120 Breweries 0.8 Low

3122A0 Tobacco product manufacturing 0.4 Low

713950 Bowling centers 0.0 Low


Table 5: Average Characteristics of the Government Exposure Portfolios

This table reports the time-series averages of median characteristics of five government exposure portfolios. Market

Share is the portfolio market share -across all firms in CRSP-, Firms is the monthly number of firms in each

portfolio, Industries is the monthly number of three-SIC digit industries in each portfolio, GOV is the portfolio

level of industry exposure to government spending, Size is the (log) market capitalization, BM is the ratio of

book-to-market equity and Lev is the leverage ratio. The sample is from July 1955 to December 2009.

MarketPortfolio Share (%) Firms Industries Gov (%) Size BM Lev

Lo 10.60 543 115 1.51 4.00 0.86 0.402 9.02 378 81 4.81 3.95 0.85 0.393 22.40 750 121 9.17 4.08 0.79 0.374 20.69 719 104 14.74 4.17 0.77 0.34

Hi 12.92 836 97 29.58 3.94 0.71 0.30


Table 6: Average Returns of the Government Exposure Portfolios

This table reports the time-series average of the excess value-weighted annualized returns of the five government

exposure portfolio across all years, as well as across Democratic (Dem) and Republican (Rep) presidential terms.

Dif Dem-Rep is the difference in the portfolio average excess returns across Democratic and Republican presidential

terms and Diff p-value is the corresponding Newey-West corrected p-value (in parenthesis). Hi - Lo is the return of

the High (Hi) minus Low (Lo) government exposure spread portfolio. The sample is from July 1955 to December

2009 (top panel) and from July 1929 to Dec 2009 (bottom panel).

All Dem Rep Dif DifPortfolio Years Years Years Dem - Rep p-value

Sample Period: 1955-2009Lo 7.13 8.72 6.14 2.58 (0.59)2 5.49 7.83 4.03 3.80 (0.42)3 5.94 9.94 3.45 6.49 (0.10)4 4.88 10.17 1.58 8.59 (0.05)

Hi 6.54 14.86 1.36 13.50 (0.02)Hi-Lo −0.59 6.14 −4.78 10.92 (0.01)

p-value (0.75) (0.06) (0.02)

Extended Sample Period: 1929-2009Lo 7.26 10.71 3.72 6.99 (0.08)2 7.12 12.20 1.89 10.32 (0.03)3 6.84 12.30 1.22 11.09 (0.01)4 6.94 13.57 0.11 13.46 (0.00)

Hi 8.32 17.04 −0.66 17.70 (0.00)Hi-Lo 1.06 6.33 −4.38 10.71 (0.00)

p-value (0.54) (0.01) (0.07)


Figure 1: The Government Exposure Spread Across Presidential Terms

The figure plots the average annualized value-weighted excess returns of the government exposure spread portfolio across presidential terms. The sample data

are monthly from July 1929 to December 2009.


Table 8: Cross-Sectional Regressions

This table reports the results of Fama-MacBeth cross-sectional regressions of monthly excess stock returns on lagged

firm characteristics. Gov is the firm level exposure to the government sector, Size is the natural log of firm size,

B/M is the natural log of firm book-to-market ratio, Mom is firm momentum (prior cumulated 12 months returns),

Beta is the firm level current market beta computed from daily returns within the month. Dem and Rep are dummy

variables equal to one if the president is from the Democratic or Republican party, respectively. Political Contrib

is the ratio of firms’ political contribution to total assets. Fed denotes the per capita federal spending in a firm’s

headquarter state. It is measured as the cross-sectional percentile rank across all U.S. states in each year. The

table reports the average loadings for each cross-sectional regression and the corresponding Newey-West corrected

p-value (in parenthesis). The sample data is monthly from July 1955 to December 2009.

Fed× Fed×Dem× Rep× Political Dem× Rep×

Row Intercept Size B/M Mom Beta Gov Gov Contrib Gov Gov1 1.02 1.53 −0.71

(0.00) (0.01) (0.06)2 0.99 −0.07 0.13 1.00 0.34 1.00 −0.58

(0.00) (0.06) (0.00) (0.00) (0.00) (0.02) (0.05)3 1.01 −0.07 0.13 1.00 0.34 0.94 −0.63 0.38

(0.00) (0.04) (0.00) (0.00) (0.00) (0.03) (0.04) (0.00)4 1.02 1.26 −0.23 1.09 −0.91

(0.00) (0.06) (0.64) (0.10) (0.03)5 1.20 −0.09 0.18 1.08 0.29 0.90 −0.22 0.53 −0.80

(0.00) (0.01) (0.00) (0.00) (0.00) (0.10) (0.57) (0.37) (0.04)


Political Uncertainty and Risk Premia (Pastor and Veronesi 2013, JFE)

• Political news moves markets

– E.g., Eurozone debt crisis, U.S. debt ceiling talks, etc.

• Yet, no role for political news in finance theory


Source: Baker, Bloom, and Davis: “Measuring Economic Policy Uncertainty”


Model

• Finite horizon [0, T ]; continuum of equity-financed firms i ∈ [0, 1]

• Firm i’s profitability (= growth rate of capital):

dBit/B

it = (µ + gt) dt + σdZt + σ1dZi,t

gt = impact of government policy on average profitability

• Government can change policy at time τ , 0 < τ < T ,choosing from N potential new policies

gt =

g0 for t ≤ τg0 for t > τ if old policy is retainedgn for t > τ if new policy n is chosen, n ∈ {1, . . . ,N}


Impact Uncertainty

• Both g0 and gn are unknown

• Prior beliefs:

g0 ∼ N(0, σ2

g

)

gn ∼ N(µn

g , σ2g,n

)for n = 1, . . . , N

• Posterior beliefs:

gt ∼ N(gt, σ

2t

), where dgt = σ2

tσ−1dZt , σ2

t =

(1

σ2g

+t

σ2

)−1

• If government changes policy at time τ ,beliefs about gt change from posterior of g0 to prior of gn


Political Uncertainty

• Investors maximize E

{W

1−γT

1−γ

}, where γ > 1, WT =

1∫

0

BiTdi

• “Quasi-benevolent” government maximizes

maxn∈{0,1,...,N}

Cn × Eτ

[W 1−γ

T

1 − γ| policy n

]

Cn = political cost of choosing policy n

(Cn > 1 ⇒ cost; Cn < 1 ⇒ benefit)

• Cn is unknown for n = 1, . . . , N before time τ

– Uncertainty about Cn ⇒ political uncertainty

• Prior beliefs: cn = log (Cn) ∼ N(−1

2σ2

c, σ2c

)⇒ E0 [Cn] = 1


Learning about Political Costs

• For t ∈ (t0, τ ), agents observe signals about Cn:

dsnt = cn dt + h dZn

c,t for n = 1, . . . ,N

– Steady flow of political news

• Posterior beliefs:

cn ∼ N(cnt , σ

2c,t

)

where dcnt = σ2

c,th−1dZn

c,t , σ2c,t =

(1σ2

c+ t−t0

h2

)−1

• dZnc,t = political shocks

– Orthogonal to economic shocks dZt, dZi,t


Utility Score

• Result: Given any two policies m,n ∈ {0, 1, . . . ,N},

Eτ

[W 1−γ

T

1 − γ| policy m

]> Eτ

[W 1−γ

T

1 − γ| policy n

]⇐⇒ µm > µn

where µn = utility score of policy n:

µn = µng −

σ2g,n

2(T − τ ) (γ − 1) n = 1, . . . , N

µ0 = gτ −σ2

τ

2(T − τ ) (γ − 1)

⇒ Higher mean and lower variance of g deliver more utility


Optimal Government Policy Choice

• Result: Government chooses the policy n ∈ {0, 1, . . . ,N} whose value ofµn − cn is the largest, where cn = cn/ (γ − 1) (T − τ )

• Corollary: Government changes its policy iff

gτ < maxn∈{1,...,N}

{µn − cn} +σ2

τ

2(T − τ ) (γ − 1)

i.e., if the current policy is perceived as sufficiently unproductive

⇒ Government provides “put protection” to the market

• Investors don’t know cn ⇒ cannot fully anticipate a policy change


Stock Prices

• Firm i’s stock is a claim on the firm’s liquidating dividend BiT

• Market value of stock i:

M it = Et

[πT

πt

BiT

]

• Complete markets ⇒ State price density:

πt =1

λEt

[W−γ

T

], where WT =

1∫

0

BiTdi

• Risk-free rate = 0 (equivalently, use risk-free bond as numeraire)

– Because no intermediate consumption


Stock Market Reaction to the Policy Announcement

• Result: Closed-form solution for announcement return Rn (gτ)

• Corollary: For any pair of policies m, n ∈ {0, 1, . . . , N},

1 + Rm (gτ)

1 + Rn (gτ)= e(µm−µn)(T−τ )−γ

2(T−τ )2(σ2g,m−σ2

g,n)

• Government policies cannot be judged by stock market reactions

– Can have Rm(xτ ) < Rn(xτ) and µm > µn , or vice versa

• If µm = µn and σg,m > σg,n, then Rm (gτ) < Rn (gτ)

– “Deeper reforms” elicit less favorable stock market reactions

• Note: Mt = 1−γλπt

Et

[W

1−γT

1−γ

]. A policy change can affect πt.


Three Types of Shocks

• Result: Before time τ , SDF follows the process

dπt

πt

= −γσdZt︸︷︷︸Capital shocks

+ σπ,0dZt︸︷︷︸Impact shocks

+N∑

n=1

σπ,ndZnc,t

︸︷︷︸Political shocks

1. Capital shocks: Fluctuations in aggregate capital (dBt)

2. Impact shocks: Learning about policy impact (dgt)

– Capital + Impact shocks = Economic shocks (dZt)

3. Political shocks: Learning about political costs (dcnt )

– Orthogonal to economic shocks

– σπ,n → 0 when gt → ∞


A Two-Policy Example

• Consider policies H and L, with σg,H > σg,L

• Choose µg,H > µg,L so that both policies yield same utility

• Parameters:

σg σc µ σ σ1 T τ γ h σg,L σg,H

2% 10% 10% 5% 10% 20 10 5 5% 1% 3%


The Level of Stock Prices: Economic vs Political Shocks

−0.02 −0.015 −0.01 −0.005 0 0.005 0.01 0.015 0.021.8

2

2.2

2.4

2.6

2.8

3

Economic conditions (gt)

M/B

New risky policy more likely

New policies equally likely

New safe policy more likely


The Equity Risk Premium and Its Components

−0.02 −0.015 −0.01 −0.005 0 0.005 0.01 0.015 0.020

1

2

3

4

5

6


Percentper

year

Capital shocks

Impact shocks

Political shocks


−0.02 −0.01 0 0.01 0.020

1

2

3

4

5

6

7


Percent

per

year

A. σg = 1%

Capital shocks

Impact shocks

Political shocks

−0.02 −0.01 0 0.01 0.020

1

2

3

4

5

6

7


Percent

per

year

B. σg = 3%

−0.02 −0.01 0 0.01 0.020

1

2

3

4

5

6

7


Percent

per

year

C. σc = 5%

−0.02 −0.01 0 0.01 0.020

5

10

15


Percent

per

year

D. σc = 20%


−0.02 −0.01 0 0.01 0.020

2

4

6

8

10

12

14


Percent

per

year

A. h = 2.5%

Capital shocks

Impact shocks

Political shocks

−0.02 −0.01 0 0.01 0.020

1

2

3

4

5

6

7


Percent

per

year

B. h = 10%

−0.02 −0.01 0 0.01 0.020

1

2

3

4

5

6

7


Percent

per

year

C. τ − t = 1.5

−0.02 −0.01 0 0.01 0.020

1

2

3

4

5

6

7


Percent

per

year

D. τ − t = 0.5


The Effect of Policy Heterogeneity

• Heterogeneity = H = σg,H − σg,L

• Three values: H = 1%, 2%, 3%

• To vary H, we vary σg,H and σg,L keeping other parameters fixed

• Both policies H and L deliver the same utility


−0.02 −0.01 0 0.01 0.020

20

40

60

80

100A. Probability of Retaining Old Policy


Percent

High H

Med H

Low H

−0.02 −0.01 0 0.01 0.022

3

4

5

6

7

8


Percent

per

year

B. Total Risk Premium

−0.02 −0.01 0 0.01 0.021

2

3

4

5

6


Percent

per

year

C. Economic Shocks

−0.02 −0.01 0 0.01 0.020

1

2

3

4

5

6


Percent

per

year

D. Political Shocks


Policy Changes Allowed vs Precluded

−0.02 −0.01 0 0.01 0.022

3

4

5

6

7

8


Percent

per

year

A. Total Risk Premium

High H

Med H

Low H

No change

−0.02 −0.01 0 0.01 0.021.8

2

2.2

2.4

2.6

2.8

3


M/B

B. Market−to−Book Ratio

High H

Med H

Low H

No change

−0.02 −0.01 0 0.01 0.0210

15

20

25

30


Percent

per

year

C. Return Volatility

High H

Med H

Low H

No change

−0.02 −0.01 0 0.01 0.0240

50

60

70

80

90


Percent

D. Correlation

High H

Med H

Low H

No change


Empirical Analysis

• Test model’s predictions about political uncertainty (PU)

– PU is higher in weaker economic conditions

– PU commands a risk premium, larger when economy is weak

– PU makes stocks more volatile and more correlated,especially when the economy is weak

• Proxy for PU: Baker, Bloom, and Davis (2011)

– Weighted average of 3 components:

∗ News coverage of policy-related uncertainty

∗ Number of expiring federal tax code provisions

∗ Disagreement among forecasters of inflation and govt spending


1985 1990 1995 2000 2005 20100

10

20

30

40

50

60

70

Month

Cor

rela

tion

(per

cent

)

Panel A. Political Uncertainty vs Stock Correlation

Recession

Political uncertainty

Stock correlation

1985 1990 1995 2000 2005 20100

10

20

30

40

50

60

70

Month

Sta

ndar

d de

viat

ion

(per

cent

per

yea

r)

Panel B. Political Uncertainty vs Stock Volatility

Recession

Political uncertainty

Stock volatility


Is PU Higher in a Weaker Economy?

Table reports estimates of b and their t-statistics for

Specification 1: PUt = a + bEt + et

Specification 2: PUt = a + bEt + cPUt−1 + et

Measure of Economic Conditions

CFI -REC IPG P/E -DEF

Specification 1 -0.31 -0.69 -20.95 -0.02 -0.75(-7.24) (-5.12) (-4.10) (-3.38) (-8.61)

Specification 2 -0.05 -0.09 -2.90 -0.00 -0.09(-3.90) (-2.75) (-1.85) (-1.58) (-3.06)


Are Volatility and Correlation Higher When PU Is Higher?


Specification 1: V Ct = a + bPUt + et

Specification 2: V Ct = a + bPUt + cV Ct−1 + et

Correlation Volatility

EW VW Realized Implied

Specification 1 0.17 0.15 0.01 0.08(9.81) (7.25) (4.81) (5.27)

Specification 2 0.09 0.07 0.00 0.01(6.43) (5.14) (3.45) (2.53)


Are VOL and COR More Linked to PU in a Weaker Economy?


Specification 1: V Ct = a + bPUtEt + cPUt + dEt + et



Correlation: EW -0.03 -0.04 -3.53 -0.00 -0.00(-2.41) (-0.96) (-2.36) (-0.00) (-0.08)

Correlation: VW -0.03 -0.03 -3.54 -0.00 0.04(-1.92) (-0.60) (-2.03) (-0.26) (1.28)

Volatility: Realized -0.00 -0.01 -0.39 -0.00 -0.00(-5.46) (-4.39) (-4.52) (-3.74) (-3.17)

Volatility: Implied -0.04 -0.12 -3.48 -0.01 -0.05(-4.50) (-3.69) (-3.18) (-5.48) (-1.91)


Are VOL and COR More Linked to PU in a Weaker Economy?


Specification 2: V Ct = a + bPUtEt + cPUt + dEt + eV Ct−1 + et



Correlation: EW -0.02 -0.03 -2.35 0.00 -0.00(-2.04) (-1.07) (-1.97) (0.05) (-0.05)

Correlation: VW -0.02 -0.03 -2.04 -0.00 0.02(-1.48) (-0.79) (-1.54) (-0.10) (1.13)

Volatility: Realized -0.00 -0.01 -0.21 -0.00 -0.00(-4.11) (-3.86) (-3.11) (-2.77) (-2.58)

Volatility: Implied -0.01 -0.05 -0.19 -0.00 -0.03(-2.81) (-3.71) (-0.36) (-2.76) (-2.70)


Is Political Risk Premium Higher in a Weaker Economy?


Rt+1,t+h = a + bPUtEt + cPUt + dEt + et .


Horizon CFI -REC IPG P/E -DEF

Panel A. Equity premium: Future realized excess return

3 months -0.02 -0.05 -0.89 -0.01 -0.03(-1.30) (-1.24) (-0.71) (-2.17) (-1.19)

6 months -0.04 -0.11 -2.50 -0.01 -0.09(-2.09) (-1.53) (-1.17) (-3.18) (-1.97)

12 months -0.09 -0.21 -6.48 -0.02 -0.15(-2.41) (-1.78) (-1.76) (-2.85) (-1.69)

Panel B. Equity premium: Fitted value from a regression

-0.09 -0.27 -6.46 -0.02 -0.01(-3.42) (-3.12) (-2.35) (-4.75) (-0.10)


Conclusions

• We develop a theory in which political news moves stock prices

• Political uncertainty

– commands a risk premium that is larger in a weaker economy

– reduces the value of the government’s implicit put protection

– increases stock volatilities and correlations, especially whenthe economy is weak and policy heterogeneity is large


Kelly, Pastor, and Veronesi (2013)

• Empirically analyze whether / how uncertainty associated with political events(national elections and global summits) is priced in the option market

– Why options?

∗ Short maturities

∗ Different strikes

– Why elections and summits?

∗ Can result in major policy shifts

∗ Exogenous variation in political uncertainty

• Guided by an existing theoretical model of government policy choice

– We derive the model’s implications for option prices


What We Find

• Political uncertainty is priced in the option market in ways predicted by theory

• Options whose lives span political events tend to be more expensive.Such options offer valuable protection against

– price risk

– tail risk

– variance risk

associated with major political events

• This protection is more valuable

– when the economy is weaker

– when political uncertainty is higher


What We Find: Magnitudes

• Treatment-group options: put options whose lives span political events

• Price risk:

– ATM treatment-group options are more expensive by 5.1% compared toneighboring control-group options, on average

• Tail risk:

– 5% (10%) OTM treatment-group options are more expensive by 9.6% (16.0%)

• Variance risk:

– ATM treatment-group options are 48.1% more expensive relative to theBlack-Scholes model, on average; control-group options are 36.5% more ex-pensive

• The role of economic conditions:

– ATM treatment-group options are 8% (1%) more expensive compared tocontrol-group options when the economy is weak (strong)


Our Theoretical Contribution

• Analyze the PV (2013) model’s predictions for option prices around po-litical events

– Derive in closed form the price of a European put option whose life spans τ ;also the jump risk premium at time τ

– Analyze the price risk, variance risk, and tail risk associated with politicalevents, and their dependence on economic conditions and uncertainty

• Reinterpret the PV model to analyze elections

– Voters decide at time τ whether to replace the incumbent government and,if so, which of N potential new governments to elect

– Voters pay attention not only to economics

– Result: The incumbent government is more likely to be voted out when theeconomy is doing poorly


A Two-Policy Example

• Potential new policies H and L, same utility but H ’s impact is more uncertain

• Same parameter values as in PV (2013)

• Simulate many paths of state variables in the model (gt, CHt , CL

t )

• For each simulated path, calculate three variables at time τ − 12

based onone-period European put options that expire at time τ + 1

2

– IV : implied volatility of an ATM option (price risk )

– V RP : variance risk premium of an ATM option (variance risk )

∗ V RP = implied variance minus expected future variance

– Slope: implied volatility slope across strike prices (tail risk )

∗ Slope = implied vol 5% OTM minus implied vol 5% ITM


−0.02 −0.015 −0.01 −0.005 0 0.005 0.01 0.015 0.020

0.05

0.1

0.15

0.2

0.25Implied Volatility

IV

Economic Conditions (g)


−0.02 −0.01 0 0.01 0.02−0.02

−0.01

0

0.01

0.02

0.03

0.04Slope

Slope

Economic Conditions (g)−0.02 −0.01 0 0.01 0.02

−1.5

−1

−0.5

0

0.5

1Skewness of Returns

Skewness


−0.02 −0.01 0 0.01 0.02−0.01

0

0.01

0.02

0.03Variance Risk Premium

IV

2−E[V

ar]

Economic Conditions (g)−0.02 −0.01 0 0.01 0.02−1

0

1

2

3Jump Risk Premium

JRP



• Measure political uncertainty by the entropy of policy probabilities

Entropy = −pHt log pH

t − pLt log pL

t − p0t log p0

t , as of time t = τ − 12

0 0.2 0.4 0.6 0.8 10.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

0.22Implied Volatility

IV

Uncertainty


0 0.2 0.4 0.6 0.8 1−0.01

0

0.01

0.02

0.03

0.04Slope

Slope

Uncertainty

0 0.2 0.4 0.6 0.8 1−0.01

−0.005

0

0.005

0.01

0.015

0.02Variance Risk Premium

IV

2−E[V

ar]

Uncertainty


Data

• Options: 20 countries (from OptionMetrics)

– Put options on each country’s premier stock market index (for 15 countries)or, if unavailable, ETF on the country’s MSCI index (for 5 countries)

• Political events

– National elections: parliamentary, presidential

– Global summits: G8, G20, European

• Economic conditions

– GDP : Realized real GDP growth (from OECD)

– FST : Forecast of real GDP growth (from IMF)

– CLI : Composite leading indicator (from OECD)

– MKT : Stock market index return (from Datastream)

• Political uncertainty: for elections only

– UNC: minus the poll spread (most recent opinion poll spread before theelection; if unavailable, then ex-post election margin)


Table 1: Option sample

Start End

Country Index Date Date

Australia ASX 200 20040102 20120604

Belgium BEL20 20020102 20120831

Brazil MSCI Brazil 20060525 20120131Canada MSCI Canada 20060302 20120131

Finland OMXH25 20020102 20120831

France CAC 40 20030414 20120831Germany DAX 20020102 20120831

Italy FTSE MIB 20061011 20120831

Japan NIKKEI 225 20040506 20120604

Korea Kospi 20040503 20120131Mexico MSCI Mexico 20071129 20120131

Netherlands AEX 20020102 20120831

Singapore MSCI Singapore 20091118 20120131

South Africa MSCI South Africa 20070524 20120131Spain IBEX 35 20070514 20120831

Sweden OMXS30 20070126 20120831

Switzerland SMI 20020102 20120831Taiwan TAIEX 20040102 20120131

UK FTSE 100 20020102 20120831

USA S&P 500 19900101 20120131


Table 2: Number of political events

Elections SummitsTotal Total Parl. Pres. Total Euro G8/G20

All 271 64 57 14 216 170 74Australia 6 1 1 0 5 0 5

Belgium 13 2 2 0 11 11 0

Brazil 9 4 2 4 6 0 6

Canada 7 2 2 0 6 0 6Finland 1 0 0 0 1 1 0

France 27 6 4 2 21 21 7

Germany 25 5 5 0 21 21 7Italy 24 3 3 0 21 21 7

Japan 10 4 4 0 6 0 6

Korea 8 2 1 1 6 0 6

Mexico 7 1 1 0 6 0 6Netherlands 22 3 3 0 19 19 0

Singapore 2 2 1 1 0 0 0

South Africa 6 1 1 0 5 0 5

Spain 20 4 4 0 17 17 0Sweden 19 2 2 0 18 18 0

Switzerland 24 5 5 0 20 20 0

Taiwan 2 2 1 1 0 0 0UK 24 4 4 0 21 21 7

USA 15 11 11 5 6 0 6


Option-Market Variables

a− s a

τ

b− s b c− s c

treatmentcontrol control

τ : political event datea, b, c: option expiration dates

• Implied volatility difference:

IV Dτ = IV b −1

2(IV a + IV c) ,

where

IV b = Mean {IVb−s,b : b − s ∈ [τ − 20, τ − 1], ATM, open interest}

and IV a, IV c are defined analogously


Option-Market Variables (cont’d)

• Variance risk premium difference:

V RPDτ = V RP b −1

2(V RP a + V RP c) ,

V RP b = Mean {V RPb−s,b : b − s ∈ [τ − 20, τ − 1], ATM, open interest}

V RPb−s,b = IV 2b−s,b − RV 2

b−s,b

• Implied volatility slope difference:

SlopeDτ = Slopeb −1

2(Slopea + Slopec) ,

Slopeb = Mean {Slopeb−s,b : b − s ∈ [τ − 20, τ − 1]}

Slopeb−s,b = Slope from regression of implied vol on ∆ across all (at least 3)OTM options (−0.5 < ∆ < −0.1) with positive open interest


20 25 30 35 40 45 50Moneyness (|∆|)

Put Implied Volatility

IV no event

RV no event

VRP


20 25 30 35 40 45 50Moneyness (|∆|)


IV no event

RV no event

IV event

RV event

VRP


Empirical Results

• We find empirical support for 8 of 9 theoretical predictions

• Political uncertainty is priced

– IV D is positive, on average

– V RPD is positive, on average

– SlopeD is positive, on average

• The price of political uncertainty is higher in weaker economic conditions

– IV D is larger when the economy is weaker

– V RPD is larger when the economy is weaker

– SlopeD is larger when the economy is weaker

• The price of political uncertainty is higher amid higher uncertainty

– IV D is larger when the election outcome is more uncertain

– V RPD is larger when the election outcome is more uncertain


Table 3: Mean implied volatility differences

Weak minus strong economy

All MKT GDP FST CLI

Panel A: All political events

Mean 1.43 2.57 1.94 2.22 3.00

(4.43) (3.79) (3.34) (3.78) (4.61)

Obs. 271 271 271 266 267

Panel B: Elections only

Mean 1.63 2.63 1.73 2.51 2.36

(3.13) (2.73) (1.78) (2.34) (2.39)

Obs. 64 64 64 59 60

Panel C: Summits only

Mean 1.42 2.68 2.13 2.40 3.25

(3.76) (3.27) (3.17) (3.56) (4.30)

Obs. 216 216 216 216 216




All MKT GDP FST CLI


Mean 1.43 2.57 1.94 2.22 3.00

(4.43) (3.79) (3.34) (3.78) (4.61)

Obs. 271 271 271 266 267

• Economic significance:

– ATM treatment-group options are more expensive by 5.1% compared tocontrol-group options, on average




All MKT GDP FST CLI


Mean 1.43 2.57 1.94 2.22 3.00

(4.43) (3.79) (3.34) (3.78) (4.61)

Obs. 271 271 271 266 267


Mean 1.63 2.63 1.73 2.51 2.36

(3.13) (2.73) (1.78) (2.34) (2.39)

Obs. 64 64 64 59 60


Mean 1.42 2.68 2.13 2.40 3.25

(3.76) (3.27) (3.17) (3.56) (4.30)

Obs. 216 216 216 216 216




All MKT GDP FST CLI


Mean 1.43 2.57 1.94 2.22 3.00

(4.43) (3.79) (3.34) (3.78) (4.61)

Obs. 271 271 271 266 267

• Economic significance:

– ATM treatment-group options are 7.1% to 9.0% more expensive comparedto control-group options when the economy is weak

– ATM treatment-group options are 0.1% to 1.2% more expensive comparedto control-group options when the economy is strong


Examples of Influential Political Events

• The crisis “combo”:

U.S. election (November 4, 2008; Obama vs. McCain)G20 summit (November 14-15, 2008)

– Average IV D = 12.2%

• The pivotal Greek election (June 17, 2012)

– Average IV D = 6.7% (across European countries)

– Highest values: Spain (IV D = 10.3%) and Italy (7.7%)

– Lowest values: Sweden (IV D = 3.8%) and Switzerland (4.3%)


Table 4: Variance risk premium and implied volatility slope: Mean differencess

Variance risk premium (V RPD) Implied volatility slope (SlopeD)

Weak minus strong economy Weak minus strong economy

All MKT GDP FST CLI All MKT GDP FST CLI


Mean 1.07 3.02 2.07 2.55 3.05 1.73 3.26 2.11 2.66 3.02

(2.61) (3.51) (2.80) (3.54) (3.63) (3.59) (3.11) (2.52) (3.08) (2.97)

Obs. 271 271 271 266 267 238 238 238 233 236


Mean 1.30 2.46 1.07 2.45 1.26 1.14 3.56 1.14 1.96 1.38

(2.59) (2.62) (1.11) (2.20) (1.25) (2.08) (3.69) (1.08) (1.71) (1.28)

Obs. 64 64 64 59 60 55 55 55 50 53


Mean 1.07 3.39 2.58 2.97 3.75 1.84 3.43 2.53 2.87 3.46

(2.15) (3.15) (2.92) (3.49) (3.74) (3.16) (2.58) (2.54) (2.83) (2.80)

Obs. 216 216 216 216 216 191 191 191 191 191







Mean 1.07 3.02 2.07 2.55 3.05 1.73 3.26 2.11 2.66 3.02

(2.61) (3.51) (2.80) (3.54) (3.63) (3.59) (3.11) (2.52) (3.08) (2.97)

Obs. 271 271 271 266 267 238 238 238 233 236

• Economic significance (variance risk):

– ATM treatment-group options are 48.1% more expensive relative to theBlack-Scholes model, on average; control-group options are 36.5% more ex-pensive







Mean 1.07 3.02 2.07 2.55 3.05 1.73 3.26 2.11 2.66 3.02

(2.61) (3.51) (2.80) (3.54) (3.63) (3.59) (3.11) (2.52) (3.08) (2.97)

Obs. 271 271 271 266 267 238 238 238 233 236

• Economic significance (tail risk):

– 5% (10%) OTM treatment-group options are more expensive by 9.6% (16.0%)







Mean 1.07 3.02 2.07 2.55 3.05 1.73 3.26 2.11 2.66 3.02

(2.61) (3.51) (2.80) (3.54) (3.63) (3.59) (3.11) (2.52) (3.08) (2.97)

Obs. 271 271 271 266 267 238 238 238 233 236


Mean 1.30 2.46 1.07 2.45 1.26 1.14 3.56 1.14 1.96 1.38

(2.59) (2.62) (1.11) (2.20) (1.25) (2.08) (3.69) (1.08) (1.71) (1.28)

Obs. 64 64 64 59 60 55 55 55 50 53


Mean 1.07 3.39 2.58 2.97 3.75 1.84 3.43 2.53 2.87 3.46

(2.15) (3.15) (2.92) (3.49) (3.74) (3.16) (2.58) (2.54) (2.83) (2.80)

Obs. 216 216 216 216 216 191 191 191 191 191


Panel A: Stock Market Return Panel B: GDP Growth

−0.5 −0.4 −0.3 −0.2 −0.1 0 0.1 0.2 0.3

−0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30

Economic Conditions

IVD

−5 −4 −3 −2 −1 0 1 2

−0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30

Economic Conditions

IVD

Panel C: GDP Growth Forecast Panel D: Composite Leading Indicator

−3 −2 −1 0 1 2

−0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30

Economic Conditions

IVD

−4 −3 −2 −1 0 1 2

−0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30

Economic Conditions

IVD


Table 5: Implied volatility difference and economic conditionsMeasure of economic conditions

MKT GDP FST CLI


ECON -2.42 -3.58 -2.01 -2.71 -0.83 -0.63 -1.39 -1.47

(-5.34) (-5.10) (-4.82) (-5.01) (-2.55) (-1.37) (-3.85) (-2.74)

ECON · 1(ECON>0) 3.77 4.02 -0.96 0.26

(3.33) (3.85) (-0.87) (0.28)

R2 0.21 0.25 0.14 0.18 0.02 0.03 0.07 0.07Obs. 271 271 271 271 266 266 267 267


ECON -1.72 -2.60 -1.56 -2.39 -1.69 -2.01 -1.34 -1.10

(-3.49) (-3.51) (-3.03) (-4.63) (-3.00) (-2.09) (-2.15) (-0.86)

ECON · 1(ECON>0) 2.69 3.26 1.18 -0.57

(1.62) (2.75) (0.61) (-0.34)

R2 0.17 0.20 0.14 0.18 0.15 0.16 0.12 0.13Obs. 64 64 64 64 59 59 60 60


ECON -2.68 -3.93 -2.27 -3.02 -0.77 -0.54 -1.53 -1.88

(-5.04) (-4.86) (-4.84) (-5.08) (-2.10) (-1.10) (-3.91) (-3.18)

ECON · 1(ECON>0) 4.16 4.61 -1.26 1.23

(3.24) (3.89) (-0.95) (1.04)

R2 0.23 0.28 0.17 0.20 0.02 0.02 0.08 0.08Obs. 216 216 216 216 216 216 216 216



−0.5 −0.4 −0.3 −0.2 −0.1 0 0.1 0.2 0.3

−0.20

−0.10

0.00

0.10

0.20

0.30

Economic Conditions

VR

PD

−5 −4 −3 −2 −1 0 1 2

−0.20

−0.10

0.00

0.10

0.20

0.30

Economic Conditions

VR

PD


−3 −2 −1 0 1 2

−0.20

−0.10

0.00

0.10

0.20

0.30

Economic Conditions

VR

PD

−4 −3 −2 −1 0 1 2

−0.20

−0.10

0.00

0.10

0.20

0.30

Economic Conditions

VR

PD


Table 6: Variance risk premium difference and economic conditionsMeasure of economic conditions

MKT GDP FST CLI


ECON -2.98 -4.55 -1.97 -2.43 -1.32 -1.40 -1.72 -2.23

(-4.56) (-4.45) (-3.52) (-3.40) (-3.42) (-2.50) (-3.38) (-2.88)

ECON · 1(ECON>0) 5.10 2.62 0.35 1.75

(3.41) (1.67) (0.27) (1.44)

R2 0.19 0.24 0.08 0.09 0.04 0.04 0.06 0.07Obs. 271 271 271 271 266 266 267 267


ECON -1.62 -2.22 -1.32 -2.79 -1.78 -1.76 -1.18 -1.79

(-2.34) (-1.90) (-1.78) (-3.80) (-2.69) (-1.70) (-1.51) (-1.18)

ECON · 1(ECON>0) 1.86 5.79 -0.09 1.40

(0.92) (3.73) (-0.05) (0.70)

R2 0.16 0.18 0.11 0.25 0.18 0.18 0.09 0.10Obs. 64 64 64 64 59 59 60 60


ECON -3.45 -5.27 -2.32 -2.67 -1.40 -1.53 -2.01 -2.69

(-4.52) (-4.53) (-3.67) (-3.39) (-3.13) (-2.48) (-3.56) (-3.08)

ECON · 1(ECON>0) 6.07 2.17 0.71 2.37

(3.59) (1.18) (0.46) (1.56)

R2 0.22 0.28 0.10 0.10 0.04 0.04 0.07 0.08Obs. 216 216 216 216 216 216 216 216



−0.5 −0.4 −0.3 −0.2 −0.1 0 0.1 0.2 0.3−0.10

−0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

Economic Conditions

Slo

peD

−5 −4 −3 −2 −1 0 1 2−0.10

−0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

Economic Conditions

Slo

peD


−3 −2 −1 0 1 2−0.10

−0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

Economic Conditions

Slo

peD

−4 −3 −2 −1 0 1 2−0.10

−0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

Economic Conditions

Slo

peD


Table 7: Implied volatility slope difference and economic conditionsMeasure of economic conditions

MKT GDP FST CLI


ECON -3.19 -5.37 -2.20 -2.77 -0.78 -0.32 -1.62 -1.96

(-3.82) (-4.21) (-3.02) (-2.87) (-1.66) (-0.48) (-2.47) (-1.89)

ECON · 1(ECON>0) 6.86 3.11 -2.15 1.08

(3.62) (1.83) (-1.34) (0.66)

R2 0.19 0.26 0.09 0.10 0.01 0.02 0.05 0.05Obs. 238 238 238 238 233 233 236 236


ECON -2.11 -1.70 -1.21 -2.34 -0.31 -0.18 -0.98 -1.05

(-5.26) (-2.13) (-2.22) (-3.50) (-0.42) (-0.16) (-1.70) (-0.81)

ECON · 1(ECON>0) -1.17 3.38 -0.48 0.12

(-0.77) (2.13) (-0.19) (0.07)

R2 0.27 0.28 0.09 0.15 0.01 0.01 0.06 0.06Obs. 55 55 55 55 50 50 53 53


ECON -3.54 -6.15 -2.49 -3.01 -0.88 -0.28 -1.78 -2.20

(-3.58) (-4.28) (-3.01) (-2.86) (-1.66) (-0.38) (-2.41) (-1.88)

ECON · 1(ECON>0) 8.42 3.13 -3.10 1.41

(3.95) (1.63) (-1.68) (0.69)

R2 0.19 0.28 0.10 0.10 0.01 0.02 0.05 0.05Obs. 191 191 191 191 191 191 191 191


−0.4 −0.3 −0.2 −0.1 0

−0.08

−0.06

−0.04

−0.02

0.00

0.02

0.04

0.06

0.08

0.10

0.12

UNC

IVD

−0.4 −0.3 −0.2 −0.1 0

−0.04

−0.02

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

UNC

VR

PD

−0.25 −0.2 −0.15 −0.1 −0.05 0

−0.10

−0.08

−0.06

−0.04

−0.02

0.00

0.02

0.04

0.06

0.08

0.10

UNC

Slo

peD


Table 8: The role of election uncertainty

Measure of economic conditions

MKT GDP FST CLI

Panel A: Implied volatility (IV D)

UNC 1.87 1.64 1.58 1.80 1.82 1.68 1.69 1.44 1.45

(5.39) (4.52) (4.43) (4.93) (4.49) (4.11) (4.28) (3.21) (3.14)

ECON -1.46 -2.17 -1.47 -2.34 -1.36 -1.72 -1.52 -1.61

(-2.98) (-2.85) (-3.27) (-5.57) (-2.45) (-1.89) (-3.26) (-1.77)

ECON · 1(ECON>0) 2.14 3.41 1.34 0.22

(1.39) (2.65) (0.80) (0.17)

R2 0.20 0.31 0.34 0.32 0.37 0.29 0.30 0.26 0.26

Obs. 64 64 64 64 64 59 59 60 60




MKT GDP FST CLI

Panel B: Variance risk premium (V RPD)

UNC 1.06 0.82 0.78 0.99 1.03 0.74 0.74 0.89 1.00

(3.48) (2.39) (2.32) (2.54) (2.31) (1.91) (1.89) (1.75) (1.76)

ECON -1.49 -2.01 -1.27 -2.76 -1.64 -1.63 -1.29 -2.15

(-2.10) (-1.66) (-1.75) (-3.90) (-2.38) (-1.58) (-1.80) (-1.66)

ECON · 1(ECON>0) 1.59 5.87 -0.02 1.95

(0.79) (3.69) (-0.01) (1.09)

R2 0.07 0.20 0.21 0.17 0.32 0.21 0.21 0.14 0.17

Obs. 64 64 64 64 64 59 59 60 60




MKT GDP FST CLI

Panel C: Implied volatility slope (SlopeD)

UNC 0.25 -0.11 -0.14 0.07 0.11 0.25 0.24 0.23 0.24

(0.59) (-0.33) (-0.39) (0.17) (0.27) (0.50) (0.47) (0.56) (0.57)

ECON -2.13 -1.71 -1.20 -2.33 -0.28 -0.17 -0.98 -1.07

(-5.08) (-2.12) (-2.16) (-3.39) (-0.36) (-0.15) (-1.67) (-0.82)

ECON · 1(ECON>0) -1.20 3.40 -0.39 0.18

(-0.78) (2.13) (-0.16) (0.10)

R2 0.00 0.27 0.28 0.09 0.15 0.01 0.01 0.06 0.06

Obs. 55 55 55 55 55 50 50 53 53


Robustness

• Our results survive various additional tests

– Use all summits, instead of just the economically relevant subset

∗ Table 9

– Placebo test

∗ Tables 10, 11

– Alternative definitions of the variance risk premium

∗ Realized variance: Current-day instead of average over the option’s life

∗ Realized variance: AR(1) forecast instead of average over the option’s life

∗ Implied variance: Model-free instead of Black-Scholes

– Rerun regressions without combining events that are close in calendar time


Table 9: Robustness: All summitsMeasure of economic conditions

MKT GDP FST CLI

Panel A: IV D

ECON -1.85 -3.28 -1.74 -2.51 -0.69 -0.44 -1.13 -1.72

(-4.11) (-4.44) (-4.46) (-4.97) (-2.54) (-1.18) (-3.49) (-3.36)

ECON · 1(ECON>0) 4.00 4.27 -1.16 1.82

(3.69) (4.42) (-1.13) (1.97)

R2 0.14 0.19 0.12 0.16 0.02 0.02 0.05 0.06Obs. 310 310 310 310 310 310 310 310

Panel B: V RPD

ECON -2.76 -4.72 -2.03 -2.36 -1.10 -1.06 -1.82 -2.74

(-4.53) (-4.62) (-3.98) (-3.55) (-3.42) (-2.25) (-4.05) (-3.55)

ECON · 1(ECON>0) 5.50 1.79 -0.16 2.82

(3.88) (1.29) (-0.13) (2.27)

R2 0.18 0.25 0.10 0.10 0.03 0.03 0.08 0.09Obs. 310 310 310 310 310 310 310 310

Panel C: SlopeD

ECON -2.44 -4.95 -1.98 -2.55 -0.82 -0.22 -1.37 -2.17

(-2.91) (-3.64) (-2.94) (-2.86) (-2.11) (-0.38) (-2.36) (-2.18)

ECON · 1(ECON>0) 6.93 3.03 -2.80 2.34

(3.60) (1.92) (-1.88) (1.43)

R2 0.11 0.19 0.07 0.08 0.01 0.02 0.03 0.04Obs. 273 273 273 273 273 273 273 273


Table 10: Placebo events and mean differences


All MKT GDP FST CLI

Panel A: IV D

Mean (data) 1.43 2.57 1.94 2.22 3.00

Mean (pseudo-events) -0.32 1.35 -0.63 -1.63 -1.03

Difference 1.75 1.22 2.57 3.85 4.03p-value <0.001 0.017 <0.001 <0.001 <0.001

Obs. 271 271 271 266 267

Panel B: V RPD

Mean (data) 1.07 3.02 2.07 2.55 3.05Mean (pseudo-events) -0.26 0.21 -0.11 -0.44 -0.61

Difference 1.33 2.81 2.18 2.99 3.66

p-value <0.001 <0.001 <0.001 <0.001 <0.001

Obs. 271 271 271 266 267

Panel C: SlopeD

Mean (data) 1.73 3.26 2.11 2.66 3.02

Mean (pseudo-events) -0.31 0.41 -0.65 0.59 -0.46

Difference 2.04 2.85 2.75 2.06 3.48

p-value <0.001 0.005 <0.001 <0.001 <0.001

Obs. 238 238 238 233 236


Table 11: Placebo events and economic conditionsMeasure of economic conditions

MKT GDP FST CLI

Panel A: IV D

ECON Data -2.42 -3.58 -2.01 -2.71 -0.83 -0.63 -1.39 -1.47

Placebo -0.89 -1.29 0.77 1.21 0.81 0.95 0.79 1.10

Difference -1.53 -2.29 -2.77 -3.92 -1.64 -1.58 -2.18 -2.57

p-value <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001

ECON · Data 3.77 4.02 -0.96 0.26

1(ECON>0) Placebo 1.00 -1.55 -0.32 -0.73

Difference 2.77 5.57 -0.64 0.99

p-value <0.001 <0.001 0.593 0.102

Obs. 271 271 271 271 266 266 267 267

Panel B: V RPD

ECON Data -2.98 -4.55 -1.97 -2.43 -1.32 -1.40 -1.72 -2.23

Placebo -0.30 -0.51 0.51 0.88 0.38 0.73 0.47 1.12

Difference -2.68 -4.03 -2.48 -3.31 -1.70 -2.12 -2.19 -3.35

p-value <0.001 <0.001 <0.001 <0.001 0.002 0.003 0.001 0.002

ECON · Data 5.10 2.62 0.35 1.75

1(ECON>0) Placebo 0.68 -1.55 -0.72 -1.64

Difference 4.42 4.17 1.07 3.39

p-value 0.002 0.026 0.067 0.004

Obs. 271 271 271 271 266 266 267 267

Panel C: SlopeD

ECON Data -3.19 -5.37 -2.20 -2.77 -0.78 -0.32 -1.62 -1.96

Placebo -0.67 -1.00 0.71 1.27 0.52 0.71 0.74 1.22

Difference -2.52 -4.37 -2.91 -4.03 -1.30 -1.03 -2.36 -3.17

p-value <0.001 <0.001 <0.001 <0.001 <0.001 0.014 <0.001 <0.001

ECON · Data 6.86 3.11 -2.15 1.08

1(ECON>0) Placebo 0.55 -1.29 -0.83 -1.51

Difference 6.31 4.40 -1.31 2.59

p-value <0.001 <0.001 0.601 0.001

Obs. 238 238 238 238 233 233 236 236


Conclusions

• Political uncertainty is priced in the option market in ways predicted by theory

• Options whose lives span political events offer valuable protection against

– price risk

– variance risk

– tail risk

associated with major political events

• This protection is more valuable

– when the economy is weaker

– when political uncertainty is higher


Veronesi and Zingales (2010): Paulson’s Gift

• The 2008 financial crisis witnessed the largest intervention of the U.S. government in the financial

sector.

• The stated goal was to restore confidence to our financial system, through a massive transfer of

resources from the taxpayers to the banking sector.

• From an economic point of view, such an intervention is justified only in the presence of a market

failure.

– If this market failure is present, then the government intervention should create, not just

redistribute, value.

• Did this intervention create value or was it simply a massive transfer of resources from taxpayers

to financial institutions?

• If it did create value, why?

• What can we learn about the possible cost of financial distress in financial institutions?


What we do

• We estimate the costs and benefits of the U.S. government plan announced on Monday, October

13, 2008.

– The plan included a $125bn preferred equity infusion in the ten largest U.S. commercial

banks

– A three year Government guarantee on new unsecured bank debt issues.

• Compute effect on the enterprise value of banks

– Estimate changes in the value of existing debt using liquid CDS spreads

• Controlling for the market and GE capital variation in CDS spreads with find:

1. The intervention increased the value of banks financial claims by $131 billion

2. Taxpayers cost of $25 -$47 billions

– =⇒ net benefit between $84bn and $107bn.

3. Net benefit arises from a reduction in the probability of bankruptcy, which we estimate

would destroy 22% of the enterprise value.

4. The big winners were the Citigroup’s and investment banks’ bondholders. The losers were

JP Morgan shareholders and the U.S. taxpayers.


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Why Government Intervention May Create/Destroy Value?

• Stop a bank run

– Bank runs may be inefficient (Diamond and Dybvig (1983)) =⇒ an intervention to stop a

bank run may create value

• Who may be running?

– Not the depositors, but short-term creditors, who refused to roll over bank debt. (Gorton

and Metrick (2009)).

• Is there any evidence of a bank run?

– Use CDS spreads to compute

P (n) = Prob(Default at time n |No default up to n − 1)

– For each bank, compute the “Run Index”

Rit = P i

t (t + 1) − P it (t + 2)

– Normal times: Rit < 0. “Run times” Ri

t > 0.


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a \ b a \ b ] \ \ _ a b a ` b ] \ \ c ^ b ] ` b ] \ \ c ` b _ b ] \ \ c a a b a ^ b ] \ \ c ] b ] ] b ] \ \ ` _ b a b ] \ \ ` d b d b ] \ \ ` a ] b a ` b ] \ \ `

e f g h i j k l j m n op f q r j k l s j k g t up t q q v g g w u k m n


The Run Index

5/30/2008 6/26/2008 7/23/2008 8/19/2008 9/15/2008 10/10/2008 11/6/2008 12/3/2008

0

0.02

0.04

0.06

0.08

0.1

Ru

n I

nd

ex

Goldman Sachs

Morgan Stanley

Merrill Lynch

5/30/2008 6/26/2008 7/23/2008 8/19/2008 9/15/2008 10/10/2008 11/6/2008 12/3/2008−0.01

−0.005

0

0.005

0.01

0.015R

un

In

de

x

Citigroup

Bank of America

JP Morgan Chase

Wells Fargo


Why Government Intervention May Create/Destroy Value?

• Resolve inefficiency due to debt overhang

– Excessive leverage makes it hard to obtain new funds to exploit investment oportunities

– Capital injection that resolves the debt overhang and reduces its deadweight cost may create

value

– =⇒ Increase in bank value higher than tax-payer costs.

• However, government intervention may be costly:

– Increase inefficiency by restricting banks’ decisions that may reduce profits.

– Add political criteria in lending decisions, reducing profitability.

– Delay or limit the transfer of assets to most efficient managers / firms.


Event Study around the Announcment of Paulson’s Plan

• Typically, event studies concentrate on equity (abnormal) returns.

– For highly levered entities, such as banks, equity is not sufficient to proxy for enterprise value

– The impact of the announcement on the value of debt is important

• Problem: Bonds are quite illiquid, and so it is hard to measure the announcment effect

• We use variation in liquid CDS spreads to compute the change in value of debt:

Corporate Debt + PV (Insurance) = Risk Free Debt

=⇒ ∆Corporate Debt = −(PV1(Insurance) − PV0(Insurance))

• Given D(t) = existing debt that will still be outstanding debt at t, then

PVi(Insurance) =T∑

t=1

CDSi(t)

10000D(t)Qi(0, t)Z(0, t); i = 0, 1

– where Z(0, t) = zero coupon bond with maturity t;

– Qi(0, t) = risk neutral surviving probabilty up to t extracted from CDSi(t).


Event Study around the Announcment of Paulson’s Plan

• We need to control for other events taking place at the time.

• Use GE Capital CDS variation as control. Compute

Adj.∆PV (CDS) = ∆PV (CDS) − PV0(CDS)∆PV GE(CDS)

PV GE0 (CDS)

• Overall, the bonds gained $120bn in value.

– The bonds of the three old investments banks gained the most = $87bn.

– Among the old commercial banks Citigroup stood to gain the most, both in level, $21bn,

and in percentage of outstanding debt, 5.3%.


x y z { | } ~ � � y � � | � � � � | � y { � | � � � � � � x | � � � | z � y � � � � � � � | � � � � � � � | � | � � � � � � |� | � � � | � � y � { � � � � { y �

� � � � � � � � � � � � � � � � � � � � � � � ¡ � ¢ � � £ � � � ¤ ¥ � £ � � £ ¡ � � � � ¦ § � � � � � ¡ � £ ¢ � � � � § � � £ � � § � � � � � ¨ © ª � � � ¤ � « � � �¬ � � ¥ ¢ � � ® ¨ © ª � § � � ¢ � ¢ � � � � � ¡ � � � ¤ � � � « � « ¤ � � � ¡ � � ¯ ° ± � � � � � � � ² ° ³ ³ ´ ² ¯ ° ± µ � « � � � � � � � � ´ ¶ · ª � � � · �� ¤ ¥ � � ª � � � « � � � � � � � � � � ¢ � ¸ ³ ¹ ¨ © ª � � ¡ º ¤ � � � ¡ ® � � £ � � � ª � § � � � � £ � � � ¤ � � � � ª � � � ¡ ¤ « � � � £ � £ � £ � ¤ � � £ « �« � � � � § � � ¡ � £ � � ª � ¡ � ¢ � � ¤ � � � � £ ¡ � £ ® ¶ · � � ª � ª � � « � ¤ � � � � ¤ « � ¤ � � � � ¥ � � ¤ � � � ¶ � � � � � � ¤ � � � � ¡ � � § � £ � ª � � � �� ¶ � ¡ º ¤ � � � ¡ � � � � ª � § � � « � £ � � ® � � � ¡ ¤ « � � � £ � £ » ¼ « � � � ¨ ½ � � ¡ � � « � ¤ £ � � � � � · � ¤ � � ¾ ¨ ¿ ¹ ¨ © ª � ¡ � ¢ � � £ ¡ � ª �� ¡ º ¤ � � � ¡ ® � � £ ¡ � � � � � � � £ ¢ � � � £ � � � À � Á ¨

Â Ã Ä Å Æ Â Ã Ä Å ÆÇ È É Ê Ë Æ Ä Å Ì Ç È É Ê Ë Æ Ä Å Í Î Ï Ð Ñ Ò Ó Ô Õ Ö × Ò Ø Ù Ú Û Ñ Ò Ó Ü Ý Þ ß Ñ Ò Ó Ü Ñ à á Ô â Ö Ú Û Ñ Ò Ó Ü Ý Þ ß Ñ Ò Ó Ü

ã ä å ã ä å ä æ ã ä å ã ç å ä æ Þ × è é ê â × Þ × è é ê â × Þ × ë Ö Ú Ï ê â Ú Ï ê â ì í î ï í ð ñ ò ó ô õ ö ÷ ô õ ö ÷

ø ö ù ö ú û ü ý þ ÿ � � � � � � � � � � � � � � � � � � ÿ � � � � � � � � � ÿ � � � � ÿ � � ÿ � � ÿ � ��

� ! " # $ % & ' ( ) * + , , * - . / * + - * - ) + + * / 0 * ( / * ) , + * , - * , - * 12 3 4 5 6 7 8 3 9 : ; < = > ? @ < 9 > = A < B B C < > > A B < A ; < = A < C > B < C ? < C ? < CD E F F G H I J K L M N O P Q N R P N R O P R S P S M S T P R M P O M P N M S P N S P M S P M

U V W X Y Z [ \ ] ^ _ _ Y W ` a b ac d e d f c d g f f d h i jk l m n o p q r s t u t v t w u x y y z u y v w s u w w x { u s w x u { w | u t w t y u | { u x x u w

} ~ � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �� ¡ � ¢ £ ¢ ¡ £ � � ¡ ¤ ¢ � ¢ £ ¡ � ¥ ¡ ¦ � £ £ ¤ ¢ ¤ ¢ �

§ ¨ © ¨ ª « ¬ ¬ ¨ ® ¯ ª ° ® ± « ² ° ¯ « ¬ ³ ´ µ ¶ µ · ¸ ³ ¶ ¹ º » · ¶ »¼ ½ ¾ ¿ À Á Â Ã Ä Å Æ Ç È Å É Ê Ç Ë Æ Ê Ç Ä

Ì Í Î Ï Ð Ñ Ò Ó Ô Ó Ñ Ô Õ Ö × Ñ Ø Ñ Ö Ø Ù Ú Û Ö Ó Ô Ú Ö Ø Ô Ó Ö Õ

Ü Ý Þ ß à á â ã ä á å æ ç á æ ä á â è é ê æ è é á ë ê ì ê å í á î


Event Study on Equity

ï ð ñ ò ó ô õ ö ÷ ø ø ÷ ñ ù ú û ü ý þ

ÿ � � � � � � ÿ � � � � � � � � � � �

� � � � � � � � � � � � � � � � � � � � �� ! � � " # � � � � � � $ � % # � � � & � � � � ' ( � � � # � � � & � � � � ' ( � � �

) � � � * � + $ � , - . / 0 1 . 0 , 2 . 3 4 2 . 4 1 2 . 1 2 1 - . 1 , . 05 6 7 8 9 : ; < = > ? @ 6 A B C D E A F D B G B D F E B D A H B D B C A H D I C D C

J K L M N O P Q R S P T U V W W X V Y V X Z Z [ \ X \ ] [ \ X V ^ [ \ X ] ] [ ] \ X W [ ^ ^ X _` a b c d e f a g g h i j k h l m i h l n i h g l o i h l k g h n o l h jp q r r s t u v w x y z { y y | { } z ~ { | � ~ { ~ } � ~ { ~ | } { ~ � ~ { �

� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �� ¡ ¢ � ¡ £ £ ¤ � £ ¥ £ £ � ¤ ¤ � ¥ ¦§ ¨ © ª « ¬ ® ¬ ¯ ° ± ² ³ ´ µ ¶ ¶ ´ · µ µ ´ ² ³ µ ´ ¸ ¹ µ ´ ¸ ¶ ¶ µ ´ º ¹ ´ »¼ ½ ¾ ¿ À Á Â Ã À Á Ä Å Æ Ç È É Ê Ë Ì É Ç Í Ç É Ì Ê Ç É Ç Î Ç É È Ï Ç Ì É Ë Ç Ç É ÈÐ Ñ Ò Ò Ó Ô Ô Õ Ö × Ø Ù Ú Û Ü Ú Ý Ú Ü Þ ß Ý Ü à á Ý Ü Ý Û â Ý Ü à à à Ü Ú â Ú Ü ã

ä å æ ç è é ê é ë ì é í ë î ï ð ë îñ ò ó ô õ ö ó ÷ ø ù ú û ø ü ý û ø ÷ ü û ø ù û

þ ÿ � � � � � � � � � � � � � � � � � � � � � � �

� � � � � � � � � � � � � � � � � � � �� ! � ! " # $ � � � % � � & % ' $ � � � ( � ) � � * + � � � $ � � � ( � , - . / 0 1 . 2

3 4 . 4 5 6 7 8 9 : ; < = > ; ? @ A ; ? B A ; C D A ; C C C ; < C ; >E F G H I J K L M N O P F Q Q R S T U R Q V U R S S U R Q Q U R S U Q R S S R S

W X Y Z [ \ ] ^ _ ` ] a b c d e f g d h c g d i f g d g i g d g j g d g g d hk l m n o p q l r s t u v s w x u s w u u s u t u s u y u s r u s z{ | } } ~ � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �

� � � � � � � � � � � � � �� ¡ ¢ £ ¤ ¥ ¢ £ ¦ ¢ ¢ £ § ¨ ¢ £ ¨ ¢ ¢ £ ¨ ¦ ¢ £ ¨ ¢ £ ¨© ª « ¬ ® ¯ ° ® ± ² ³ ´ µ ¶ ´ · µ · ¸ · µ · ¶ · µ ´ ¹ · µ ´ · ´ µ ¶ ´ µ ¶º » ¼ ¼ ½ ¾ ¾ ¿ À Á Â Ã Ä Å Æ Ç È Å Ç É Ç Å É Ê Ç Å Ë Ì Ç Å Ë Ì È Å É È Å Ë

Í Î Ï Ð Ñ Ò Ó Ô Õ Ö Ô Õ × Ô ÓØ Ù Ú Û Ü Ý Ú Þ ß à á Þ ß â ã Þ ß ä å Þ ß ä à

æ ç è é ê ë ì í ê î ï ð ê è ñ ì í ð î ò è ó ê î ì ô î


Aggregate Results

õ ö ÷ ø ù ú û ø õ ö ÷ ø ù ú û ø õ ö ÷ ø ù ú û ø õ ö ÷ ø ù ú û ø ü ú ý þ ÿ � û � ø � ú � ÿ � � � � ú � ÿ � � � � � õ � � � � �� ö ú � ÷ � þ ú � ö ú � ÷ � þ ú � � � ö ú � ÷ � þ ú � ÷ � þ ú � � û ø � ö ú ÿ � � � � � þ ø � ú ÿ þ � ú ý ú � � ú ø ý ú ø ý

� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � ��

! � � � � � � � " # $ % # & % & # ' ( # " & # & ) ) # ) ' # * ) # ( ( # ' * # % % + # & & # % ,- . / 0 1 2 3 4 5 6 7 8 . 9 : 9 ; : ; 9 : ; < : = < : > = = : > = : ; < : ; < : < = : 9 ? : @ < : A B

C D E F G H I J K L I M N O P P Q R S Q T P Q R S Q U S Q U O U V Q T W Q X S Q R S Q P U Q Y O P U Q W O W Q T Z[ \ ] ^ _ ` a \ b c d e f d g e d h f d i f d e h d j f d e f d c f d f f d j h d f f d e kl m n n o p q r s t u v w x v w y y w z v w v v w { y w | y w x v w v v w v y w x u v w y v w v }

~ � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �� ¡ ¢ £ ¤ ¢ ¥ ¦ § ¨ © ª « © ¬ ¬ ¨ © ¬ © ® « © « ¯ ¨ © « « © ¬ ® © ° « © « ® © ¯ ® © ® ¯ © ¬ ±² ³ ´ µ ¶ · ¸ ¹ ¶ · º » ¼ ½ ½ ¾ ¿ ¿ ¾ À Á ½ ¾ Â Ã ¾ Ä ¿ ¾ Å Â Â ¾ ¿ ½ ¾ Ä Ã ¾ ½ ¿ ¾ ¿ À ¾ Á Â Ã ¾ Á Â ¾ Ä ÆÇ È É É Ê Ë Ë Ì Í Î Ï Ð Ñ Ò Ó Ô Õ Ó Ò Õ Ö Ó × × Ó Ø × Ó Ù Õ Ò Ó Ö Õ Ó Ú Õ Ó Ö × Ó × Ò Ó Û Û Ó Ù Õ Ó Õ Ü

Ý Þ ß à á â ã ä å å æ ç æ å ß æ è è à å ä é ê ë ì í î ì ï ð ë ñ ì ò ò ì ó ó ì ï ð ó ó ì ó ó ò ì í ð ñ ì í ñ ì ï ô ï ì ó í î ì ñ ñ ì í õö ÷ ø ù ú û ÷ ü ý þ ÿ � � � ø � ù � ý ú � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �� ! " # $ " % & ! ' " ( ( " ) ) " % & ) ) " ) & ) " ! & ' " # ' " % ! * " # & ' # " $ & " ' +

, - . / 0 1 . 2 0 3 4 5 6 7 . 5 6 8 9 :; < = > ? @ A B C C D E D C = D F F > C B G H I J K L M K N M L K O P K O P K O M Q K J J P K Q L K Q R K Q N P K P P S K P R K P TU V W X Y Z V [ \ ] ^ _ ` a W b X c \ Y d e f g h i j h k j i h l m h l m h l j n h g m k h m i h n o h n g g h j g g h n o h g pq r s t u v r w s x y x z s x { { t z | } ~ � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �


Taxpayer Costs and Aggregate Effect

• Government receives securities in exchange of $125 bilion equity infusion.

– Preferred equity with 5% coupon, increasing to 9% after 5 years.

– 10-year warrants

• Valuation of these securities is sensitive to assumptions. We consider many scenarios to obtain

upper and lower bounds.


Taxpayer Costs and Aggregate Effect

� � � � � � � � � � � � � � � � �

� � � � � � � � � � � � � � � � � �

� � � � � � � ¡ � ¢ £ � ¤ ¥ ¦ ¡ § � � ¥¨ ¤ ¡ � � ¡ ¥ £ © ª « ¨ ¤ ¡ � � ¡ ¥ £ © ª « ¨ � ¥ ª « ¨ � ¥ ª «

¬ ® £ ¥ ¯ ° ª « ® ¡ � ± ° ª « ® ¡ � ± ¨ ¤ ¡ � � ¡ ¥ £ © ª « ² £ ± ± ¡ � ¡ � © ¡ ¨ ¤ ¡ � � ¡ ¥ £ © ª «³ � ± ® ¢ £ � � ´ � ¡ ± ¡ � � ¡ µ ¶ ª � � ª � ¥ ° ª « ® ¡ � « ª £ · ° ª « ® ¡ � « ª £ · ² £ ± ± ¡ � ¡ � © ¡

� £ ¥ £ � � � ® § ¸ ¹ º » ¼ º ¸ ¼ ½ ¸ ½ ¼ ¸ ¾ ¼ » º ¹ ¼ ¹ ¿ ¼ ÀÁ Â Ã Ä Å Æ Ç È É Ê Ë Ì Â Í Î Í Ï Ð Ñ Í Ð Í Í Ò Ð Ó Í Ð Ï Í Ï Ð Î Ï Ð Ô

Õ Ö × Ø Ù Ú Û Ü Ý Þ Û ß à á â á ã ä á á ä å á æ ä á ã ä ç á å ä è é ä êë ì í î ï ð ñ ì ò ó ô õ ö ô ò ÷ ô ó ö ô ø ÷ ô ù ö ô úû ü ý ý þ ÿ � � � � � � � � � � � � � � � � � � � � � �

� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � ! " ! # ! $ # # ! % & $ ' ( ) * + , * ( ( * , , * , ) * + , * -. / 0 1 2 3 4 5 3 6 7 8 9 : ; < = 9 < : = < = : < 9 > < ? @ < AB C D E F G H I F G J K L M N O P O M P N Q P R M P S T P Q S P UV W X X Y Z Z [ \ ] ^ _ ` a b c d a c e e c f ` c b b c g g c h

i j k l m n o p q r n r n q s n r q p n n n q t n s q o u v q v o t q w

x y z { | } { y } | ~ � � � �

� � � � � � � � � � � � � � � � � � � � � � � � ��

� � � � � � � � � � � � � � � � � � � � � � � � � � � � � ¡ � � � � � � �¢ � � � � � � £ � � � � � � � ¤ � � � � � � � � � � � � � � � � � � � � � � � � � � ¡ � � � � � � �

� � � � � � � � � ¥ ¦ § ¨ © ¦ § © ¦ § ª © « ¬ © « § ¦ © ¦ © ¦® ¯ ° ± ² ³ ´ µ ¶ · ¸ ¹ ¯ º » ¼ ½ ¾ ¿ ½ ¼ º ¿ ½ » À ½ » º ¿ ½ » À ½ »

Á Â Ã Ä Å Æ Ç È É Ê Ç Ë Ì Í Î Ï Ð Ñ Ò Ï Ñ Ò Ï Ó Ñ Ô Ô Ñ Õ Ï Ó Ñ Ô Ô Ñ ÕÖ × Ø Ù Ú Û Ü × Ý Þ ß à á ß â ã ß ã ã ß ä ã ß ã ã ß äå æ ç ç è é ê ë ì í î ï ð ñ ò ï ð ò ï ð ó ò ï ó ò ï ð ó ò ï ó ò ï

ô õ ö ÷ ø ù ú û ü ý þ þ ø ö ÿ � � � � � � � � ÿ � � � � � ÿ � � �� ! � " � # ! � � $ ! $% & ' ( ) * + , ) * - . / 0 1 2 3 4 1 3 5 2 3 6 7 3 0 8 3 9 : 3 4; < = = > ? ? @ A B C D E F G H I F H J J H K L H J J H K L H J

M N O P Q R S T U V W R U X Y U Z W X U S Z T U W W T U Z Z X U Y

[ \ ] ^ _ ` ^ \ ` _ a b c d e


Cost of Debt Guarantee and Deposit Insurance

f g h i j k l m n o p n q p r j s g t u v j h p w x g y g t p j j z y n { | } j } h ~ p r j � v � m

� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �� ¡ � � � � ¢ � � £ � � � � �� ¤ � � � � � � � � � � � � � � � � ¥ £ � � � � � � � � � � ¤ ¦ § ¨ � � � � � � � � � � � � � � £ � � � � £ � � � � � � � � � � � � � � � � � � � � � � � � �� ¢ � � � � � � � ¥ £ � � � � � � � � � � � � � � £ � � � � ¢ � � £ � � � � � � � � � � � � � � � � � � � ¡ � � � � � � ¢ � � £ � � � � � � � ¥ £ � � � � � � � © � � � � � � �� ¢ � � £ � � � � � � � � � � ¥ £ � � � � � � � � ª � � � £ � � � � � � � � � � � � � � � � « � © ¬ § � � � � � � � � � � � � � � � � � � � ¢ � � £ � � � � � �� ¥ £ � � � � � � � ª � ¢ � � � � � � � � � � � � � � � � ¥ £ � � � � � � � © � � � � � � � � � � � � � � � � ª � � � � ¢ � � £ � � � � � � � � � � � � � � ® � ¯ ��

° ± ² ³ ´ µ ¶ · ¸ ¹ µ º ± ° ± » ¼ ¶ ½ ³ ´ µ ¶ · ¸ ¹ µ º ±¾ µ ¶ ± º · ¸ ± ¿ À Á Â ± ¹ ¸ Ã ° Ä Å Æ Ç È É Å Æ Ç È É Ê Ë Ì Ë Í Î Ï Ð Ñ Ò Ó Ô Õ Ö × Õ Ø Ù Ú Û Ü

Ý Þ ß à á á â à ã ä å à â æ ç è é ê ë ê ì í î î ï ð ñ ò ó ñ ô õ ñ ö ÷ ø ö ó ù ú û ü ý þ ö ÿ � � � � � � � � � � � � � � �� ! " � # � � � � � � � � � � � $ � � � � � � � �

% & ' & ( ) * + , - . - / 0 - 1 1 / 0 - 2 3 / 4 4 / . 1 5 / 6 . / 4 0 - / -7 8 9 : ; < = > ? @ A B 8 C D E F G H I F C C G J F K L F J H E F I L F L K L F K

M N O P Q R S T U V S W X Y Y Y Z [ \ Y Z [ Y ] ] Z ^ _ Z ` a Y ] Z a _ Z Y \ _ Z Yb c d e f g h c i j k l i i l k m i n k o p k j i o k i p k p p p k lq r s s t u v w x y z { | } ~ � | � ~ z | { { | { � � | � { | { { { | ��

� � � � � � � � � � � � � � � � � � � � �� ¡ ¢ £ ¤ ¥ ¦ ¤ § ¨ © ª « ¬ ® ® ¬ ¯ ° ¬ ± ² ¬ ³ ° ¬ ° ° ¬ ° ° ° ¬ °´ µ ¶ · ¸ ¹ º » ¸ ¹ ¼ ½ ¾ ¿ À Á Â À Ã Ä Á Å ¿ Æ Á Ç Â Á ¿ Ä Á Ä Ä Á Ä Ä Ä Á ÇÈ É Ê Ê Ë Ì Ì Í Î Ï Ð Ñ Ò Ó Ô Õ Ò Ö × Ô Ó × Ò Ô Ö Ö Ô × Ø Ô Ø Ø Ô Ø Ø Ø Ô Ù

Ú Û Ü Ý Þ ß à à á â ã ä å á ä ä æ á ã â ä æ á ã æ á ç â á ç


Where Does the Value Come From?

• One possibility is that the capital infusion and the renewed access to funds enables banks to

take advantage of the positive net present value lending opportunities.

– Yet, we know from Ivashina and Scharfstein (2010) that the discretionary lending of the

major banks went down, not up during this period.

• Another possibility is to stop an inefficient bank run.


Enterprise Change, Run Index, and Past Performance


Computing Bankruptcy Costs

• The value of a firm can be written as the value of an unlevered firm minus the present value of

bankruptcy costs.

V =

∞∑

t=1

CF (t)

(1 + r)t− BC × P

– where P =[∑5

j=1

∏i<j(1−pi)pj

(1+r)j+

∏5i=1(1−pi)p5

(r+p5)(1+r)5

].

• Therefore, we have

V1 − V0

V0=

∑∞t=1

CF1(t)(1+r)t

−∑∞

t=1CF0(t)(1+r)t

V0−

BC

V0× ∆P

• Exploiting the (limited) cross-sectional information, we obtain

∆V i

V i0

= −.025∗∗∗ − 0.22∗∗∗∆P i

• =⇒ The cost of government intervention is about 2.5% of firm value.

• =⇒ The bankruptcy cost is 22% of firm value.


Calibrating a Merton’s Model

• We calibrate a simple Merton (1974) model modified to take into account the possibility of a

liquidiyt shock (bank run) at the time of rolling over banks debt.

• We use this model for three exercises

1. Check the transfer of wealth from equity holders to bond holders due to a pure capital

infusion without any other deadweight cost.

– Model implies transfer of $30bn from equity holders (government) to bondholders.

– But it cannot explain the changes in value of equity and debt as measured in the data.

=⇒ Additional benefits from eliminating bankrun

2. Check the model can explain the change in equity and debt around the announcment, and

use it to compute the change in the probability of a run and the recovery rate.

3. Use the estimated model to do conterfactuals: Could the government do any better by using

other policies?


è é ê ë ì í î ï ð ñ ò ó ó ë ô õ ì ö õ é ÷ ñ ÷ ø õ ù í ú ÷ û í óü ý ý þ ÿ ý � � � � � � � þ � � þ � ÿ � � þ ü ÿ � ÿ � þ � þ � ý ÿ � þ � � � � þ � � � ý � � � ÿ ÿ þ � � � þ � ÿ � � � � þ � � ý � ÿ ý � � � þ � þ � � � ÿ ÿ � � ý ÿ � � þ � ÿ � þ � þ � ý � ý � � � � � � � � � � ÿ � � � � � � � � � � � ÿ � ý � � � � � � � � � � � þ � � � þ ý ÿ � þ� � � þ � � � ý ý þ ÿ ý � � � � ÿ � � ÿ � ý � � � ÿ � þ � � � � � � ÿ � ý � � � � � � ÿ ý ÿ � þ � � � � � � � � � � � � ! � � ÿ �

� � � � " # � � ÿ � þ � þ � ý � þ � � � ÿ � ÿ � ! � ÿ � � ý � � ý þ � þ � � � ÿ � � � � � � $ ÿ þ � � � þ � ÿ � � � þ � ý � � þ � � � þ �� ÿ � � � � þ ý � � � ÿ ÿ þ � � � � � � � � � þ � ý � þ � þ � � þ � � � � ! � � � � � % # � � � ý ý þ ÿ ý � � � � þ � � � þ� � � � � � � � $ ÿ � � � $ � � � � � � � � þ � � ÿ � & ü ÿ & � � þ � � � ÿ � � � � � ý � � � � & � " # & ' ( ) * + ,

- . / 0 1 * 2 * 3 . ) 4 5 , 6 1 7 * + , 7 8 3 9 3 8 , * + , 4 + . 7 * * , 7 / 8 , : * 3 ) 8 , 0 . 4 3 * 4 2 ) 8 4 + . 7 * * , 7 / 8 , : * ; 5 + 3 < , < . ) =* , 7 / 8 , : * 3 ) - < 1 8 , 2 < 4 . . * + , 7 < 3 2 : 3 < 3 * 3 , 4 >

? @ A B C D E F G D G H IJ K E L G M N O P H I Q R A IS H A B T G N A I

ü U ÿ V

S L K E L W L G M X Y @ Z [\ D M ] B ^ G D G H _

` a b ` c

d a d c

ü U e VA B C D E F G D G H IJ K E L G M N fS H A B T G N O P H I Q


Default Probability and Recovery Rates from Merton’s Model

g h i j k l m n o p h q r k s q t p k u h j v k w x h y y k t y s z { j s k | i } t p k z w | k j

~ � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �� ¡ � � � � � � � � � � � � � � � � � �� ¡ � � � � � � � � � � � � � � � � � � � � � � � � � � � ¢ � � � � � � ¡ � � � � � � � � � � £ � � � � � � � � � � � � � � ¡ � � � � � � � � � � � � � � � � � � � � � � � � �� ¡ � � � � � � � � � ¤ ¥ � � � � � � � � � � � � � � � � � � � � � � � ¡ � � � � � � � � � ¦ ¥ � � � � § ¥ � � � � � � � � � � � � � � ¡ � �� ¨ © ¤ � � � � � § ¥ � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � ª � � �« � � � � � � � � � ¬ � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � ª � � � « � � � � � � � � � ¬ � � � � � � § ¥ � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �� ª � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � ¥ � � � � ® � � � � � � � � � � � � �� § � � � � � � ¯ � � ª � � � � � � � � � � � � � � � � � � � ° ¤ ¯ § ¥ � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �� ª � � � � � � § ¥ � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � §

± ² ³ ´ µ ¶ ³ · ¸ ¹ º ³ » ¶ ¼¹ ² ² · ³ ¹ ² ² · ³ ½ ¾ ¿ À ¿ Á Â · º ¿ Ã · ¾ Ä ¹ ² ² · ³ ¹ ² ² · ³ ½ ¾ ¿ À ¿ Á Â · º ¿ Ã · ¾ Ä º Å ¶ Æ Ç · ² ´ Æ º Å ¶ Æ Ç · ² ´ ÆÈ ¿ ¼ ¶ ³ ´ ¼ ´ ³ Ä È ¶ ¼ » · ¾ » Æ Â ¶ ³ · È ¿ ¼ ¶ ³ ´ ¼ ´ ³ Ä È ¶ ¼ » · ¾ » Æ Â ¶ ³ · ¶ ² ² · ³ Ã ¶ ¼ » · ¶ ² ² · ³ Ã ¶ ¼ » ·

É Ê Ë Ê Ì Í Î Ï Ð Ñ Ò Ó Ô Õ Ñ Ô Ó Ô Ò Ö Ö Ö Ò × Ó Ø Ò Ø Ô Õ Ñ Ù Ó Ö Ò Ú Ñ Ö Ò × Ñ × Ñ Ò Ø Û Ô Ò ÙÜ Ý Þ ß à á â ã ä å æ ç Ý è è é ê è ë ì ê í î é ï ì î é ð è è è é ð è ë ì ñ í î é ï è î é ð è è î é ñ è î é ò

ó ô õ ö ÷ ø ù ú û ü ù ý þ ÿ � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � ÿ � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �� ! " # $ % & ' ( & $ % # ) $ % # * & + % # ' ( # $ % # & $ % # * $ % + & % &

, - . / 0 1 2 3 4 5 6 6 0 .7 8 - 8 5 7 8 9 5 5 8 : 0 9 ;< 0 6 = > - . 7 - ? @ A B C D E E E F C E F D C G H I C G F J D G K F C G L D C G M G K C M G L C MN O P Q R S T U R S V W X Y Z [ \ ] ^ ^ Z Y _ [ Z _ ^ ^ Z ` \ \ Y ` Z _ Y [ Z _ a a ] Z ` a ] Z bc d e e f g g h i j k l m n o o p q r n s p p n t u v n t o r q r n p u p n t o r p n q r r n u

w x y z { | y } ~ � } } � � ~ � � � � � ~ � � ~ � ��

� � � � � � � � � � � � � � � � � � � � � � � � � ¡ � � � � � � � � � � � � � � � � � � � � � ¢ � � � ¡


Costs of Alternative Plans

£ ¤ ¥ ¦ § ¨ © ª « ¬ ® ¬ ¯ ° ¦ ® § ± ² ¤ ® ³ ´ § µ ¦ ¤ ²

¶ · ¸ ¹ º » ¼ ½ ¾ ¿ ¾ » ¹ À Á ¾ ¹ º · ¾ Â ¾ Ã ¸ ¹ ¾ Ä Å » À ½ ¹ Æ Ç Å ½ » Ç » ½ Æ Ç È É ¸ Ã ¾ Ä ¸ ¿ ¾ Ç ¹ ¸ Æ Ç ¹ » Ç Ä Ê Æ ¿ Ë » Á ¾ ¹ ¸ º » ½ Æ Ç È º · ¾ ¹ ¾Ä ¸ ¿ ¾ Ç ¹ ¸ Æ Ç ¹ Ì ¸ º · É Æ À Á » ½ º ¾ Á Ç » º ¸ Ã ¾ ¹ Í ¶ · ¾ É ¸ Ã ¾ Ä ¸ ¿ ¾ Ç ¹ ¸ Æ Ç ¹ » Á ¾ Î º · ¾ » ¿ Æ À Ç º Æ É É À Ç Ä ¹ Á ¾ Ï À ¸ Á ¾ Ä ¼ Ð º · ¾ Ë ½ » Ç Ñ º · ¾¾ Ò » Ç º ¾ Ê Æ ¹ º Æ É º · ¾ Ë ½ » Ç É Æ Á º » Ò Ë » Ð ¾ Á ¹ Ñ º · ¾ Ó Ô Õ Ô Ö Ó Ô Ö × Õ Ø Ã » ½ À ¾ » º Á ¸ ¹ Ù É Æ Á º » Ò Ë » Ð ¾ Á ¹ Ú Û Ü Ë Á Æ ¼ » ¼ ¸ ½ ¸ º Ð Æ É » ½ Æ ¹ ¹ ¸ Çº · Á ¾ ¾ Ð ¾ » Á ¹ À Ç Ä ¾ Á º · ¾ » Ê º À » ½ Ë Á Æ ¼ » ¼ ¸ ½ ¸ º Ð Ý Ñ º · ¾ Þ × ß à ß á Ö × Ã » ½ À ¾ » º Á ¸ ¹ Ù É Æ Á º » Ò Ë » Ð ¾ Á ¹ Ú Û Ü Ë Á Æ ¼ » ¼ ¸ ½ ¸ º Ð Æ É » ½ Æ ¹ ¹¸ Ç º · Á ¾ ¾ Ð ¾ » Á ¹ À Ç Ä ¾ Á º · ¾ Á ¸ ¹ Ù Ç ¾ À º Á » ½ Ë Á Æ ¼ » ¼ ¸ ½ ¸ º Ð Ñ Ì · ¸ Ê · ¹ À ¼ ¹ À ¿ ¾ ¹ º · ¾ É » Ê º º · » º º · ¾ Ê Æ ¹ º ¹ Æ É É À Ç Ä ¹ ¸ Ç Ê ¾ Á º » ¸ Ç¹ º » º ¾ ¹ Æ É º · ¾ Ì Æ Á ½ Ä ¸ ¹ · ¸ È · ¾ Á Ý Ñ » Ç Ä º · ¾ Ë ¾ Á Ê ¾ Ç º » È ¾ Æ É Æ Ì Ç ¾ Á ¹ · ¸ Ë º · ¾ â Æ Ã ¾ Á Ç ¿ ¾ Ç º Ì Æ À ½ Ä · » Ã ¾ » Ê Ï À ¸ Á ¾ Ä ¸ É ¸ º¸ Ç Ã ¾ ¹ º ¾ Ä ¸ Ç ¹ º Á » ¸ È · º ¾ Ï À ¸ º Ð Í ã ½ ½ º · ¾ Ë ½ » Ç ¹ ¸ Ç Å » Ç ¾ ½ ã » Á ¾ Ê Æ Ç ¹ º Á » ¸ Ç ¾ Ä º Æ Ä ¾ ½ ¸ Ã ¾ Á » Á ¾ Ä À Ê º ¸ Æ Ç ¸ Ç ä å æ Á » º ¾ ¹ » º½ ¾ » ¹ º » ¹ ¼ ¸ È » ¹ º · ¾ » Ä ç À ¹ º ¾ Ä Ä ¾ Ê ½ ¸ Ç ¾ Á ¾ Ë Æ Á º ¾ Ä ¸ Ç ¶ » ¼ ½ ¾ è Í ã ½ ½ º · ¾ Ë ½ » Ç ¹ ¸ Ç Å » Ç ¾ ½ é » Á ¾ Ê Æ Ç ¹ º Á » ¸ Ç ¾ Ä º Æ Ä ¾ ½ ¸ Ã ¾ Á »Á ¾ Ä À Ê º ¸ Æ Ç ¸ Ç ä å æ Á » º ¾ ¹ » º ½ ¾ » ¹ º » ¹ ¼ ¸ È » ¹ º · ¾ Á » Ì Ä ¾ Ê ½ ¸ Ç ¾ Á ¾ Ë Æ Á º ¾ Ä ¸ Ç ¶ » ¼ ½ ¾ è Í ã ½ ½ º · ¾ É ¸ È À Á ¾ ¹ » Á ¾ ¸ Ç ¼ ¸ ½ ½ ¸ Æ Ç ¹Æ É ê æ ë Í

ì í î ï ð ñ ò ó í ô õ ï ö ÷ í ø ù ú û ö ï ø ô ï ø ú ü ö ý þ î ý î ÿ � � ô í ö ï û

� � � � � � � � � � � � � � � ��

� � � � � � � � � � � � � � � � � � � � � � � � ��

� � � � � � � � � � � � � ! " # $ % & ' ( ) # $ " * ! &

+ , - . / 0 - - / - 1 2 3 1 4 , 5 0 6 7 8 9 7 9 : ; 8

< = > ? @ A B C D A D E F D E G A H I A H J @ A D K E F L M N O P Q R N O S M T

U V W X Y Z [ \ ] ^ _ ^ ` a ] b Z c d Y Z e f a g h i i j k l k i j l m k n o

p q r s t u v w q x u y z s { | q } y ~ u � y u � � � � � � � � � �

� � � � � � � � � � � � � � � � � � � ��


The Run Index After the Crisis

� � � � � � � � � � � � � � � ¡ ¢ £ ¤ ¢ ¥ ¤ ¤ ¦ § ¨ © © ¨ ª « « ¬ ® ¯ ° ® ± ² ² ³ ´ µ ¶ · µ ¸ ¹ ¹ ´ º » ¼ ½ » ¼ ¾ ¿ ¾ À Á Â Ã Á Â Ä Å Ä

Æ

Ç È Ç É

Ê Ë Ê Ì

Í Î Í Ï

Ð ÑÒÓ ÒÔ ÕÖ

× Ø Ù Ú Û Ü Ý Þ Ü ß à áâ ã ä ä å æ æ ç è é ê ëì í î ï ð ñ ò ï ó

ô õ ö õ ö ÷ ÷ ø ù ú û ú ü ý ý þ ÿ � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � ! � " # "

$

% & % '

( ) ( *

+ ,-. -/ 01

2 3 4 3 5 6 7 8 9: ; < = > ? @ A B C D E ;

F G H I J K L M N O L P QR S T T U V W X Y Z

[ \ ] \ ] ^ ^ _ ` a b a c d d e f g h i g j i i k l m n n m o p p q r s t u s v w w x y z { | z } ~ ~ y � � � � � � � � � � � � � � � � � �

�

� � � �

� � �

� ��

� � � � � �� ¡ ¢ £ ¤ ¡ ¢ ¥ ¦

§ ¨ © ª « ¬ ¨® ¯ ° ± ² ³ ´ µ ² ³ ¶ · ¸


Conclusion

• There is need for more understanding of the impact of government uncertainty on asset prices

– and the opposite as well: how asset prices affect government actions and can spur political

uncertainty

∗ For instance, governments may look at stock prices to learn about fundamentals them-

selves and take decisions.

• The macro-economic literature is also moving in that direction

– After Bloom (2009, Econometrica), large interest of uncertainty-based cycles

– New research looks at the impact of policy uncertainty (e.g. tax uncertainty) on business

cycles

∗ Monetary policy may stop working in uncertainty-based business cycles, if it does not

reduce uncertainty.

• Testing asset pricing implications is tricky, as we need to know when the time variation in beliefs

occur

Teaching Notes 4. Governments and Asset Prices Pietro...

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