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Democratic Politics and Financial Markets

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Democratic Politics and Financial Markets

William Bernhard

University of Illinois

David Leblang

University of Colorado

- Economic Globalization
- Capital Mobility
- Trade Flows
- De-industrialization

- International Economic Institutions
- Institutions: IMF, World Bank, WTO, EU
- Exchange Rates, Currency Unions, Dollarization
- International Financial Architecture

- Domestic Political Change
- Changes in Constituent Interests
- Institutional Reform: Democratization, Electoral, Economic Policy

- Traditional Approaches in IPE
- Developments since the 1980s
- Concern with Domestic Politics
- Enter Economics
- Political Business Cycle
- Partisanship Theory
- Rational Expectations
- Central Bank Independence
- New Institutionalism

- I.P.E.
- Economic Foundations of Political Preferences
- Political Sources of Economic Policy & Performance

- Connect International Capital Mobility and Institutional Reform
- Democratic politics affects asset markets
- Financial market behavior influences political processes

- Combination of incentives create demands for institutional reform
- Financial interests want less political uncertainty
- Politicians want to insulate themselves from asset market volatility

- Asset Markets
- Exchange Rates
- Spot
- Forward

- Bond
- Government -- different maturities
- Corporate -- usually constant maturity

- Futures
- Stocks

- Exchange Rates
- Distinguishing Features of Asset Markets
- Prices fluctuate more than other markets (e.g., labor).
- Assets have uncertain payments. Since they arise in the future, their present value is based on EXPECTATIONS of future payoffs.
- Consequently, news and information arrival influences trading.

- Assumptions of efficient market
- Rational Actors
- Symmetric Information (can be relaxed)
- Zero Transactions Costs

- Efficient Market Hypothesis
- Deep markets quickly incorporate all information relevant to the price.
- Implications
- Prices move in a random walk
- No unit root

- Empirical Support for EMH

- Empirical
- Soundly rejected with forward exchange rates; bond markets (expectations hypothesis)
- Use of high frequency data reveals non-stationarity, persistence, cointegration

- Theoretical: Behavioral Finance
- Asymmetric Information
- Herding Behavior
- Institutional Constraints

- Current work in IPE operates at a high level of institutional and temporal aggregation.
- Think more carefully about political information and how it is processed
- Political equilibrium and predictability
- Updating

- Empirical Strategy
- Look at specific events
- Temporal disaggregation

- Goal: Show Politics Affects Markets
- Political Events and the Forward Exchange Rate Bias
- Cabinet Formation and Market Behavior
- Coalition Formation and Stock Markets
- Coalition Bargaining and Government Bonds

- The 2000 Presidential Elections and Overnight Trading

- Goal: Show Markets Affect Politics
- Polls and Pounds
- Costs of Borrowing (Planned)

- Incentives and Institutional Reform (Planned)

- Cabinet Negotiations
- Bargain over Policies (e.g., Schofield)
- Bargain over Portfolios (e.g., Laver and Shepsle)

- Laver and Shepsle
- Cabinet Composition = f(Seats, Party Positions, Jurisdictional Structure)
- Strong Party: A party is strong if it participates in every cabinet preferred by a majority to the cabinet in which that party forms a minority government.
- At the very least, a strong party can veto certain alternative cabinets.
- Strong parties tend to be at the median position.
- Strong parties are more likely in party systems with fewer parties, fewer policy dimensions.
- There can be at most one strong party.

- Winset Program
- Requires Seats, Positions, Jurisdictions
- Party Positions: Manifestos Data Set
- Two Dimensions: Economic and Social
- Three Dimensions: Economic, Social, Foreign

- The program calculates
- Whether a Strong Party exists
- Simulations varying party position
- No Strong Party: # of 1,000 Runs

- 49 formations in 8 countries during the 1980s and 1990s

http://homepage.tinet.ie/~doylep/Winset/ws_index.htm

Two Dimensional Mapping

Economic Dimension

Pro-Market

Pro-State Intervention

Free Enterprise (401)

Market Regulation (403)

Incentives (402)

minus

Economic Planning (404)

Protectionism Negative (407)

Protectionism Positive (406)

Economic Orthodoxy (414)

Controlled Economy(412)

Welfare State Limitation (505)

Nationalization (413)

Welfare State Expansion (504)

Labor Groups: Positive (701)

Social Dimension

Numbers in Parentheses are Variable Numbers from Budge et. al 2001.

Authoritarian

Not Authoritarian

Military: Positive (104)

Anti-Imperialism (103)

Freedom and Human Rights (201)

Military: Negative (105)

Constitutionalism: Positive (203)

Peace (106)

Political Authority (305)

minus

Internationalism: Negative (109)

National Way of Life: Positive (601)

Democracy (202)

Traditional Morality: Positive (603)

Education Expansion (506)

Law and Order (605)

Social Harmony (606)

Three Dimensional Mapping

Economic Dimension

Pro-Market

Pro-State Intervention

Free Enterprise (401)

Market Regulation (403)

Incentives (402)

Economic Planning (404)

Protectionism Negative (407)

Protectionism Positive (406)

Productivity (410)

minus

Demand Management (409)

Economic Orthodoxy (414)

Controlled Economy(412)

Welfare State Limitation (505)

Nationalization (413)

Middle Class/Professional (704)

Welfare State Expansion (504)

Labor Groups: Positive (701)

Cultural Dimension

Authoritarian

Not Authoritarian

National Way of Life: Positive (601)

Social Justice (503)

Traditional Morality: Positive (603)

Education Expansion (506)

Law and Order (605)

minus

Traditional Morality: Negative (604)

Social Harmony (606)

Multiculturalism: Positive (607)

Multiculturalism: Negative (608)

Minority Groups (705)

Non-Economic Groups (706)

Foreign Policy Dimension

Nationalist

Internationalist

Internationalism: Negative (109)

minus

Internationalism: Positive (107)

European Community: Negative (110)

European Community: Positive (108)

Event Period

Estimation Period

Campaign | Negotiation

Step 1: Estimate Market Model for Estimation Window

National Stock Returns= World Market, European Market

Step 2: Using Parameter Estimates from Step 1, Calculate Residuals for Event

Step 3: Average , test whether statistically different from zero.

Event Study Methodology

Estimation Period: 150 Days

Event Window

Campaign & Election

Negotiation

Variable

Campaign& Negotiation

Negotiation Period

1

2

3

4

Constant

-5.70

(3.281)

2.73

(1.52)

-6.35

(3.05)

4.90*

(1.57)

Strong Party

7.313

(4.34)

9.08*

(3.57)

No Strong

-0.008*

(0.003)

-0.011*

(0.004)

AAR for Campaign

0.482

(0.517)

0.527

(0.634)

N=49

*p<.05

Robust standard errors in parentheses

F

3.95

5.48

4.01

5.18

Prob F

0.0526

0.0474

0.0602

0.0361

- Political predictability helps market actors adjust portfolios.
- Extensions
- Other political processes with predictable equilibrium.
- More sophisticated models of asset returns.

- October 3: Election
- Early December: SPÖ-ÖVP Start Talks
- January 19: SPÖ-ÖVP Agreement
- January 21: SPÖ-ÖVP Collapses
- January 24: ÖVP-FPÖ Negotiations
- February 4: ÖVP-FPÖ Sworn-In

- October 12: Election
- October 21: National Party meets NZ First
- October 23: Labour Party meets NZ First. Parties vow confidential negotiations.
- October 29: Labour Party meets NZ First.
- October 30: National Party meets NZ First.
- November 19: NZ First says “Labour still in race” despite press speculation.
- December 6: National Party caucus approves National-NZ First coalition.
- December 7: Labour Party MPs approve Labour-NZ First coalition.
- December 10: NZ First caucus chooses coalition with National Party.

- Content Analysis
- 750 Articles from der Standard (Austria)
- 141 Articles from The Post (NZ)
- Coalition Under Discussion
- Evaluation of Each Coalition: Likely, Neutral, Unlikely
- Daily Frequency

- Net Number of Likely Mentions
- Likely Mentions – Unlikely Mentions
- Coded as missing if coalition gets no mentions that day

where

=mean of prior probability that the net number of likely mentions>0

=prior variance

=data based probability that the net number of likely mentions>0

=data based variance

N =sample size at time t.

- Prior beliefs condition impact of news.
- For each coalition, calculate the updated probability that the net number of likely mentions will be positive:

- Expectations Theory (Campbell and Shiller)
- Change in Short-Term Rates = f(Spread Between Long-Term and Short-Term Rates)

- Model
- Vector Autoregression (VAR)
- Short-Term: Overnight Rate
- Spread: 10 year Rate – Overnight Rate
- Two Lags
- Political Variables as Exogenous

- Sample
- Austria: October 11, 1999-February 4, 2000
- New Zealand: October 19, 1996-December 19, 1996

∆rt

St

∆rt

St

Net Mentions

OVP-SPO

-0.0003

(0.0007)

-0.0003

(0.0008)

Net Mentions

OVP-FPO

0.0002

(0.0002)

-0.0003

(0.0002)

Net Mentions

SPO-FPO

0.0010

(0.0020)

-0.0005

(0.002)

Posterior

OVP-SPO

-0.072*

(0.031)

0.063*

(0.033)

Posterior

OVP-FPO

0.079*

(0.041)

-0.077

(0.043)

Cell entries are parameter estimates from a bivariate VAR that includes 2 lags of each dependent variable (not reported). Standard errors in parentheses.

N=84 for both models.

St is the spread between the long (10 year) and short (overnight) interest rate on Austrian government bonds.

∆rt is the change in the short rate.

*p<0.05

Both models pass diagnostic tests for residual serial correlation, normality, heteroscedasticity and parameter constancy at the 0.05 level.

Posterior

SPO-FPO

0.312*

(0.098)

-0.356*

(0.103)

∆rt

St

∆rt

St

Net Mentions

National-NZF

-0.0006*

(0.0002)

0.0001

(0.0003)

Net Mentions

Labour-NZF

-0.0004

(0.0041)

-0.0063

(0.0049)

Posterior

National-NZF

-0.430*

(0.133)

0.521*

(0.152)

Posterior

Labour-NZF

0.115*

(0.060)

-0.140*

(0.068)

Cell entries are parameter estimates from a bivariate VAR that includes 2 lags of each dependent variable (not reported). Standard errors in parentheses.

N=84 for both models.

St is the spread between the long (10 year) and short (overnight) interest rate on New Zealand government bonds.

∆rt is the change in the short rate.

*p<0.05

Both models pass diagnostic tests for residual serial correlation, normality, heteroscedasticity and parameter constancy at the 0.05 level.

- Implications:
- Markets respond to publicly-available information.
- Prior beliefs condition impact of events.

- Extensions
- Include other episodes of bargaining: Austria 2002-03.
- Link bond and stock markets.
- Bootstrapping VARs

Returns from S&P

Returns from S&P

- OLS assumes errors are normally and independently distributed. Financial data often violate these assumptions.
- Theoretical Concerns in Volatility/Predictability
- Finance literature/risk premium, Black-Scholes pricing model
- Economics literature/target zones
- Political science literature/politics influence variability of asset prices
- (e.g., Leblang and Bernhard; Freeman, Hays and Stix)

- Key question: Are some events/periods/systems conducive to more/less volatility than others?

Basic ARCH (1) model: conditional variance of a shock at time t is a function of the squares of past shocks: where h is the variance and is a “shock,” “news,” or “error”.

Since the conditional variance needs to be nonnegative, the conditions have to be met: .

If 1 = 0, then the conditional variance is constant and is conditionally homoscedastic.

- Autoregressive: it depends on its past
- Conditional: variance depends on past information
- Heteroskedasticity: non-constant variance
- Invented by Engle (1982) to explain the volatility of inflation rates.

ARCH(p) models are difficult to estimate and

decay very slowly.

Therefore, Bollerslev (1986) developed the GARCH model.

GARCH (1,1):

The variance (ht) is a function of

an intercept (),

a shock from the prior period (), and

the variance from last period ().

Higher order GARCH models:

where = constant

2t-1 = GARCH

t-1 = ARCH

qt = Exog. variables

GARCH (1,1) Model

. arch dlsp, arch(1) garch(1) nolog

ARCH family regression

Sample: 4 to 223 Number of obs = 220

Wald chi2(.) = .

Log likelihood = -366.1473 Prob > chi2 = .

-----------------------------------------------------------------------------

| OPG

dlsp | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

dlsp |

_cons | .0232815 .0826522 0.28 0.778 -.1387138 .1852768

-------------+----------------------------------------------------------------

ARCH |

arch |

L1 | .1652834 .045527 3.63 0.000 .0760521 .2545146

garch |

L1 | .7815966 .0783583 9.97 0.000 .6280172 .935176

_cons | .1121176 .0913255 1.23 0.220 -.066877 .2911122

------------------------------------------------------------------------------

- Integrated GARCH (Engle and Bollerslev 1986).
- Phenomenon is similar to integrated series in regular (ARMA-type) time-series.
- Occurs when +=1. When this is the case it means that there is a unit root in the conditional variance; past shocks do not dissipate but persist for very long periods of time.
- Fractionally Integrated GARCH (Baillie, Bollerslev and Mikkelsen (1996).

- GARCH in Mean (Engle, Lilien and Robbins (1987).
- Idea is that there is a direct relationship between risk and return of an asset.
- In the mean equation, include some function of the conditional variance—usually the standard deviation.
- This allows the mean of a series to depend, at least in part, on the conditional variance of the series .

- Non-Linear GARCH Models (dozens in last 20 years).
- Linear GARCH models constrain prior shocks to have a symmetric affect on ht.
- BUT there are theoretical reasons to believe that prior shocks will have an asymmetric impact on volatility.
- Non-linear models allow for asymmetric shocks to volatility.

Conditional variance:

where is the standardized residual and is the asymmetric component.

- The Exponentional GARCH (EGARCH) model developed by Nelson (1991).

Brooks, Introductory Econometrics for Finance

Enders, Applied Econometric Time Series

Franses and van Dijk, Non-Linear Time Series Models in Empirical Finance

Patterson, An Introduction to Applied Time Series

- Transactions (“tick”) data from the GLOBEX (overnight) market
- 4:35pm EST – 8:59am EST.
- Transactions aggregated to the minute.

- Dependent Variable
- Closing price (each minute) for NASDAQ 100 and S&P 500 Futures (maturity: 11/15/2000)
- Calculated log differences and multiplied by 100

- Calculate the probability of an electoral college victory for Gore.
- Exploit the sampling uncertainty associated with polling data and calculate the probability, for each minute during the evening of November 7th, that Gore will win the electoral college.
- Take the latest state level poll (vote share and sample size) and test the hypothesis:
- Higher p-values indicate that the null cannot be rejected. Substantively it indicates the number of times, in repeated sampling, that Gore will win the popular vote in state i.

State

Sample Size

G%

B%

Gore/

(Bush+Gore)

Prob

Electoral Votes

Time Called by CNN (est)

Alaska

400

26

47

0.356

0.000

3

12:00am (B)

Alabama

625

38

55

0.409

0.000

9

8:00pm (B)

Arkansas

286

44

47

0.484

0.287

6

12:12am (B)

Arizona

423

39

49

0.443

0.010

8

11:51pm (B)

California

600

45

44

0.506

0.607

54

11:00pm (G)

Colorado

400

38

47

0.447

0.017

8

11:41pm (B)

Connecticut

447

48

32

0.600

1.000

8

8:00pm (G)

Delaware

625

42

46

0.477

0.127

3

8:00pm (G)

Florida

600

48

46

0.511

0.697

25

see below

Georgia

512

37

53

0.411

0.000

13

7:59pm (B)

Hawaii

261

50

31

0.617

1.000

4

11:00pm (G)

1. Randomly draw from a uniform [0,1] distribution --> create variable Q.

2. Compare Q to the p-value (P) for state i.

- If Q < P then Gore wins state i and gets all electoral votes.
- If Q > P then Bush wins state i and gets all electoral votes.
3. Calculate the number of electoral votes won by Gore. If he obtains 271 or more declare him the winner.

4. Repeat steps 1 – 5 1,000 times. Calculate the percentage of times that Gore wins the election.

The percentage obtained in step 4 is the prior probability—the probability before polls close on Nov 7th –that Gore will win the election.

5. As each state is called, update the values of P in step 2. Repeat steps 3 and 4.

Conditional Variance:

where

- FIEGARCH (Fractionally Integrated Exponential GARCH)
- (i) shocks to conditional variance have “long memory” (FI)
- (ii) shocks have asymmetric impact on variance (E)

Control Variables:

Trading volume: VOLUME

Expected duration between trades

Mean

GARCH

EGARCH

FIEGARCH

Intercept

-0.001*

(0.0003)

-0.0004

(0.0004)

-0.0004

(0.0004)

-0.0004

(0.0005)

-0.0004

(0.0005)

AR(1)

0.025

(0.26)

0.413*

(0.159)

0.279*

(0.150)

0.274*

(0.130)

0.268*

(0.133)

MA(1)

0.066

(0.248)

-0.239

(0.175)

-0151

(0.167)

-0.131

(0.167)

-0.132

(0.150)

Duration

-0.0027*

(0.0010)

-0.0005

(0.001)

-0.0005

(0.001)

-0.0006

(0.002)

-0.0005

(0.003)

Variance

Intercept

0.0015*

(0.00003)

-1.64*

(0.117)

-3.09*

(0.344)

-3.67*

(0.373)

-4.603*

(0.487)

ARCH

0.158*

(0.026)

0.30*

(0.03)

0.459*

(0.045)

0.468*

(0.046)

0.443*

(0.045)

GARCH

0.686*

(0.008)

0.801*

(0.014)

0.049

(0.048)

0.063

(0.048)

0.120

(0.048)

EGARCH

0.025

(0.068)

0.060

(0.037)

0.089*

(0.040)

0.081*

(0.038)

fraction (d)

0.121*

(0.018)

0.124*

(0.018)

0.071*

(0.018)

Duration

0.0001

(0.001)

0.120*

(0.015)

0.332*

(0.034)

0.350*

(0.034)

0.399*

(0.036)

Volume

0.001*

(0.0001)

0.018*

(0.002)

0.080*

(0.006)

0.080*

(0.007)

0.085*

(0.007)

P[Goret-5]

-0.0024*

(0.0004)

-0.406*

(0.075)

-1.11*

(0.218)

Entropyt-5

0.232*

(0.100)

Info Arrivalt-5

1.544*

(0.700)

Diagnostics

p-value

p-value

p-value

p-value

p-value

LB(12)

0.5176

0.5043

0.5269

0.4764

0.4682

LB2(12)

0.0725

0.1151

0.1345

0.1254

0.1201

Jarque-Bera

0.0000

0.0000

0.0000

0.0000

0.0000

AIC

-5257

-4735

-4784

-4769

-4784

BIC

-5208

-4681

-4725

-4710

-4725

- Pr[Gore Electoral Victory] is negative and statistically significant for S&P Futures.
- Also holds case if the variable is lagged by 5 minutes.
- Also the case for NASDAQ Futures; although magnitude is reduced.

- Robustness of Results:
- Similar results lagging PEV between 1 and 10 minutes.
- Inclusion of “critical state” (or BIG-10) dummies (MI, OH, WI, IL).
- Results improve if student-t or GED distribution used instead of the normal.

- Extensions:
- Other episodes of “ultra” high frequency data.
- Alternative econometric models for estimating volatility (TAR, Markov Switching)

- Currency MarketsElectoral Politics
- “The Pound slid to an 18-month low against the German mark yesterday…[on] evidence of a lengthening Labour lead in the opinion polls.” The Independent, December 31, 1991.
- “Sterling continued to fall…largely due to the ICM election poll…which showed Labour to be just 5 percentage points ahead of the Conservatives.” AFX News, April 23, 1997.

- Electoral PoliticsCurrency Markets
- “The economic recovery which followed the pound’s departure from the European Exchange Rate Mechanism…should have put the Tories back at 42 per cent in the opinion polls…But the ERM debacle cost them 16 per cent in popularity.” The Guardian, June 4, 1994.

- What is Exchange Rate Volatility?
- Political Uncertainty and Exchange Rate Volatility
- Public Popularity
- Level of Approval
- Change in Approval

- Exchange Rate Turmoil
- Exchange Rate Changes
- Level:
- Depreciation
- Appreciation

- Volatility

- Level:

Exchange Rate Model

- Use GARCH model to generate measures of
- unanticipated depreciations
- unanticipated appreciations
- exchange rate volatility (conditional variance)
Public Opinion (Vote Intention for Incumbent)

- Use OLS model to generate measures of
- positive shocks to incumbent’s popularity
- negative shocks to incumbent’s popularity
Connecting the Models

- Estimate the two models iteratively and recursively.
- At each iteration,
- include lagged measures of exchange rate behavior in public opinion model.
- include lagged measures of public opinion shocks in exchange rate model (conditional variance).

- Update generated measures one observation at a time.
Data

- Weekly frequency; June 1987-June 2001

Vertical Lines represent the September 1992 Currency Crisis (mid-September 1992-December 31, 1992).

- Calculate predicted exchange rate and 95% confidence interval.
- If actual exchange rate is above the confidence interval for three weeks in a row, use actual residual as a measure of unanticipated depreciation. Otherwise, depreciation variable coded zero.
- If actual exchange rate is below the confidence interval for three weeks in a row, use actual residual as a measure of unanticipated appreciation. Otherwise, appreciation variable coded zero.

Unanticipated Appreciations were non-zero 83 times in the sample.

Unanticipated Depreciations were non-zero 66 times in the sample.

Vertical Lines represent the September 1992 Currency Crisis.

- Data: Vote Intention for Incumbent Government (Differenced)
- Weekly Frequency

- Method: OLS
- Control Variables
- Elections, Honeymoons
- Unemployment, Inflation
- Other Political Events

- Generate Measures for Unanticipated Poll Movements

- Monthly Poll Results
- MORI http://mori.com/
- ICM http://www.icmresearch.co.uk
- Gallup UK
- Miscellaneous Polls around Elections

- Date of Field Work, Sample Size, Polling Organization
- Kalman Filter

Horizontal lines indicate 35% and 45% levels of government popularity.

Positive Shocks were non-zero 255 times in the sample;

Negative Shocks were non-zero 266 times in the sample.

Vertical Lines represent the September 1992 Currency Crisis.

Variable

Coefficient

Robust SE

P-Value

Constant

0.0118

0.0614

0.847

Unexpected Appreciationt-1

-15.620

13.391

0.244

Unexpected Depreciationt-1

29.8054*

10.477

0.005

Diagnostics

P-value

LB(Q)

0.1024

LB(Q2)

0.0634

RESET

0.1149

JARQUE-BERA

0.0000*

Variable

Coefficient

Robust SE

P-Value

CONDITIONAL MEAN

Constant

-0.0002

0.0004

0.700

CONDITIONAL VARIANCE

Constant

-10.722*

0.164

0.000

Positive Shockt-1

-1.409*

0.650

0.030

Negative Shock t-1

-0.211

0.131

0.109

Consequential t-1

1.358*

0.242

0.000

Weak t-1

-0.848*

0.258

0.001

Consequential t-1*Positive Shock t-1

-0.043

2.068

0.983

Consequential t-1*Negative Shock t-1

0.421

0.287

0.142

Weak t-1*Positive Shock t-1

1.755*

0.638

0.006

Weak t-1*Negative Shock t-1

-0.674*

0.200

0.001

Currency Crisis

2.044

0.424

0.000

GARCH TERMS

ARCH(1)

0.0153

0.022

0.496

GARCH(1)

0.770*

0.054

0.000

Joint Tests

Chi2

P-value

Consequential Termsb

34.16*

0.0000

Weak Termsc

69.02*

0.0000

Diagnostics

P-value

LB(Q)

0.7723

LB(Q2)

0.9385

JARQUE-BERA

0.0281*

Vertical Lines represent the September 1992 Currency Crisis

(mid-September 1992-December 31, 1992).

Shock to Public Opinion

Popularity Level

No Shock

Negative Shock

Positive Shock

Strong

-0.21

(-3.95, 3.54)

-1.39*

(-1.89, -0.88)

Consequential

1.35*

(0.88, 1.82)

0.21

(-0.31, 0.72)

-1.39*

(-2.00, -0.75)

Weak

-0.84*

(-1.08, -0.6)

-0.88*

(-1.24, -0.54)

3.13*

(2.71, 3.54)

Vertical line indicates week of election, April 8, 1992.

- “The Pound slid to an 18-month low against the German mark yesterday …[on] evidence of a lengthening Labour lead in the opinion polls.” The Independent, December 31, 1991.

Election date: May 1, 1997

- “Given the disarray of the Tory party, not to mention the huge Conservative deficit in the public opinion polls, one might have expected to see the pound wobbling this week. But…the pound is enjoying a strong run on the foreign exchanges.” The Guardian, January 12, 1994.
- “Sterling continued to fall…largely due to the ICM election poll…which showed Labour to be just 5 percentage points ahead of the Conservatives.” AFX News, April 23, 1997.

Vertical lines indicate Petrol Crisis, September 2000.

- Feedback between public opinion and exchange rates
- Public opinion shocks that increase the unpredictability of future electoral outcomes lead to higher exchange rate volatility.
- Unexpected depreciations and exchange rate volatility have a small, but significant negative effect on incumbent popularity.

- Extensions:
- Other ways to model feedback between political and economic series.
- Other instances where frequent polling information is available.

- Asset Market Volatility Spreads Across Markets
- Domestic—Stocks, Bonds, Real Estate
- Across Countries--Factor Model based on international CAPM

- Political Events Influence Borrowing Costs
- Flight to Quality--within and across markets especially during periods of heightened volatility
- Spreads between private and public borrowing

- Implications
- Insulation of markets from political uncertainty.
- Becomes more serious as populations age and move assets from stock to bond markets.

- Political uncertainty creates asset market volatility which, in turn, can hurt politicians’ ability to remain in office.
- Therefore, pursue institutional reforms that
- Increase political predictability
- Insulate politicians from from the consequences of asset market volatility.

- Examples:
- Single Currency in the E.U.
- Electoral Reform
- Central Bank Independence
- Financial Market Regulation

- How to Model Expectations
- Learning/Updating
- Incorporate Political Information

- Use Market Behavior to Extract Political Information
- Disaggregated Data