Partisan Politics and Stock Market Performance:
The Effect of Expected Government Partisanship on Stock
Returns in the 2002 German Federal Election
Working Paper
11 September 2006
Roland Füss
Institut für Allgemeine Wirtschaftsforschung
Abteilung für Empirische Wirtschaftsforschung
und Ökonometrie
Albert-Ludwigs-Universität Freiburg
Platz der Alten Synagoge/KG II
79085 Freiburg im Breisgau
Germany
Michael Bechtel
(corresponding author)
Department of Politics and Management
University of Konstanz
Box D86
78457 Konstanz
Germany
Email: michael.bechtel@uni-konstanz.de
We thank […] for helpful comments and suggestions. Also, we would like to thank Dieter Kaiser for kindly providing
us with the trading volume data. We bear responsibility for all remaining errors.
Abstract
Partisan theory and extant evidence from parties’ ideal policies suggest firms to perform better
under right- than under left-leaning governments. If investors anticipate these effects of different
parties holding office, changes in expected government partisanship should produce distinct
patterns of stock market performance, with prices reflecting the electoral prospects of the
competing parties in the pre-election time. This is the first study which investigates such
anticipated effects of expected government partisanship on stock market performance in
Germany. In accordance with rational partisan models of government we assume that increasing
public support for the left- (right) leaning party coalition should result in decreasing (increasing)
stock market performance. We analyze the effect of expected government partisanship on small
firms in the 2002 German federal election employing GARCH(1,1) and TARCH(1,1) volatility
models. The empirical evidence shows that overall stock performance of small German firms was
positively (negatively) linked with the probability of a right-leaning (left-leaning) coalition
winning the election. Moreover, we find increasing electoral prospects of a right-leaning coalition
to trigger volatility increases while electoral uncertainty has a volatility reducing effect. The
findings carry implications for parties’ future economic policies and show possibilities for further
research.
Keywords:
Government partisanship, stock market performance, elections,
GARCH modeling, political information, price formation
JEL-Classification: C12, G12, G38
1. Introduction
Political scientists and economists alike are increasingly interested in the interplay between
politics and stock markets (Schneider and Tröger 2006; Leblang and Mukherjee 2005; Jensen
and Schmith 2004). One reason for this increased attention can be seen in the opportunity to test
the explanatory power of established politico-economic models. If different parties strategically
manipulate the economy to optimally benefit their voter base their economic policies should
produce distinct reactions by stock markets. Another reason is rooted in research into the effects
of globalization on the policy options left to parties (Boix and Adserà 2001; Garrett 1998). If
economic integration induces parties’ policies to converge, national partisan effects on global
financial markets should disappear. A final source fostering scholarly interest is the considerable
public attention drawn by stock market developments. For example, the rapid decline of the
German stock market index following the victory of the Social Democratic Party (SDP) against
the Christian Democratic Union (CDU) in the 2002 Federal election was a headline-catching
event.1
We examine the systematic distributive effects of government partisanship on the German
stock market in the 2002 German federal election. We argue that the dominant theoretical model
linking government partisanship and stock market performance via inflation does not apply to
Germany, where partisan manipulation of monetary policy is hardly possible, because the
European central bank enjoys independence from the political process (Lijphart 1999; Hays et al.
2000). We base our argument on partisan theory (Hibbs 1977) and extant evidence from the
analysis of party manifestos (Budge et al. 2001) with regard to parties’ preferences for economic
policies. Analysis of party manifestos has shown right-leaning parties to provide economic
policies which are more beneficial to firms’ profits than left-wing parties. Based on this
assumption and the semi-strong form of the efficient market hypothesis (Fama 1970) we can
assess how investors value different parties holding office. Indeed, if markets process information
efficiently, effects of different parties holding office in the near future should be anticipated and
incorporated into current prices. Changes in expected government partisanship should then produce
distinct patterns of stock market performance, with prices reflecting the electoral prospects of the
competing parties (Leblang and Mukherjee 2005; Alesina et al. 1997; Alesina 1987): If public
support for the left- (right) leaning party coalition increases, stock market performance should
1
http://news.bbc.co.uk/1/hi/business/2275359.stm; 12/29/2005.
1
decrease (increase). Thus, we gain an impression of how the German stock market expects
parties’ policies to affect the performance of firms.
The effect of expected government partisanship in the 2002 German federal election is
examined by employing GARCH(1,1) and TARCH(1,1) volatility models. The empirical evidence
shows that overall stock performance of small German firms was positively linked with the
probability of a right-leaning coalition winning the election. Moreover, we find increasing electoral
prospects of a right-leaning coalition to have triggered volatility increases while electoral uncertainty
had a volatility reducing effect. In contrast, our analysis shows no significant effects of expected
government partisanship on medium and large sized enterprises. The findings carry important
implications for parties’ future economic policies and show possibilities for further research.
The remainder of this paper is organized as follows: The next section provides a compact
review of the literature on the effect of government partisanship on stock market performance.
Subsequently, section three develops the theoretical model and derives empirical implications
for stock market reactions to expected government partisanship. In order to account for the
characteristics of financial time series data, we employ a GARCH framework in section four to
test our hypotheses for the 2002 German federal election. The final section summarizes and
discusses the implications of our findings.
2. Government Partisanship and the Stock Market
There has been constant and extensive scholarly interest in the effects of partisanship on
stock markets in the United States for more than two decades. This body of literature has been
inspired strongly by the partisan (business) cycle model, which traces economic performance
back to the strategic behavior of parties. Drawing on the Downsian view of democracy (Downs
1957) Hibbs (1977, 1987) relates economic policies to parties’ ideologies. Different ideologies
imply different economic policies benefiting some parts of the electorate at the expense of
others. As Hibbs points out, “governments pursue macroeconomic policies broadly in
accordance with the objective economic interests and subjective preferences of their classdefined core political constituencies”, (1977: 1467). Thus, as parties are assumed to be
ideologically motivated and to stick to their electoral platforms when holding office, left-leaning
parties will not only try to reduce unemployment in the pre-election period, because their voter
base benefits more from low unemployment than from low inflation. Moreover, different parties
will permanently pursue policy goals in accordance with their ideological labels with inflation
2
being higher under left-wing than under right-wing governments (Alesina et al. 1997; Alesina
1987; Downs 1957).
Consequently, the argument made in the literature is as follows: Since left-leaning parties
are more willing to accept higher inflation, incumbency of left parties is associated with a
decline of the real rate of returns for investors, which causes stock prices to fall. Drawing on this
theoretical linkage between parties’ preferred economic policies and macroeconomic outcomes
(Drazen 2002; Hibbs 1987), studies have tried to uncover effects of government partisanship on
stock market performance (Leblang and Mukherjee 2005; Foerster and Schmitz 1997; Gärtner
and Wellershoff 1995; Huang 1985). However, the evidence for the United States seems
inconclusive at best. Using OLS regressions on data for 20 presidential elections after 1900,
Riley and Luksetich (1980) find some support for the hypothesis that the stock market performs
better under times of Republican incumbency. On the contrary, Huang (1985) shows that for the
period from 1832 to 1980, higher average returns have been achieved during Democratic
administrations than during Republican ones. Gärtner and Wellershoff (1995) report the stock
market to perform better during the second half of a presidency irrespective of which party is
holding office. Analyzing monthly data from 1927 to 1998, Santa-Clara and Valkanov (2003)
find partisan effects as stock market returns are higher during Democratic than during
Republican presidencies. The most recent and encompassing study (Leblang and Mukherjee
2005) shows that stock dividend yields are higher under Republican administrations and increase
if the market expects the Republican party to win the upcoming election.
Much less work has been devoted to the analysis of partisan effects on stock market
performance for countries other than the United States. Genmill (1992) scrutinizes the impact of
expected government partisanship on the FTSE 100 in the 1987 British general election. Herron
(2000) estimates that if the Labor Party had won the 1992 general election the British stock
market would have dropped 5%. Martínez and Santiso (2003) investigate the reaction of the
Wall Street to political events in the 2002 Brazilian presidential elections. In order to estimate
the impact of different presidential candidates on macroeconomic performance, Jensen and
Schmith (2005) use movements in the Brazilian stock market as proxies for future expectations
for the Brazilian economy. They succeed in falsifying the hypothesis that the Brazilian
presidential candidates had an effect on the mean of the stock market.
Some have extended their analyses to the effects of politics on stock market volatility. The
evidence for the United States and Great Britain suggests that volatility is lower (higher) in times
of Democratic (Republican) and Labour (Conservative) incumbency and also if traders expect
3
the left-wing (right wing) party to win the upcoming election (Leblang and Mukherjee 2005). In
contrast, Herron (2000) estimates that if the Labor Party had won the 1992 British election the
British stock market would have experienced a surge in volatility. Also in the 2002 Brazilian
presidential election Jensen and Schmith (2005) find higher volatility to be associated with the
popularity of leftist candidates.
For Germany, studies are even rarer and have hitherto ignored the possibility of partisan
politics influencing volatility. Pierdzioch and Döpke (2004) examine the connection between
current government partisanship and stock market returns. They apply the political business
cycle model which was originally developed with the U.S. system in mind and analyze quarterly
stock market data from 1960 to 2002. By including a dummy variable in their regression
equation indicating which party holds office, they can not find an effect of government
partisanship on the mean of the German stock market. Approaching the question in a similar
fashion but running their estimations on monthly data, Bohl and Gottschalk (2005) reject the
hypothesis of partisan effects on the stock market in Germany. In this paper we question the
findings for the German case on both theoretical and empirical grounds.
Theoretically, linking government partisanship and stock market performance directly via
inflation seems plausible only in majoritarian democracies where parties exert control over
monetary policy. The existence of a largely independent central bank in consensus democracies
like Germany makes the theoretical relationship between inflation and government partisanship
unconvincing (Lijphart 1999).2 Empirically, assessing the effect of current partisanship on stock
market performance does not reflect the prospective trading behavior of rational investors who
try to anticipate partisan effects on the economy. If markets are semi-strong form efficient (Fama
1970) expected adverse effects on firms’ profits should be incorporated in today’s prices. Thus,
to the extent past research failed to find stock market performance to be influenced by current
government partisanship, this would merely mean that these effects might have been anticipated
already. A second objection concerns the use of highly aggregated data which is a hindrance to
capturing reactions to changing electoral prospects of different parties competing for office. Put
differently, in times where new polling results are disseminated through the media at least on a
weekly basis, investors are unlikely to ignore changes in electoral prospects which occur more
often than once every three months. Using quarterly data will thus prevent research from
2
Empirical evidence supports the presumption of no significant impact of partisanship on inflation in consensus
democracies (Hays et al. 2000) and with the introduction of a single European currency national monetary policy is
not even more under national control, but rather in the hands of the European central bank with the primary
objective to maintain price stability.
4
detecting potentially interesting and important short term effects. Thirdly, OLS regression
techniques are ill-suited for analyzing leptokurtotic and volatility clustered data (Leblang and
Mukherjee 2004; Pagan and Schwert 1990; Beck 1983). Using generalized autoregressive
conditional heteroscedasticity (GARCH) models rather than ordinary (Nofsinger 2004;
Bittlingmayer 1998; Gärtner and Wellershoff 1995) or nonlinear least squares regressions
(Herron 2000) would certainly add confidence in the estimates. Furthermore, these models
would also offer the opportunity to simultaneously assess the effect of politics on stock price
volatility (see Beck 1983 for an early discussion; Leblang and Mukherjee 2004, 2005; Jensen
and Schmith 2005; Pagan and Schwert 1990).
We consequently deviate from past research on the interplay between partisanship and
stock market performance in Germany, as we pay attention to both the partisan differences in
economic policies (Hibbs 1977) and the prospective behavior of financial investors who try to
anticipate the effects of economic policies under different governments. We follow past research
in that different parties produce different patterns of economic policies on dividends which cause
distinct responses by the stock market. Extensive qualitative and quantitative analysis of party
manifestos evidences left parties to focus on demand side policies aiming at low-income sections
of society (Budge and Keman 1990). It is left parties which tend to care about the distribution of
wealth more strongly and are more likely to redistribute capital via higher taxation of firms and
high income individuals (Budge et al. 2001; Budge et al. 1994; Garrett 1998). Our model thus
links government partisanship with stock market performance via expected economic policies
(Drazen 2002; Persson and Tabellini 2000; Alesina et al. 1997; Alesina 1987).
5
3. Analytical Framework
Financial theory assumes the price of a stock to equal today’s value of all future dividend
flows (Shiller 1983; Howells and Keith 1998). If government partisanship is connected with
stock market performance expected not present government partisanship should matter to
investors, as these are interested in maximizing their income from future dividend payments.
Following Cohen’s electoral information hypothesis (Cohen 1993; Alesina et al. 1997)
individuals will constantly update their expectations about future economic policies conditional
on the information currently available (Fama 1970). Price changes then reflect the change in the
probability of a certain party winning the election.
Net Present Value and Expected Government Partisanship
According to the discounted cash flow (or net present value) model at time t the price of a
stock S t , depends on its expected value E [Vt ] , which equals the sum of all future dividends
discounted to the present. Thus, even if investors are primarily concerned with capital gains,
because the source of the capital gains equals expected future dividends the current market price
of an individual stock is based on the expected flow of dividends throughout the life of a
company. Given a continuous stream of cash flows, the expected value of the sum of discounted
future dividends equals
 +∞ −δk

E t [Vt ] = E t  ∫ e Dk dk 
t

(1)
where Dk denotes the dividend payment at time k , δ is a discount factor composed of a
riskless interest rate rF and a risk premium RP which is appropriate given the risk of the stock
under consideration.3 As t approaches infinity, E [Vt ] resembles the stock price S t . To see how
expected economic policy is connected with the discounted cash flow model, note that according
to standard economic theory the size of a dividend payment Dk of firm i equals i ’s profits π i
divided by the number of shares (Miller and Modigliani 1961; Williams 1938). This is to say that
In finance the capital asset pricing model (CAPM) is used to determine the appropriate discount factor δ for a
stock of firm i : δ i = rF + β i (rM − rF ) . In this equation rM is the rate of return on the market portfolio, β i is the
systematic or market risk of a security and β i (rM − rF ) is the risk premium RP.
3
6
on the micro level π i,k determines the amount of capital available to be distributed as dividends
Di ,k to shareholders.
The incumbent policymaker p j can either be right-leaning (in this case j = R ) or leftleaning ( j = L ).4 Consider a simple profit before tax function in the case of a left-leaning
coalition holding office ( p L ):
π i ,k = P · Y ( p L ) − ( L ⋅ W ( p L ) + R ⋅ K ( p L ))
with
(2)
∂π ∂Y
∂π ∂W
∂π ∂K
⋅ L >0,
⋅ L < 0 , and
⋅
< 0.
∂Y ∂p
∂W ∂p
∂K ∂p L
The first part of the difference equals a firm’s revenues, i.e. the product of P , which is a
vector of prices, and Y , which is a vector of output quantities.5 The second part captures the
costs associated with production, where W denotes the labor prices and L is the quantities of
labor employed in the production process. The last source of costs arises from the amount of
capital K needed for production multiplied by the costs of capital R . All three parameters Y ,
W , and K are subject to policy decisions by the government p L , which can choose between
two broad classes of macro economic policy strategies. Demand side policies aim at
manipulating the economy by increasing government spending and tax levels which is supposed
to stimulate the economy by increasing demand (see for example Persson and Tabellini 2000).
Thus, for a government p L the partial derivative of π i,k with respect to output Y will be
positive, because of higher demand. However, while output increases, labor costs W increase.
This is because left-leaning parties are not only associated with higher non-wage labor costs
needed for social security and redistribution, but also strengthen labor unions’ in wage
bargaining (Calmfors and Driffill 1988; OECD 2004). Because of higher labor costs prices will
increase, which causes the independent central bank with the main objective to maintain price
stability to intervene by setting higher key interest rates. This triggers costs of capital to rise and
∂π ∂K
⋅
is smaller than 0.
thus results in a negative effect on profits, i.e. the partial derivative
∂K ∂p L
Generally, these two adverse effects together should exceed the gains from increased demand
causing firms to perform worse under left-wing administrations. In contrast, supply side policies
4
For the German case we define a coalition between CDU, CSU (Christian Social Union) and the Liberals as rightleaning and a coalition between SDP and Greens as left-leaning.
5
We have to assume competition to be imperfect, since otherwise there were no profits as marginal costs would
equal marginal benefits.
7
focus on the incentive structure within the economy. Lowering taxes as well as welfare
expenditure combined with a policy of wage-restraint are assumed to create a climate attracting
investment and increasing incentives to take up work, thereby enhancing economic growth.
Thus, we hypothesize a positive effect of right-leaning governments on firms’ profits.
However, before profits can be distributed, they have to be taxed with rate τ . Profits after tax
π iτ,k can be defined as:
(
)(
π iτ, k = f π i.k p j , Z i ⋅ 1 - τ k p j
)
(3)
The first component is determined by factors conditional on a government’s economic policy
( π i.k p j ), because the party or coalition of parties holding office p j permanently sets macro
economic key parameters in a way beneficial to their voter base (Alesina 1987; Hibbs 1977).
Profits before tax are also influenced by factors Z specific to a firm, e.g. its product innovations,
technological progress, or the quality of management, all of which are assumed to be
independent from a government’s economic policy and k , respectively. Finally, profits after tax
of company i depend on the corporate tax rate τ k , which is directly set by the party or party
coalition controlling government with left parties preferring higher tax rates than right-wing
parties.
Having defined profits this way, we can easily see how the impact of parties’ different economic
policies is transmitted to the stock market. According to our empirically supported assumption
that right-wing parties enact policies more beneficial to firms’ profits, for a trader in the preelection time there are two possible states of the world after the upcoming election. With
probability PrtL ∈ [0,1] a left party or coalition wins the election while the right wing party or
coalition wins with PrtR = (1 − PrtL ) . In order to see how the expected value varies with expected
government partisanship we use equation 1:
 +∞ −δk

 +∞ −δk

R
R
E t [Vt ] = Pr  ∫ e Dk p dk  + (1 − Prt ) ∫ e Dk p L dk 
t

t

R
t
(5)
Rational expectation leads investors to value future dividends as the sum of two expected values.
The first part equals the net present value of future dividends under a right-leaning government
times the probability that the right wing party or coalition will win the upcoming election.6 The
second part is the net present value of all future dividends under a left-leaning government
6
The upper limit of the integral ( +∞ ) reflects that it is unkown how long the winning party will remain in office.
8
multiplied by the probability of a left-leaning party or coalition winning the election. After some
simple algebraic transformation we obtain:
[
]
+∞
+∞
Et [Vt ] =  ∫ e −δk Dk p L dk  + PrtR  ∫ e −δk Dk p R − Dk p L dk 
t

t

(6)
This equation has a very intuitive interpretation. While there is a minimum net present value of a
stock equal to the first integral, i.e. in a world in which a left-leaning party coalition governs,
this value rises with the probability of a right-wing coalition winning the election times the
surplus in profits achieved under a right wing government. On the contrary, the expected value is
reduced if a victory becomes less likely.7
Volatility and Price Behavior
In linking the expected value of shares – which is dependent on expected government
partisanship – with trading behavior in order to obtain predictions for the mean and the volatility
of stock prices we rely on the work of Glosten and Milgrom (1985) as it has been reformulated
and modified by Leblang and Mukherjee (2005) in game-theoretic terms. However, instead of
restating the full model and proofs we restrict ourselves to a non-formal description of the
relevant causal mechanisms.
Trade is assumed to take place in the form of a sequential game played by a risk-averse
trader with homogeneous expectations who takes prices as given and a risk neutral market maker
(specialist) who quotes binding stock prices in order to ensure the liquidity and viability of the
market.8 The designated dealer is able to transfer (buy) the demanded (offered) stock amount to
(from) the trader at each time interval, which causes prices to adjust to changes in supply or
demand, respectively. Prior to the election traders acquire information, which enables them to
form expectations about the probability of a certain party winning office. The trader then
chooses the optimal demand of stocks in accordance with his expectations about future economic
policies, which are dependent on his expectations about which party will win the upcoming
7
Setting up the equation using PrtL instead of PrtR shows that the stock price decreases as PrtL increases.
8
For the Frankfurt Stock Exchange market makers are usually assigned to support trading in inactive stocks and are
known as designated sponsors. Designated dealers operate only in the continuous trading of the Xetra system,
where they must be admitted as participant. For this they have to “insure that there is additional liquidity in its stock
by posting quotes in the system, either at the sponsor’s own initiative, at the request of a market participant (quote
request),
or
trading
auctions.”
http://deutscheboerse.com/dbag/dispatch/en/kir/gdb_navigation/trading_members/30_Market_ Making?; 31st March 2006.
9
election. The market maker adjusts prices accordingly, so that these reflect the change in the
trader’s expectation. Consider the equation
∆ t = E[Vt ] - Pt
(7)
where ∆ t denotes the spread which equals the difference between the expected value of
the stock E[Vt ] and the quoted price Pt . With an increase in E[Vt ] as caused by an increase in
the probability of a right-leaning coalition winning the upcoming election, ∆ t will rise. If the
“true” expected value of the stock is different from the currently quoted price, because beliefs
have been revised according to the electoral prospects of the political parties, the market maker
will adjust quotes and the market will converge to the new equilibrium. When the trader
rebalances his portfolio in response to a positive ∆ t , the number of shares traded increases. In
order to equilibrate demand and supply the market maker optimally adjusts prices and volatility.
To abate demand he not only sets higher prices, but also increases volatility, because this reduces
the demand of risk-averse traders (Karpoff 1986; Andersen 1996). In other words, whenever
demand for stocks increases because stocks gain attractiveness, higher trading volume is
associated with an increase in volatility. In case ∆ t is negative, because a left-leaning
government is expected after the election, the expected value of stocks decreases. As investing in
stocks looses attractiveness, demand falls. Again, the market maker responds by choosing price
and volatility optimally. To achieve balance between demand and supply he lowers the price,
and in order to create incentives for the risk-averse trader to buy or at least hold his stocks,
volatility is set at low levels.9 This implies different reactions of trading volume to different
types of new information (Lisenfeld 1998; Edington and Lee 1993). While good news, such as
increased electoral prospects of the right-wing party coalition, have a positive effect on trading
volume which in turn increases volatility, downward movements lower the number of shares
traded and consequently volatility decreases. We thus expect to find the following relationship:
Hypothesis 1: If the electoral probability of a right-wing (left-wing) coalition increases, trading
volume increases (decreases) which causes the mean and the volatility of the
stock market to rise (fall).
This hypothesis reflects that investors anticipate the effects of economic policies on future
dividend payments. On the aggregate level, the expectation of economic policies more beneficial
to firms’ profits should result in higher stock market prices and higher volatility. However, there
9
For a formal proof of the equilibrium propositions see Leblang and Mukherjee (2005).
10
is a second source which might trigger higher volatility. With the expected election outcome
becoming closer and closer, it becomes increasingly difficult to predict the expected value of a
stock. The market maker consequently tries to equilibrate demand and supply by quick changes
in price. This causes tock prices to strongly fluctuate in times where uncertainty about the
election outcome is high (Leblang and Mukherjee 2005; see also Fowler 2006). Consequently,
we expect that
Hypothesis 2: If electoral uncertainty increases (decreases), volatility increases (decreases).
Table 1 summarizes our empirical implications.10 Note that hypothesis 1 implies an
interaction effect between the expectation of a right wing government and trading volume. If a
right-leaning coalition is expected, stocks become more attractive, which results in higher
demand. Higher trading volume leads to a higher mean and volatility of stock prices. In case a
left-leaning coalition is expected to hold office after the upcoming election, trading volume falls
and this reduces prices and changes in prices.
Table 1: Hypothesized Effects of Expected Government Partisanship and Electoral
Uncertainty on the Mean and the Volatility of the Stock Market
Independent Variable
Effect on mean
Effect on volatility
1a
Right wing government expected * trading volume
+
+
1b
Left wing government expected * trading volume
-
-
2
Electoral uncertainty
+
4. Data and Methodology
To test our hypotheses we use a sample of daily stock prices and survey data for the 9 months
preceding the 2002 German federal election from Forsa, one of Germany’s leading polling
institutes.11 In order to assess the impact of expected government partisanship we choose the
10
The second hypothesis captures an idea central to financial analysts where volatility is viewed as a measure of
risk. For example, it enters the Black-Scholes-Formula used to determine the price of an asset. It seems already
intuitively plausible then, that for volatility to be a measure of risk it should be correlated with (political)
uncertainty.
11
A full replication archive is available online at… . The raw polling data has in part been made accessible online,
see: http://www.wahlrecht.de/umfragen/forsa/2002.htm; 18th March 2006. It is also available at the Central Archive
for Empirical Social Research (ZA), University of Cologne.
11
SDAX as our dependent variable (figure 1). The SDAX is designed to represent the overall
performance of so-called small caps, i.e. firms with comparatively low market capitalization.
0
-.01
-.02
-.04
-.03
SDAX Returns
.01
.02
Figure 1: SDAX Returns in the 2002 German Federal Election
15jan2002
15mar2002
15may2002
15jul2002
15sep2002
15feb2002
15apr2002
15jun2002
15aug2002
There are several reasons which justify our decision to select the SDAX instead of the
DAX or MDAX as our dependent variable. First, national economic performance hinges
crucially on the wellbeing of small enterprises: They account for 40 percent of net investment in
Germany, 70 percent of all jobs and 80 percent of all trainees (see Deutscher Bundestag 2002).
This alone might be reason enough to examine SDAX returns. Second, because the DAX
(MDAX) reflects the performance of global (semi-global) firms which generate most of their
turnover outside Germany, we do not see a reason why their stocks should notably respond to
national politics. DAX (MDAX) companies realize 75% (62%) of their turnover outside
Germany. Enterprises represented in the DAX, such as Daimler-Chrysler, Siemens or BASF,
enjoyed record profits for years, but hardly paid taxes in Germany. Also in production these
companies are anything else than fully dependent on national economic policies: More than 50%
(40%) of their employees are located in countries other than Germany, which leaves these firms
12
hardly sensible to changes in labor and non-wage labor costs within Germany. Finally, the
higher amount of resources available to mid and large sized companies makes it much easier for
them to use exit options, i.e. evade adverse changes in national economic policies (Kurzer 1993;
Garrett 1998; Hymer 1979). Thus, if there any partisan effects on the stock market we should
find them in the SDAX data.12
Turning to our key explanatory variable, the probability of party or coalition j winning the
upcoming election, we face different operationalization possibilities. Brander (1991) uses the
vote share a party received in the most recent survey to measure the probability of a party’s
victory. Similarly, Jensen and Schmith (2005) pool public opinion data from different pollsters
and fill in missing values with the last polling result. A second alternative is the market model
where odds on elections provided by bookmakers are used to reflect “the acquisition of new
information on the relative standing” (Herron 2000: 331) of parties (Genmill 1992; Roberts
1990). Third, it is possible to calculate electoral probabilities from polling results which account
for the time left to the upcoming election and the variance in polled vote shares (“electoral
option model” see Alesina et al. 1997: 114-116). We decided to apply the electoral option model,
which is becoming increasingly popular in the literature (Leblang and Mukherjee 2005; Hays et
al. 2000; Alesina et al. 1997). Electoral probabilities are preferable to other measures as they
reflect two important facts: First, if polling results are very volatile, a winning margin does not
contain as much information as it does if polling results do not change much in the pre-election
time. Second, if a party is leading in the polls, this is more likely to result in an electoral victory
if there are only a few days left until the election.13
Our proxy for the probability that a right-wing coalition formed by the CDU/CSU and the
Liberals (FDP), will receive the majority in the upcoming election at time t is:
 QtR + µm - 50 
Pr = Φ 

 σ m

R
t
(8)
where Φ is the cumulative standard normal distribution, QtR denotes the proportion of citizens
who intended to vote for the right wing coalition (CDU/CSU and Liberals) relative to those who
12
However, we re-estimated all models with DAX and MDAX as dependent variables. The models were jointly
insignificant. More on this below.
13
A third reason which eliminates the second operationalization possibility is that book market data was not
available. Also note that political stock market data provides a fourth, potential measure. However, such data was
not available for the 2002 pre-election period.
13
intended to vote for the left wing coalition (SPD and Greens) at time t , and m is the number of
days left until the election.14 µ is the sample mean of changes in this proportion, and σ is the
sample standard deviation in daily changes. This delivers a measure which tells us how likely it
is that a right wing coalition will hold office after the upcoming election with PrtR ∈ [0,1] . Thus,
PrtR is used as a proxy for investors’ expectation of a right-leaning government after the
election. The probability of a left-wing coalition winning office is calculated as PrtL = 1 − PrtR .
In order to assess the effect of electoral uncertainty we capitalize on the detailed polling
data available to create a measure of electoral risk called entropy, where we use the proportion of
voters who have not decided which party to vote for et (υ t ) . Because in the pre-election time the
number of waverers equals the potential votes left for the competing coalition to make up the
winning margin, it becomes increasingly difficult to catch up in the electoral race the more
voters have indicated a clear preference for a party. To evaluate whether this operationalization
is not simply a matter of taste and the results simply statistical artifacts, we re-estimated all
models with an alternative measure of electoral risk used in the literature which is based on the
electoral probabilities (Leblang and Mukherjee 2005):
et (Prt j ) = 1 − 4(Prt j − 0.5) 2
(9)
where et denotes entropy at time t and Prt j is the probability of coalition j = {R, L}
winning the election. As can easily be verified, et (Prt j ) is an inverse U-shaped function which
reaches its maximum 1 if Prt j = 0.5 . Since Prt j ∈ [0,1] the function reaches its minima for
Prt j = 0 and Prt j = 1 . This reflects that uncertainty is minimal if the probability of a victory is
either very high ( Prt j ≈ 1 ) or very low ( Prt j ≈ 0 ). The intuition behind this measure is that the
smaller the difference in the winning probabilities, the less certain are expectations of
government partisanship (Leblang and Mukherjee 2005).
14
We can sum up the polled vote shares for the CDU/CSU and the Liberals (FDP) to receive the vote share of a
right-wing coalition, because in 2002 it was never a question that these parties would form a coalition.
14
Table 2: Descriptive Statistics
Mean
Min
Max
St.dev.
Skewness
Kurtosis
JB test
SDAX Returns
-0.136
-3.460
2.073
0.662
-0.991
7.419
179.837***
Dow Jones Returns
-0.119
-4.752
6.154
1.516
0.466
4.910
34.632***
Trading Volume
339,284.1
82,595.0
1,528,029
215,307.9
2.315
11.419
707.725***
Treasury Bill Rate
3.098
2.600
3.500
.0292
-0.408
1.899
14.384***
Inflation Rate
1.378
0.980
2.080
0.359
0.737
2.016
24.085***
PrtR
59.517
9.666
86.596
12.295
-1.007
5.965
98.496***
PrtL
40.483
13.404
90.334
12.295
1.007
5.965
98.496***
et (υ t )
14.737
11.448
17.937
1.636
-0.065
1.916
9.145**
e t (PrtR )
68.122
10.921
99.828
22.508
-0.823
2.784
21.109***
N = 184. Mean, min, max, and standard deviation (St.dev.) in percentages except for trading volume. ***, **, and *
denote statistical significance at the .10, .05 and .01 level. JB = Jarque-Bera test for normal distribution.
Table 2 presents descriptive statistics for our main variables. First note that the SDAX returns,
our dependent variable, is not only strongly skewed to the left, but also leptokurtotic indicating
thick tails of the distribution. Unsurprisingly, the Jarque Bera test soundly rejects the null
hypothesis of normally distributed returns. While leptokurtosis (excessive kurtosis) is surely
present in the dependent variable, we still need to test for volatility clustering.15 The Lagrange
multiplier test rejects the null of no volatility clustering (ARCH effects) at the 1% significance
level, hence we must indeed assume squared residuals to be correlated across time. Finally,
autocorrelation diagnostic test were computed and the Ljung-Box Q2(5) test of squared returns
indicates the presence of volatility clustering.16
Suited to the characteristics of our dependent variable we employ a GARCH (Generalized
Autoregressive Conditional Heteroscedasticity) framework to test the empirical implications of
the model. This technique is especially suited for the analysis of data which exhibits
leptokurtosis and volatility clustering (Bollerslev 1986; Engle 2001, 1982) as is the case with
most financial time series. Recent tests have additionally shown that generalized ARCH
(GARCH) models outperform Markov-Switching techniques (Leblang and Mukherjee 2004).
The basic idea behind GARCH models is to view the variance to be conditional on its own
history, i.e. the series is modeled as a linear function of its lagged values (autoregressive). While
15
Kurtosis of our SDAX series equals 7.419. This clearly exceeds 3 which we would expect from a normal
distribution.
16
The Ljung-Box Q-statistic with five lags yields a value of 37.206 indicating autocorrelation in the squared returns
at the .01 significance level. Thus, there exists substantial dependence in the volatility of returns.
15
GARCH models are standard in empirical finance (Engle 2001; Bollerslev et al. 1988) and
although Beck (1983) pointed towards their great potential for research in political science more
than two decades ago, political scientists hitherto have largely missed to apply this technique.17
We thus find it appropriate to briefly review the GARCH model as well as its extension TARCH
which we also intend to use in our analysis.
Let Pt be the stock market price observed at time t, and rt = ln(Pt) - ln(Pt-1) the
continuously compounded return on the stock market index in the time interval t-1 to t. If ψ t −1 is
the information set given at time t-1, the conditional mean and the conditional variance of rt can
be defined as:
µ t = E [rt ψ t -1 ] and ht = E [(rt - µ t ) 2 ψ t -1 ]
(10)
Typically, Ψt-1 consists of all linear functions of the past returns. In a GARCH(1,1)
specification, the conditional mean is:
rt = µ t + ξ Lt + ht ε t
(11)
(
)
(
)
where µt is a constant, Lt is a vector of exogenous variables, E ε t ψ t -1 = 0 and Var ε t ψ t -1 = 1 .
The conditional variance for the standard GARCH(1,1) model with exogenous variables is:
ht = ω + α1 ( rt -1 - µ ) 2 + β1 ht -1 + λi X i ,t
(12)
The conditional variance consists of four terms: a constant (ω), the prior shocks ε t2-1
(ARCH term), the past variance ht -1 (GARCH term), and a set of exogenous volatility regressors
X i ,t . The ARCH term captures the tendency of volatility clustering, i.e. if large (small) price
changes are followed by other large (small) price changes albeit of unpredictable sign. A high α1
coefficient signalizes that large surprises induce relatively large revisions in future volatility.
The persistence in volatility is measured by β1 , the coefficient of the GARCH term. If β1 is
much larger than α 1 this suggests that the market has a memory that lasts longer than one period,
and that volatility is more sensitive to its own history (its own lagged values) than to new
innovations. The last term in equation (12) captures the impact of exogenous (e.g. economic,
17
Rare exceptions are: Leblang and Mukherjee 2005, 2004; Jensen and Schmith 2005; Pagan and Schwert 1990.
16
political) variables on volatility. The model can be estimated via maximum likelihood where the
distribution of the innovations ε t -1 is assumed to be Gaussian.18
There is strong evidence that changes in prices at time t − 1 and the conditional variance of
returns at t are negatively correlated (e.g. Black 1976). Hence, volatility reacts more strongly to
negative than to positive information. Unlike the linear GARCH(1,1) specification in equation
(12), which assumes a symmetric effect on positive and negative errors on volatility, asymmetric
ARCH models take explicitly into account the sign of the innovation. Indeed, asymmetric ARCH
models have been found to significantly outperform their symmetric counterparts (Nelson 1991;
Glosten et. al 1993). There are several ways to capture asymmetric volatility responses. We use
the threshold GARCH(1,1) (also called TARCH(1,1)) model proposed by Glosten et al. (1993)
to analyze the volatility dynamics specified by:
ht = ω + α1 ε t2-1 + β1 ht -1 + δt -1 γ1 ε t2-1 + λi X i ,t
(13)
where δt -1 is an indicator variable which equals 1 if the price innovation at time t-1 was
negative and takes on the value 0 if a positive shock occurred. The TARCH model thus assumes
that positive price innovations at time t have an effect on volatility in t + 1 equal to α 1 . In case
of a negative shock ( δ t -1 = 1 ) the conditional variance additionally increases by γ 1ε t2−1 and the
combined marginal effect on volatility is picked up by the sum of the coefficients α 1 + γ 1 . If a
leverage effect exists, the coefficient γ1 is positive, because negative price innovations more
strongly affect volatility than positive innovations of the same magnitude.
5. Empirical Results
Before we can interpret the results a possible objection has to be addressed. In the 2002
Federal election there were two events which could potentially bias our results. The first event is
the massive flooding in East Germany in August 2002 which caused devastation all along the
Elbe river. This opportunity was effectively used by the federal government, consisting of a
coalition between the Social democrats and the Greens, to enormously regain sympathy when it
mobilized the armed forces not only to help with sandbagging against widespread flooding, but
18
Bollerslev and Wooldridge (1992) show that in a GARCH model the maximum likelihood estimates of the
parameters are consistent even if the true distribution of the innovations is not Gaussian. However, the usual
standard errors of the estimators are not consistent if the assumption of Gaussian errors is violated. In this case
semi-robust standard errors can be used. These standard errors are robust to deviations in the normality of the
residuals.
17
also for clean-up and reconstruction work in the affected regions. The victims of the flooding
were even promised compensation payments.19 The second major event was Chancellor
Schröder’s (SDP) denouncement not to let German soldiers take part in the planned war on Iraq.
This decision met the preference of the vast majority of German voters, while the Christian
democrats (CDU/CSU) did not clearly rule out this possibility. However, these major events are
captured by according changes in polling results and thus, in the electoral probabilities. As we
are interested in the systematic effect of expected government partisanship on stock market
performance, these single events simply add to the variance of our main explanatory variables.
Potentially confounding international events relevant to the stock market are controlled for by
including a lagged Dow Jones Industrial variable in our estimations (Schneider and Tröger
2006).
We estimated three alternative specifications for each of the two electoral probabilities
accounting for heteroscedasticity by applying Bollerslev and Woooldridge (1992) semi robust
standard errors. Turning to the interpretation of the results for the mean equation of our GARCH
specification (table 3), we can see in models I, III, and IV that the interaction term of the logged
differences in trading volume and the electoral probability Pr R has a positive sign and is
significant.20 This is in line with the predictions of our model where demand for stocks is
supposed to rise in anticipation of a right-leaning government which leads to higher stock market
returns. With regard to stock market volatility, which is modeled in the variance equation of the
GARCH specification, the significant ARCH coefficient α̂ suggests that yesterday’s deviations
have a considerable impact on changes of the following day. According to the hypotheses, the
coefficient of the interaction term suggests that if the market expected a right-leaning coalition to
win the upcoming 2002 Federal Election, trading volume increased which resulted in higher
stock market volatility. The estimator stays significant across different specifications and
changes only slightly in substance. These estimators also remain robust against confounding
influences from the so-called Monday effect, a well known market anomaly.21
19
Up to 30.000 soldiers were employed at the Elbe region in August and September 2002 with an extra 10.000 on
the alert.
20
Note that the estimators for the unconditional effect of the electoral probabilities and trading volume should not
be interpreted as representing real world relationships, as they assume the other variable to be zero, which is never
the case in our sample and also seems not interesting viewed from the perspective of the theory (see e.g.
Braumoeller 2004).
21
The impact of the treasury bill rate on the volatility process of SDAX returns is only significant in GARCH
models V and VI. The positive sign indicates that a high interest rate raises the cost of borrowing to companies, and
hence increases the level of equity return volatility. This result nicely fits to the finding of Glosten et al. (1993) who
also show treasury bill rate and stock return volatility to be positively correlated.
18
The opposite picture emerges if we focus on how the electoral prospects of a left-leaning
government influenced stock market performance (II, IV, VI). The coefficient of the interaction
term consisting of the probability of a left-wing government and the logged differences in
trading volume has a negative marginal effect on SDAX returns. With the market expecting a
left-wing government, also volatility decreased. For the electoral uncertainty measure the
coefficient is only significant in models II and III where it has a negative sign, by that
contradicting the theory which predicts higher uncertainty to trigger higher volatility. However,
because the coefficient is not robust, we should refrain from drawing any inferences.
Further commenting on the results for our variance specifications we can see that all α̂
coefficients are significant, meaning that ARCH effects in the SDAX time series indeed exist.
The magnitude of the ARCH terms shows the effect of an unexpected return on the volatility of
the following day. High values suggest an instable expected volatility or a disproportionate
response from market participants to past price innovations ε t2−1 . The values range from 0.3 and
0.4. Normally, those values lie between 0.1 to 0.2. Shocks on the conditional variance are of
minor relevance in the parsimonious models (I and II) given the low βˆ coefficients. In contrast,
the persistence of the volatility, calculated as the sum of ARCH and GARCH terms, increases
from 0.769 in the baseline specification (I) up to 0.915 (model VI) once we fully specify by
adding inflation, interest rate, and a Monday dummy to the mean and the variance equation,
respectively. Note that for the electoral prospects of the left-wing coalition (II, IV, VI)
persistence is nearly ten percent higher in comparison to models I, III, and V which estimate the
sensitivity of SDAX returns to the probability of a right-leaning coalition winning the upcoming
election. As the sum of the GARCH terms is substantially smaller than one, all models show a
mean-reverting behavior.
The ARCH-LM test for each of the six specifications fails to reject the null of no clustering
in the residuals, which means that the volatility dynamics of our SDAX returns is successfully
modeled. As can be seen at the bottom row of table 3, also autocorrelation diagnostic tests were
computed. The Ljung-Box Q(5) statistic for each model indicates that serial correlation in the
mean still exists, whereas the standardized squared residuals in the GARCH models are
uncorrelated, suggesting that the GARCH models have adequately captured the persistence in
the variance of returns. Finally, the results for the Jarque-Bera statistic suggest that deviations
from normality in the standardized residuals, mainly caused by the excess kurtosis, could be
reduced substantially.
19
Table 3: GARCH Models for Logged Changes in SDAX Returns (N = 184)
Parameters
I
II
III
IV
V
VI
Mean equation
∆(PrR)
0.027**
(0.012)
∆(PrL)
∆Log(Trading Volume)
∆Log(Trading Volume) x PrR
-0.010*
(0.005)
0.016**
(0.008)
-0.027**
(0.012)
0.006**
(0.003)
0.138***
(0.026)
-0.016**
(0.008)
0.138***
(0.026)
-0.041
(0.032)
0.387**
(0.158)
0.382***
(0.107)
-0.909*
(0.554)
∆Log(Trading Volume) x PrL
∆Log(Dow Jonest-1)
0.028***
(0.007)
-0.007*
(0.005)
0.013*
(0.007)
0.032***
(0.009)
-0.027*
(0.015)
0.007**
(0.003)
0.151***
(0.023)
0.786
(0.542)
-0.018**
(0.008)
0.144***
(0.040)
0.824
(0.843)
-0.041
(0.032)
-0.037
(0.030)
-0.043
(0.030)
0.387**
(0.158)
0.383***
(0.107)
0.387***
(0.135)
0.408***
(0.107)
-0.593
(0.390)
0.308***
(0.119)
0.551***
(0.115)
∆(Inflation)
DMonday
Constant
-0.010*
(0.005)
0.017**
(0.007)
0.144***
(0.021)
0.401
(0.276)
0.151*
(0.083)
-0.060*
(0.034)
-0.028***
(0.009)
0.008***
(0.002)
-0.019**
(0.007)
0.150***
(0.022)
0.532*
(0.288)
0.178**
(0.081)
-0.062*
(0.033)
Variance equation
α̂
β̂
∆(PrR)
∆(PrL)
∆Log(TradingVolume)
∆Log(TradingVolume) x PrR
-0.926*
(0.550)
1.547**
(0.726)
-2.556
(1.447)
-1.553**
(0.728)
-2.557*
(1.446)
0.480**
(0.229)
1.683
1.892
-142.790
9.589***
0.964
13.404**
4.295
0.480**
(0.229)
1.683
1.892
-142.787
9.583***
0.964
13.403**
4.296
∆Log(Trading Volume) x PrL
et (υ t )
0.909*
(0.553)
0.623***
(0.192)
∆(Interest Rate)
Constant
AIC
SIC
LogL
J.B. test
ARCH LM(1) test
Q(5)
Q2(5)
-0.931
(0.609)
1.559**
(0.799)
-3.336**
(1.446)
0.298
(0.319)
0.582**
(0.237)
1.666
1.911
-139.306
6.101**
0.856
11.385**
2.511
0.464
(0.742)
0.613
(0.470)
-1.297
(1.907)
-3.013
(2.877)
0.322
(0.475)
0.520
(0.462)
1.725
1.970
-144.709
40.483***
0.003
10.283*
1.205
0.328***
(0.116)
0.550***
(0.099)
-0.510*
(0.305)
-0.724**
(0.352)
1.313***
(0.476)
-1.556
(0.991)
0.332*
(0.186)
0.288*
(0.162)
1.636
1.898
-135.524
5.027*
0.514
10.094*
3.182
0.323***
(0.110)
0.592***
(0.095)
0.436
(0.287)
0.542***
(0.125)
-1.137***
(0.362)
-1.364
(0.920)
0.345**
(0.144)
0.250*
(0.150)
1.618
1.880
-133.872
4.500
0.373
10.347*
3.020
Coefficients shown with Bollerslev and Wooldridge semi robust standard errors in parentheses. ***, **, and * denote
statistical significance at .10, .05 and .01 level.
20
In a second step we re-estimated all specifications using a TARCH model in order to test
the robustness of the estimators once we account for asymmetric effects of past deviations on
volatility. Table 4 shows the results. None of the estimators we are interested in substantially
deviates from those of the GARCH specification. Note however, that accounting for asymmetric
effects of past deviations turns out to have been the correct decision: γˆ , which captures the
effect of negative changes on volatility, is significantly positive, indicating the presence of the
leverage effect. Recall that the TARCH model assumes that positive price innovations at time t-1
have an effect on the volatility in t by α̂ times the squared residuals, while the impact of a
negative shock equals αˆ + γˆ times the squared residuals. In the 2002 German federal election
negative innovations indeed had a greater impact than positive ones. Moreover, past shocks are
almost exclusively driven by “bad” news in the 2002 pre-election time, because the α̂
coefficients are small and insignificant altogether. Indeed, a negative innovation (V and VI) at
time t − 1 increases volatility at time t ten times as much as a positive innovation of the same
magnitude. This result reflects the strong downward market especially in the second half of
2002.
The information criteria reported in the bottom part of table 4 confirm the impression of
the TARCH model fitting slightly better to the data. Moreover, the diagnostic tests show that our
standardized residuals do not suffer from ARCH effects and are also normally distributed in the
case of the fully specified models V and VI. In comparison to the GARCH models, the LjungBox statistics leads us to accept the null hypothesis of no serial correlation in the standardized
residuals in the mean and in the squared standardized residuals in the variance equation,
respectively. All these diagnostic tools emphasize the reliability of our results.
21
Table 4: TARCH models for Logged Changes in SDAX Returns (N = 184)
Parameters
I
II
III
IV
V
VI
Mean equation
∆(PrR)
0.028**
(0.012)
∆(PrL)
∆Log(Trading Volume)
∆Log(Trading Volume) x PrR
-0.010**
(0.005)
0.016**
(0.006)
-0.028**
(0.012)
0.006***
(0.002)
0.126***
(0.026)
-0.015**
(0.007)
0.131***
(0.025)
-0.075**
(0.035)
0.018
(0.078)
0.483**
(0.243)
0.463***
(0.099)
-1.441**
(0.580)
∆Log(Trading Volume) x PrL
∆Log(Dow Jonest-1)
0.029***
(0.010)
-0.010**
(0.005)
0.016**
(0.007)
0.026***
(0.009)
-0.029***
(0.010)
0.007***
(0.002)
0.137***
(0.022)
0.597
(0.447)
-0.016***
(0.007)
0.138***
(0.021)
0.593
(0.422)
-0.070**
(0.035)
-0.060*
(0.034)
-0.060*
(0.034)
0.021
(0.078)
0.457**
(0.236)
0.483***
(0.104)
0.099
(0.113)
0.382*
(0.234)
0.485***
(0.098)
-0.989**
(0.421))
0.082
(0.103)
0.392*
(0.227)
0.503***
(0.100)
∆(Inflation)
DMonday
Constant
-0.011*
(0.006)
0.019**
(0.008)
0.145***
(0.022)
0.478
(0.348)
0.208**
(0.085)
-0.100**
(0.037)
-0.026***
(0.009)
0.009***
(0.002)
-0.020**
(0.008)
0.145***
(0.022)
0.483
(0.347)
0.209**
(0.085)
-0.100***
(0.037)
Variance equation
α̂
γˆ
β̂
∆(PrR)
∆(PrL)
∆Log(TradingVolume)
∆Log(TradingVolume) x PrR
-0.900
(0.635)
1.479*
(0.852)
-0.324
(0.954)
-1.425*
(0.758)
-0.251
(0.920)
0.141
(0.149)
1.663
1.891
-140.036
6.243**
0.225
9.256*
3.867
0.124
(0.144)
1.651
1.879
-138.926
6.120**
0.141
8.793
3.717
∆Log(Trading Volume) x PrL
et (υ t )
1.325**
(0.534)
0.571***
(0.198)
∆(Interest Rate)
Constant
AIC
SIC
LogL
J.B. test
ARCH LM(1) test
Q(5)
Q2(5)
-0.635
(0.464)
1.134*
(0.621)
-1.095
(0.984)
0.338*
(0.197)
0.244
(0.160)
1.654
1.916
-137.178
4.266
0.114
8.380
1.980
1.052***
(0.404)
0.549**
(0.161)
-1.106*
(0.612)
-0.749
(0.909)
0.324*
(0.194)
0.189
(0.148)
1.648
1.910
-136.602
4.666*
0.126
8.134
2.062
0.036
(0.062)
0.373**
(0.179)
0.640***
(0.094)
-0.856***
(0.315)
-0.423
(0.313)
0.866**
(0.425)
-0.130
(0.540)
0.257*
(0.146)
0.066
(0.087)
1.634
1.913
-134.287
1.649
0.287
9.087
3.034
0.040
(0.064)
0.367**
(0.179)
0.641***
(0.094)
0.830***
(0.314)
0.442***
(0.116)
-0.856**
(0.415)
-0.181
(0.545)
0.258*
(0.145)
0.074
(0.088)
1.634
1.914
-134.356
1.599
0.274
9.097
2.945
Coefficients shown with Bollerslev and Wooldridge semi robust standard errors in parentheses. ***, **, and * denote
statistical significance at .10, .05 and .01 level.
22
As discussed above, the electoral uncertainty variable turned out not to have had a robust
effect on volatility in the 2002 Federal Election. To test whether this result is due to our
operationalization we re-estimated both, GARCH and TARCH model each with the six
specifications using the alternative measure of electoral risk based on the electoral probabilities
et (Prt j ) . The measure is designed to reflect that uncertainty is minimal if the probability of an
electoral victory is either very high ( Prt j ≈ 1 ) or very low ( Prt j ≈ 0 ). Table 5 reports the
coefficients for electoral risk only in order to conserve space.22 The estimators of the GARCH
model indicate no significant impact of electoral uncertainty on changes in stock market returns.
The TARCH estimates however strongly suggest that volatility decreases if electoral uncertainty
increases. Thus, increased closeness of the electoral race had a volatility reducing effect in the
2002 Federal Election. This finding is surprising but not new to the literature. For the 2000 U.S.
presidential election Leblang and Mukherjee (2004: 312-313) also find a volatility reducing
effect of electoral uncertainty using the same measure.
Table 5: Results for Alternative Uncertainty Measure ( et (PrtR ) )
GARCH
TARCH
I
II
III
IV
V
VI
-0.051
(0.053)
-0.144**
(0.058)
-0.053
(0.054)
-0.147**
(0.062)
-0.100*
(0.055)
-0.167*
(0.056)
-0.089*
(0.054)
-0.166***
(0.056)
-0.006
(0.042)
-0.123***
(0.048)
-0.006
(0.042)
-0.119**
(0.054)
Coefficients shown with Bollerslev and Wooldridge semi robust standard errors in parentheses. ***, **, and * denote
statistical significance at .10, .05 and .01 level.
Although the volatility-reducing effect of electoral uncertainty on stock returns is not new
to the literature, it still awaits an explanation. A first attempt has been made by Freeman et al.
(2000). As in the case of exchange rates, which are affected by political uncertainty in major
economies like Australia, United Kingdom and the United States, but not in Germany, our result
too could be attributed to the institutional characteristics of Germany’s political system. More
specifically, in consensus democracies, where proportional representation foster coalition
governments, significant policy changes become less likely with increasing closeness of the
electoral race. Thus, higher electoral uncertainty could be interpreted as a signal of relative
future economic policy stability, which would imply less risk resulting in lower volatility.
22
The full tables will be made available online.
23
Finally, we have to report on our argument made above, that we should not expect stocks
of large and medium-sized enterprises operating on a global scale, which can easily use exit
options, to show reactions to changes in electoral prospects of national political parties. We reestimated all models for the DAX and MDAX series. The results can be found in table A.1-A.6
in the appendix. Because they are very similar, we discuss them together. The estimators of the
interaction term were insignificant in almost all specifications and in the TARCH models (A.2
and A.4) the parameter restriction on the ARCH term ( α ) was violated. But more importantly,
even the null hypothesis of the variables having no joint effect on the indices for large and
medium sized enterprises could not be rejected. This strongly supports the presumption of
national politics having no systematic impact on internationalized firms (anymore). This finding
also carries considerable implications for the development of parties’ future economic policies as
well as the electoral prospects of left-wing parties in general which we develop more clearly in
the conclusion.
How do these findings compare with the results from previous studies on stock market
reactions to government partisanship in Germany? Pierdzioch and Döpke (2004) examine the
relation between government partisanship and stock market returns using a dummy variable
indicating which party currently holds office. Their results show no robust effect of current
government partisanship on stock market performance. Also Bohl and Gottschalk (2005) refute
the conjecture of government partisanship affecting the German stock market. Our results
demonstrate that the stock market is far from being immune to partisan politics, but rather seems
to incorporate these expected effects into current prices. This not only shows that parties matter,
but also lends support to the semi-strong form of the efficient market hypothesis.
5. Conclusion
Hitherto most studies examining the effect of government partisanship on stock market
performance failed to pay attention to the prospective trading behavior of rational investors. In
this paper we examined the systematic distributive effects of expected government partisanship
on the stock market in the 2002 German federal election. While past research linked government
partisanship and stock market performance via inflation, we have criticized such causal
mechanism not to apply to the German case, a consensus democracy where the (European)
central bank enjoys dependence from the political process (Lijphart 1999; Hays et al. 2000).
24
Instead, we base our argument on partisan theory (Hibbs 1977) and extant evidence from the
analysis of party manifestos (Budge et al. 2001) with regard to parties’ preferences for economic
policies.
We assume different parties to differently manipulate the economic key parameters
demand, labor costs, costs of capital and corporate tax, all central to firms’ profits. Investors will
anticipate the impact of changes in future economic policy on expected dividend payment. In
case the left-wing coalition wins the upcoming election, traders expect a minimum dividend,
while as the electoral prospects of the right-wing coalition increases, investing in a share
becomes more and more attractive. Thus, the expected value of a stock increases with the
electoral prospects of expected government partisanship being right-leaning and decreases if it
becomes more likely that the left-wing coalition is expected to win the upcoming election. In the
stylized world of our market microstructure these changes in the expected value of an asset cause
shifts in the mean and the volatility of stock prices. Higher (lower) net present values increase
(decrease) trading volume which results in a higher (lower) mean and volatility of stock prices.
Additionally, electoral uncertainty should be reflected by higher stock market volatility.
In order to account for the characteristics of financial time series data we employed an
autoregressive conditional heteroskedasticity (ARCH) framework to test our hypotheses for the
2002 German federal election. Our results from GARCH(1,1) and TARCH(1,1) volatility models
strongly support the main hypotheses. However, the empirical evidence shows that in the 2002
election only overall stock performance of small German firms was positively linked with the
probability of a right-leaning coalition winning the election, while medium sized and large firms
were not systematically affected by expected government partisanship. For small firms we find
increasing electoral prospects of a right-leaning coalition to cause volatility increases. This not only
shows that partisan politics still matter and that future political developments are incorporated into
today’s prices, which lends support to the semi-strong form of market efficiency (Fama 1970).
Moreover it explains why past research failed to find significant influences of current government
partisanship on stock market behavior in Germany (Pierdzoch and Döpke 2004; Bohl and
Gottschalk 2005). Surprisingly, higher electoral uncertainty caused volatility to decrease. This
clearly is at odds with the theory, although past research has reported similar results for the 2000
U.S. Presidential Election (Leblang and Mukherjee 2004).
This paper provides impetus and shows possibilities for further research in four directions.
First, it seems promising to examine whether the sensitivity of the German stock market varies
across time and across sections. It would be interesting to see whether and how stock market
25
reactions to expected government partisanship in the case of stocks of large and medium companies
decreased gradually. Also across sections the sensitivity to politics needs attention as it could well be
conditional on economic sectors and their varying success in lobbying different parties for protection
(McGillivray 2004). For example, Herron et al. (1999) identify 15 economic sectors where stock
prices vary significantly with changes in expectations about the winner of the 1992 U.S.
presidential election. Second, we need cross country evidence on the impact of expected
government partisanship on stock markets to assess the effect of domestic political institutions.
The third direction of further research efforts concerns the puzzling uncertainty-volatility nexus.
We presume that in consensus democracies, where proportional representation foster coalition
governments, significant policy changes become less likely with increasing closeness of the
electoral race. Thus, higher electoral uncertainty might be interpreted as a signal of relative
future economic policy stability, which would imply less risk resulting in lower volatility.
However, a more thorough theoretical analysis of the finding that political uncertainty has a
volatility reducing effect on stock market performance seems indeed warranted.
Finally, these findings carry important implications for the development of parties’ future
economic policies. In the literature on the effects of globalization on democracy and
governments’ spending behavior it has been repeatedly argued that increased economic openness
diminishes the scope of left-wing parties to implement their ideal policies, because investors as
well as firms can use exit options to evade adverse domestic economic policies (Kurzer 1993;
Garrett 1998). For Germany our evidence suggests that reality might be more than just one step
ahead. While stocks of small firms were still responsive to changes in expected government
partisanship in the 2002 Federal election, the performance of medium sized and large firms was
not. This lets us suspect that these companies have successfully diversified their risk and are no
longer vulnerable to changes in national government. The second part of this story is that for
parties which prefer to maintain a strong welfare state there might be ever less left from which to
extract resources needed for redistribution. This would indeed imply that although the SDP
might still emphasize their traditional ideological label of aiming at “social justice”, at best there
presumably is ever less room to actually carry out such policies in the future. Finally, our results
hint that left parties in Germany might also suffer from a fundamental electoral disadvantage.
Because the SDAX can be regarded as representing the performance of small enterprises in
Germany in general, which is crucial for national economic performance, left-wing governments
are likely to experience shorter incumbency periods to the extent citizens‘ voting decisions are
based on their assessment of past economic performance.
26
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30
Table A.1: GARCH models for logged changes in DAX 30 returns (N = 184)
Parameters
I
II
III
IV
V
VI
Mean equation
-0.009
(0.021)
∆Log(Trading Volume)
∆Log(Trading
PrR
Volume)
x
∆Log(Trading
PrL
Volume)
x
∆Log(Dow Jonest-1)
0.002
(0.013)
0.012
(0.033)
-0.008
(0.021)
0.004
(0.013)
0.011
(0.034)
-0.009
(0.033)
0.240**
(0.100)
0.233**
(0.100)
∆(Inflation)
-0.050
(0.035)
-0.046
(0.035)
-0.054
(0.035)
∆(PrL)
0.054
(0.035)
0.053
(0.035)
0.052
(0.035)
∆(PrR)
-0.008
(0.021)
0.003
(0.013)
0.011
(0.033)
-0.013
(0.033)
-0.013
(0.034)
0.234**
(0.100)
0.245**
(0.099)
0.234**
(0.101)
0.242**
(0.102)
0.962
(1.602)
1.158
(1.661)
1.103
(1.678)
1.191
(1.684)
-0.137
(0.311)
-0.130
(0.310)
DMonday
-0.153
(0.103)
-0.152
(0.102)
-0.156
(0.103)
-0.152
(0.103)
-0.125
(0.093)
-0.129
(0.095)
α̂
0.040
(0.027)
0.038
(0.026)
0.041
(0.028)
0.042
(0.028)
0.037
(0.027)
0.039
(0.028)
β̂
0.945***
(0.029)
0.949***
(0.028)
0.946***
(0.030)
0.944***
(0.029)
0.948***
(0.029)
0.945***
(0.030)
∆(PrR)
0.095***
(0.035)
Constant
Variance equation
-0.091***
(0.035)
∆(PrL)
∆Log(TradingVolume)
0.071
(0.046)
∆Log(TradingVolume) x PrR
-0.095
(0.076)
∆Log(Trading Volume) x
PrL
et (υ t )
0.095***
(0.034)
-0.019
(0.031)
-0.100***
(0.035)
0.061
(0.045)
-0.025
(0.032)
-0.075
(0.075)
0.084
(0.074)
-0.100***
(0.019)
0.096***
(0.035)
-0.093***
(0.018)
∆(Interest Rate)
-0.102***
(0.035)
0.071
(0.046)
-0.094
(0.076)
0.105
(0.078)
-0.095***
-0.022
(0.032)
0.094
(0.077)
(0.019)
-0.100***
(0.019)
-0.100
(0.020)
-0.105
(0.021)
0.005
(0.017)
0.005
(0.017)
0.009
(0.018)
0.008
(0.018)
1.574***
(0.321)
1.458***
(0.296)
1.508***
(0.310)
1.577***
(0.314)
1.586***
(0.324)
1.674***
(0.344)
AIC
4.192
4.187
4.214
4.224
4.219
4.227
SIC
4.402
4.397
4.458
4.468
4.481
4.489
LogL
-373.679
-373.213
-373.669
-374.559
-373.101
-373.841
JB test
4.536
4.531
4.477
4.585
4.414
4.512
ARCH LM(1) test
0.883
0.870
1.012
0.927
1.040
0.973
Q(5)
5.768
5.626
5.101
5.011
5.119
5.192
2
5.875
5.781
6.278
7.012
6.306
6.565
Constant
Q (5)
Coefficients shown with Bollerslev and Wooldridge semi robust standard errors in parentheses. ***, **, and * denote
statistical significance at .10, .05 and .01 level.
1
Table A.2: TARCH models for logged changes in DAX 30 returns (N = 184)
Parameters
I
II
III
IV
V
VI
Mean equation
∆(PrR)
∆(PrL)
∆Log(Trading Volume)
∆Log(Trading Volume) x PrR
-0.005
(0.021)
-0.008
(0.033)
-0.038
(0.040)
0.003
(0.013)
-0.038
(0.039)
0.000
(0.013)
0.001
(0.035)
0.402
(1.452)
0.255***
(0.092)
0.255
(0.092)
0.255***
(0.092)
0.320
(1.392)
0.241***
(0.091)
-0.229**
(0.119)
-0.229**
(0.119)
-0.243**
(0.117)
-0.246**
(0.119)
-0.046
(0.029)
0.195***
(0.054)
0.938***
(0.028)
0.078**
(0.037)
-0.046
(0.029)
0.195***
(0.054)
0.937***
(0.028)
-0.067**
(0.027)
0.216***
(0.054)
0.956***
(0.021)
0.075**
(0.036))
-0.064**
(0.027)
0.218***
(0.057)
0.951***
(0.023)
∆(Inflation)
∆Log(Dow Jonest-1)
0.002
(0.022)
-0.004
(0.034)
-0.008
(0.033)
∆Log(Trading Volume) x PrL
0.040
(0.038)
0.038
(0.038)
0.038
(0.040)
DMonday
Constant
-0.001
(0.021)
0.001
(0.034)
0.498
(1.512)
0.248***
(0.090)
-0.200
(0.297)
-0.194*
(0.112)
-0.040
(0.038)
-0.001
(0.013)
-0.002
(0.034)
0.421
(1.493)
0.241***
(0.089)
-0.213
(0.297)
-0.193*
(0.110)
Variance equation
α̂
γˆ
β̂
∆(PrR)
∆(PrL)
∆Log(TradingVolume)
∆Log(TradingVolume) x PrR
0.072
(0.046)
-0.096
(0.075)
1.507***
(0.350)
1.511***
(0.350)
4.190
4.191
4.203
-0.093***
(0.021)
AIC
SIC
LogL
JB test
ARCH LM(1) test
Q(5)
Q2(5)
-0.075**
(0.036)
-0.009
(0.031)
0.042
(0.042)
-0.043
(0.070)
0.064
(0.075)
-0.065***
(0.014)
-0.011
(0.015)
1.061***
(0.238)
-0.093***
(0.021)
∆(Interest Rate)
Constant
0.056
(0.045)
-0.066
(0.074)
0.098
(0.076)
∆Log(Trading Volume) x PrL
Entropy et (υ t )
-0.078**
(0.037)
-0.025
(0.031)
-0.066**
(0.027)
0.204***
(0.050)
0.961***
(0.020)
0.082**
(0.035)
-0.068**
(0.028)
0.203***
(0.049)
0.965***
(0.019)
-0.082**
(0.035)
0.001
(0.029)
-0.043
(0.069)
-0.068***
(0.016)
-0.069***
(0.014)
-0.066***
(0.013)
-0.011
(0.017)
1.106***
(0.260)
-0.008
(0.013)
1.110***
(0.232)
-0.008
(0.013)
1.060***
(0.216)
4.209
4.200
4.196
4.418
4.418
4.465
4.471
4.480
4.476
-372.518
-372.533
-371.659
-372.184
-370.421
-370.035
4.631*
4.630*
4.811*
4.892*
4.905*
4.820*
2.143
2.141
1.656
1.566
1.736
1.725
5.772
5.767
4.628
4.859
4.962
4.827
11.653
**
11.666
**
13.132
**
13.524
**
12.030
**
11.874**
Coefficients shown with Bollerslev and Wooldridge semi robust standard errors in parentheses. ***, **, and * denote
statistical significance at .10, .05 and .01 level.
2
Table A.3: Results for alternative uncertainty measure ( et (PrtR ) )
I
GARCH
TARCH
II
***
-0.003
(0.001)
-0.002
(0.001)
III
**
-0.003
(0.001)
-0.200*
(0.121)
IV
*
-0.051
(0.028)
0.094*
(0.056)
**
-0.003
(0.002)
-0.002
(0.001)
V
VI
-0.003
(0.002)
0.086*
(0.045)
-0.002
(0.002)
0.034
(0.052)
Only coefficients of uncertainty measure are shown with Bollerslev and Wooldridge semi robust standard errors in
parentheses to conserve space. Full tables will be made available online. ***, **, and * denote statistical significance
at .10, .05 and .01 level.
3
Table A.4: GARCH models for logged changes in MDAX returns (N = 184)
Parameters
I
II
III
IV
V
VI
Mean equation
0.025*
(0.007)
0.021
(0.013)
∆(PrR)
-0.026**
(0.013)
-0.021
(0.013)
∆(PrL)
-0.012
(0.013)
∆Log(Trading Volume)
∆Log(Trading
PrR
Volume)
x
∆Log(Trading
PrL
Volume)
x
∆Log(Dow Jonest-1)
0.004
(0.008)
0.016**
(0.021)
-0.010
(0.011)
0.002
(0.007)
0.012
(0.018)
-0.016
(0.021)
0.219***
(0.041)
0.027**
(0.013)
0.219***
(0.041)
∆(Inflation)
-0.018
(0.016)
-0.014
(0.012)
0.005
(0.009)
0.018
(0.019)
-0.012
(0.018)
-0.020
(0.023)
0.163***
(0.038)
0.162***
(0.038)
0.192***
(0.038)
0.214***
(0.043)
-0.105
(0.568)
-0.101
(0.568)
0.176
(0.530)
0.109
(0.646)
-0.112
(0.125)
-0.134
(0.146)
DMonday
-0.096*
(0.054)
-0.096*
(0.054)
-0.089*
(0.051)
-0.089*
(0.051)
-0.063
(0.057)
-0.048
(0.065)
α̂
0.083**
(0.042)
0.083**
(0.042)
0.140***
(0.050)
0.139***
(0.050)
0.110**
(0.043)
0.095*
(0.052)
β̂
0.857***
(0.045)
0.857***
(0.045)
0.820***
(0.056)
0.820***
(0.056)
0.854***
(0.043)
0.816***
(0.063)
∆(PrR)
0.010
(0.010)
Constant
Variance equation
∆Log(TradingVolume)
0.035**
(0.016)
∆Log(TradingVolume) x PrR
-0.057**
(0.026)
0.020*
(0.012)
-0.024**
(0.011)
-0.012
(0.007)
-0.032*
(0.019)
0.057**
(0.026)
∆Log(Trading Volume) x
PrL
Entropy et (υ t )
-0.022**
(0.011)
-0.024**
(0.011)
∆(Interest Rate)
-0.012
(0.011)
-0.010
(0.008)
-0.010
(0.010)
∆(PrL)
0.011
(0.007)
0.010
(0.008)
0.020*
(0.012)
-0.032*
(0.019)
0.074**
(0.035)
0.031
(0.019)
-0.017
-0.029**
(0.014)
(0.106)
-0.017*
(0.010)
-0.014
(0.009)
-0.036***
(0.012)
0.004
(0.005)
0.003
(0.005)
0.003
(0.004)
0.001
(0.006)
0.401**
(0.172)
0.401**
(0.172)
0.282*
(0.158)
0.284*
(0.158)
0.225*
(0.133)
0.601***
(0.194)
AIC
2.442
2.442
2.417
2.417
2.432
2.493
SIC
2.652
2.652
2.662
2.661
2.694
2.755
LogL
-212.672
-212.672
-208.354
-208.348
-208.744
-214.355
JB test
0.637
0.637
0.416
0.405
0.726
0.443
ARCH LM(1) test
0.350
0.349
0.309
0.304
0.012
0.083
Q(5)
3.902
3.902
3.088
3.075
3.874
2.400
2
6.690
6.690
4.824
4.862
6.641
3.842
Constant
Q (5)
Coefficients shown with Bollerslev and Wooldridge semi robust standard errors in parentheses. ***, **, and * denote
statistical significance at .10, .05 and .01 level.
4
Table A.5: TARCH models for logged changes in MDAX returns (N = 184)
Parameters
I
II
III
IV
V
VI
Mean equation
∆(PrR)
0.027***
(0.010)
∆(PrL)
∆Log(Trading Volume)
∆Log(Trading Volume) x PrR
-0.012
(0.012)
0.016
(0.020)
0.029***
(0.010)
-0.031***
(0.009
0.004
(0.006)
-0.014
(0.015)
∆Log(Trading Volume) x PrL
-0.023*
(0.012)
0.005
(0.011)
-0.018
(0.026)
-0.334
(0.550)
0.190***
(0.037)
0.152***
(0.031)
0.117***
(0.024)
-0.092
(0.496)
0.150***
(0.029)
-0.124***
(0.040)
-0.097**
(0.030)
-0.121**
(0.042)
-0.129***
(0.048)
-0.144***
(0.041)
0.331***
(0.075)
0.933***
(0.037)
0.002
(0.007)
-0.184***
(0.033)
0.423***
(0.060)
0.925***
(0.027)
-0.133***
(0.049)
0.327***
(0.077)
0.908***
(0.046)
0.003
(0.006)
-0.106**
(0.042)
0.289***
(0.076)
0.901***
(0.045)
∆(Inflation)
∆Log(Dow Jonest-1)
-0.007
(0.012)
0.008
(0.019)
0.011
(0.014)
DMonday
Constant
-0.014
(0.013)
0.018
(0.020)
-0.076
(0.603)
0.240***
(0.036)
-0.156
(0.134)
-0.113**
(0.050)
-0.011
(0.014)
0.004
(0.008)
-0.018
(0.020)
-0.076
(0.603)
0.240***
(0.035)
-0.156
(0.134)
-0.113**
(0.050)
Variance equation
α̂
γˆ
β̂
∆(PrR)
∆(PrL)
∆Log(TradingVolume)
∆Log(TradingVolume) x PrR
0.004
(0.008)
-0.004
(0.014)
0.040
(0.173)
-0.084
(0.081)
2.307
2.257
2.334
0.007
(0.005)
AIC
SIC
LogL
JB test
ARCH LM(1) test
Q(5)
Q2(5)
-0.001
(0.010)
-0.016
(0.014)
0.026***
(0.008)
-0.042***
(0.014)
-0.106***
(0.030)
0.250***
(0.058)
0.925***
(0.025)
-0.006
(0.006)
-0.016***
(0.006)
0.042***
(0.014)
0.041
(0.035)
-0.004
(0.008)
-0.001
(0.004)
0.097
(0.124)
-0.001
(0.011)
∆(Interest Rate)
Constant
0.015*
(0.008)
-0.024*
(0.013)
-0.003
(0.008)
∆Log(Trading Volume) x PrL
Entropy et (υ t )
-0.001
(0.006)
0.002
(0.004)
-0.106***
(0.030)
0.250***
(0.058)
0.925***
(0.025)
0.006
(0.006)
-0.008
(0.009)
-0.010
(0.008)
-0.010
(0.008)
0.001
(0.003)
0.148
(0.133)
-0.001
(0.004)
0.187
(0.130)
-0.001
(0.004)
0.187
(0.130)
2.386
2.409
2.409
2.534
2.485
2.596
2.648
2.689
2.688
-199.230
-194.675
-199.746
-204.502
-205.621
-205.621
1.005
1.429
1.207
0.462
2.953
2.953
0.156
0.476
0.052
0.002
1.785
1.785
2.599
2.530
2.264
2.835
3.135
3.135
5.974
2.775
5.622
2.555
13.613
**
13.613**
Coefficients shown with Bollerslev and Wooldridge semi robust standard errors in parentheses. ***, **, and * denote
statistical significance at .10, .05 and .01 level.
5
Table A.6: Results for alternative uncertainty measure ( et (PrtR ) )
GARCH
TARCH
I
II
III
IV
V
VI
0.000
(0.001)
0.001***
(0.000)
0.000
(0.001)
0.001
(0.000)
-0.000
(0.000)
0.000
(0.000)
-0.001
(0.000)
0.000
(0.000)
-0.000
(0.001)
0.001**
(0.000)
0.000
(0.001)
0.001*
(0.000)
Only coefficients of uncertainty measure are shown with Bollerslev and Wooldridge semi robust standard errors in
parentheses to conserve space. Full tables will be made available online. ***, **, and * denote statistical significance
at .10, .05 and .01 level.
6