exchange rate volatility: does politics matter?

BENTO J. LOBO University of Southwestern Louisiana

Lafayette, Louisiana

DAVID TUFTE University of New Orleans

New Orleans, Louisiana

Exchange Rate Volatility: Does Politics Matter?*

We examine the weekly volatility of the Japanese Yen, British Pound, German Mark and Ca- nadian Dollar relative to the U.S. Dollar through five recent U.S, presidential terms. Our EGARCH-M model adds several new findings to the literature. Our results suggest that: 1) the volatility of all four exchange rates is impacted by either the year in the electoral cycle and/or the politaeal party in office; 2) past innovations exert an asymmetric impact on the conditional volatility of exchange rates, and 3) close to a U.S. election, an unexpected dollar depreciation impacts the volatility of the Yen and Mark significantly more than does an unexpected dollar appreciation.

1. Introduct ion The pr imary insight of studies in modern political economy is that

there is scope for government macropolicies to influence the real side of the economy in a politically profitable manner. This result emerges in spite of rational individual behavior because such policy is unlikely to be predictable and/or anticipated policy changes can have significant short-rnn effects on output and employment (Persson and Tabellini 1994). We show in this study that exchange rate movements can be bet ter understood by incorporating the dynamics of the political environment. Intuition suggests two channels through which polities impacts exchange rates: 1) through politically-induced policy effects on key" macroeconomic variables, for instance, when an incumbent president enacts partisan and/or electoral policies to facilitate reelection, or when the central bank reacts to politically-motivated fiscal action,

*The authors thank Gerald Whituey and Gregory Koutmos for helpful comments on earlier drafts of this paper, and Andy Szakmary for the data. This draft has benefited greatly from the comments of two anonymous referees.

Journal of Macroeconomics, Spring 1998, Vol. 20, No. 2, pp. 351-365 Copyright © 1998 by Louisiana State University Press 0164-0704/98/$1.50

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Bento J. Lobo and David Tufie

by actively intervening in currency markets 1, and 2) via investors' anticipation of policy changes in periods of electoral uncertainty.

Many stylized facts about the volatility of exchange rates have emerged in the recent past (Baiflie and McMahon 1989). These include serial correlation in the conditional second moments, time-varying means and variances in daily exchange rate data, leptokurtosis in the unconditional distribution of exchange rate changes, and contiguous periods of volatility and stability in the short run (Baillie and Boflerslev 1989; Engle, Ito and Lin 1990).

The predictability of volatility in asset markets is well documented (Bollerslev, Chou and Kroner 1992). The principal empirical tool used to model volatility has been the ARCH and GARCH class of models. More recent approaches have used asymmetric volatility models in which positive and negative innovations are allowed to impact volatility differently.

This paper explores the effects of polities on currency markets. We propose that investors in foreign exchange markets are sensitive to changes in the balance of political power close to a major U.S. election (midterm or presidential), due to the policy uncertainty that these changes potentially embody. Our results, based on data from five presidential terms in the recent float, suggest that both the conditional mean and variance of leading exchange rates are impacted by political developments in the U.S. The longer sample also reveals that currency volatility is impacted asymmetrically by past market innovations.

2. Modern Political Economy The writings in modern political economy predict that politicians ma-

nipulate the economy to produce outcomes that serve their best interests. The literature has taken one of two modeling approaches: 1) the Political Business Cycle (PBC) model and 2) the Partisan (PT) model.

The PBC model (Nordhaus 1975) assumes that politicians face a homogeneous electorate with clear preferences about key economic outcomes such as unemployment and inflation. Voters elect politicians on the basis of their expected economic performance. Politicians, on the other hand, are constrained by a given economic structure, typically described by a dynamic Phillips-curve. This theory yields two principal empirical implications: 1) politicians will follow different policy rules prior to and during election years and 2) in countries with fixed election dates, targeted variables as well as

1As opposed to passive intervention in accordance with a "leaning against the wind" policy. See also Almeldnders and Eijffinger (1994) for a more detailed discussion of central bank objectives.

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Exchange Rate Volatility: Does Politics Matter

policy instruments will exhibit cyclical patterns. In addition to this four-year pattern, Tufte (1978) reports a two-year cycle for the U.S. coinciding with midterm elections.

The empirical evidence in support of the Nordhaus-type PBC models is inconclusive for the United States. Nordhaus (1975) provided evidence in favor of the PBC, especially during the first Nixon administration. However, MeCallum (1978), Golden and Poterba (1980), Beck (1987, 1988), Hibbs (1977), Chappell and Keech (1986), and Havrilesky (1988) rejected explicitly or implicitly the pure PBC hypothesis. On the other hand, some evidence in favor was found by Tufte (1978), Frey and Schneider (1978, 1982, 1983), Schneider and Frey (1988) and Haynes and Stone (1988). The evidence in favor of the PBC is found mostly on disposable income and transfers, rather than on inflation and unemployment.

The partisan theories (Hibbs 1977; MacRae 1977), in contrast, build on the assumption that politicians differ by politieal preferences or ideology. In a two-party policy, one is deemed to be inflation-averse and the other unemployment-averse. As in the PBC model, a dynamic Phillips-curve de- scribes the economic structure that constrains politicians. The two main empirical implications of this approach are: 1) different parties follow different optimal policy rules and 2) targeted variables and policy instruments fluctuate over time following alternation of parties in power.

Chappell and Keeeh (1986) found evidence of differences in party policies that were quite similar in magnitude to those reported by Hibbs (1977). Alesina and Sachs (1988) rejected the hypothesis that maeroeconomic outcomes had been the same under Democratic and Republican regimes in the post-WWII period.

More recently, both lines of thought have been modified to incorporate rational expectations. The rational political business cycle (RPBC) propo- nents (Cukierman and Meltzer 1986, Rogoff and Sibert 1988) suggest that it is the informational asymmetry between government and voters that makes motivated policy cycles possible. The rational partisan theorists (RPT), such as Alesina (1987) and Alesina and Sachs (1988), argue that with sluggish wage adjustments, changes in the inflation rate associated with changes in government create temporary post-election differences in output and unemployment.

Alesina and Roubini (1994) found strong support for both the RPT and the RPBC. Specifically, they found that (1) downturns and upsurges in GNP growth tended to follow actual changes in government; (2) inflation increased immediately after elections and, (3) in countries with clear left- right coalitions, there is strong evidence of a partisan cycle with temporary output and unemployment effects. They found no evidence in favor of the Nordhaus-type or the Hibbs-type models.

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Bento j, Lobo and David Tufte

The referendum (or satisficing) approach to political economy (Frey and Schneider 1978, 1982, 1983), suggests that political entrepreneurs in- terfere with the macroeconomy only when economic problems are so acute as to have lowered their chances of reelection or to have disappointed their constituencies significantly. Otherwise, politicians pursue noneconomic partisan goals. Davidson, Fratianni and von Hagen (1990) tested the referendum model for U.S. macroeconomic data from 1905 to 1984. They found that the predictive power of time series models that incorporated PBC and PT effects was superior to models that used homogeneous dynamics across political regimes and parties. Furthermore, they found that the behavior of policy instruments depended significantly on the party in power.

Empirical tests of the impact of politics on currency markets have been scarce. Bachman (1992) investigated the effects of political risk arising from elections in which the incumbent party changed, on the forward bias in currency markets. He concluded that politics provides useful information for foreign exchange traders and deserves to be incorporated in studies of exchange rate determination.

Regardless of the lack of empirical consensus regarding the policy effects of politics on real variables, what is widely accepted as true is that political actors do respond to a reelection constraint. It follows that infor- mationally efficient exchange rates would react to these dynamics.

3. Data and Preliminary Statistics We use weekly Wednesday-close data for the U.S. dollar relative to

the Japanese Yen, British Pound, German Mark and Canadian Dollar (henceforth, ]Y, BP, DM and CD), from January 30, 1973 to November 24, 1992. Exchange rates are measured in terms of the foreign currency price of U.S. dollars. The data is from Knight-Ridder Inc. Table 1 presents summary statistics for the first differenced logarithms of spot exchange rates.

The diagnostics are in concordance with previous research in the area that found currency data to be skewed and leptokurtic relative to the normal distribution. The rejection of normality can be partially attributed to inter- temporal dependencies in the moments of the series. All the series show signs of conditional heteroskedasticity (i.e., highly significant LB s statistics). Additionally, the ]Y and CD show signs of serial dependence in the conditional mean. The diagnostics indicate that a GARCH-class of model would be appropriate, along with an error distribution that allows for greater kurtosis than the normal.

4. An EGARCH-M Model of Exchange Rates The analysis to follow uses an exponential GARCH-in-mean

(EGARCH-M) model developed by Nelson (1991). The EGARCH model

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TABLE 1. Summary Statistics on Changes in Exchange Rates

jY BP DM CD

Mean - 0.08 0.05 - 0.06 0.02 Variance 2.05 2.34 2.39 0.21 Skewness - 0.78" 0.19" - 0.17" 0.77" Kurtosis 7.88* 7.07* 5.33* 7.31" Excess-K 4.88 4.07 2.33 4.31 LB(12) 25.12" 17.26 16.73 121.18" LB2(12) 27.59* 105.87" 60.08* 34.82*

NOTES: Exchange rates are first-differenced logarithms of spot rates multiplied by 100. 2 Excess-K is the excess kurtosis relative to the normal distribution. LB(12) and LB (12) are the

Ljung-Box Q-statistics under the nulls of no serial correlation and homoskedasticity, respec- tively. Twelve lags are used in each test, yielding statistics which are distributed ~z, with 11 and 10 degrees of freedom. The superscript * denotes significance at the 5% level.

is more general than the standard GARCH model in that it allows innovations of different signs to have a differential impact on volatility and allows bigger shocks to have a larger impact on volatility than does the standard GARCH model. Also, by modeling the logarithm of the conditional variance, it is not necessary to restrict parameter values to avoid negative variances.

The Benchmark Model We describe a benchmark model below where the logarithm of the

spot exchange rate (St) is expressed as martingale difference process. 2 The benchmark model allows us to contrast in certain key respects our more elaborate politico-economic specification with models used in previous research (Kontmos and Theodossiou 1994). The model can be described by the following equations:

(AStlIt_ 1) ~ f(gt, ~t, v)

gt = s0 + ;~ , - ~ 01et-,, (1) i=1

p q ,, ) ] , = exp 13o + ~ ~bj log (o~_j) + ~ Y~gG-, j = l ~=1

(2)

2Baillie and Bollerslev (1989) demonstrate that the presence of conditional heteroskedastaelty does not violate this martingale property of exchange rates but is inconsistent with the random walk model.

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Bento J. Lobo and D a v i d Tuf ie

where AS t denotes weekly percentage changes in U.S. exchange rates, measured as 100*(St - S t - l ) . The conditional probability density f ( ' ) is based on information available at time t - 1 ( I t - l ) , and is a function of the conditional mean (~tt) and variance ((~ts), and a scale parameter v. The parameter )~ allows for linkages between the first and second moments of the distribution of AS t. The residual, e t = AS t - gt, represents a market innovation or shock. The moving average component (0) captures serial dependence in the raw series of exchange rate changes. A statistically significant 0 suggests that past innovations can be used to forecast future movements of the series and constitutes a violation of the martingale hypothesis.

In the conditional variance Equation (2), qb captures the persistence in volatility. Stationarity of crs requires that Ejqb 1 < 1. The standardized innovation, zt, is defined as et/cr t such that a positive (negative) zt implies an unexpected U.S. dollar appreciation (depreciation) and g(zt) = ([Iztl - E(Iztl] + 5zt), is an asymmetric function ofz t . The term in brackets relates lagged standardized innovations to volatility in a symmetric fashion, while 5 relates standardized innovations to volatility in an asymmetric manner. Pro- vided 3~ is positive, if ( - 1 < 5 < 0), then a negative innovation (i.e., an unexpected dollar depreciation) increases volatility more than does a positive innovation (i.e., an unexpected dollar appreciation). A positive value for 5 implies that positive innovations increase volatility more than do negative innovations. If (5 < - 1), then positive surprises actually reduce volatility while negative surprises increase volatility.

The parametric specification for f ( ' ) used here is the power exponential or Generalized Error Distribution (GED), which includes the normal distribution as a special case. The GED density function can be written as

f(~tt, % v) = (v/2). [F(3/v)]°S[F(1/v)] -15(1/crt)'exp [-[F(3/v)/F(1/v)]~/Slet/(~tl~;

where F(.) is the gamma function and v is a scale parameter controlling the shape of the distribution. For v = 2, the density function is that of the normal distribution. The double exponential (v = 1) and the uniform distribution (v ~ m) are also nested within this specification. The theoretical kurtosis for the GED is given by

K* = F(5 /v )F(1 /v ) /F(3 /v ) s .

Note that for values of v < 2, K* > 3, i.e., the GED is more kurtotic than the normal distribution.

Estimates for the parameters of the conditional mean and variance (H) and for v are obtained by maximizing the log-likelihood function:

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T

max L(HIm, p, q) = ~ logf(g t, o t, u) . t = i

The log-likelihood function is highly nonlinear in the parameters and was optimized using the B H H H algorithm in RATS. The solutions we obtained were not sensitive to starting values, and between 50 and 150 itera- tions were sufficient to obtain convergence. Likelihood ratio tests suggested a single-lag specification for the moving average component of the conditional mean and an EGARCH(1, 1) specification for the conditional variance equations. Similar evidence can be found in Koutmos and Theodossiou (1994) and Theodossiou (1994).

The Politico-Economic Model The politico-economic approach taken in this paper predicts that poli-

tics impacts currency markets in two ways: first, via reelection-motivated policy actions of incumbent presidents/parties and second, through the policy uncertainty arising from uncertain electoral outcomes. We model the effects of the first channel, by modifying the conditional mean and variance equations to incorporate electoral year and partisan effects in accordance with the PBC and PT. Additionally, we allow for a structural change in the series following the concerted government intervention following the Plaza Accord in September 1985. To capture policy uncertainty attributable to electoral risk, we examine the mean and volatility of these exchange rates close to major U.S elections, i.e. presidential and midterm. 3

Consider a modified model of the conditional mean as follows:

4

gt = ~ e4YEARi + c~sDPT + aoELE + a7 D85 + X~t - 0 E - t - 1 ,

t = l (3)

where, YEAR1 through YEAR4 are zero-one dummy variables for the four years in the electoral cycle, and together represent the unconditional mean of the series. DPT, the partisan effects dummy, takes a value one during the sole democratic regime of Carter, zero otherwise. ELE takes a value one in the sixteen weeks prior to a presidential or midterm election, zero otherwise. D85 controls for a structural shift following the Plaza Accord by taking a value of one after September 1985, and zero everywhere else. The conditional variance can be written as

aWe club presidential and midterm elections together in order to isolate the total effect of U.S. elections on currency markets. Differentiating the effects of the two types of elections is an interesting question that could well be addressed in a separate study.

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c~2t = exp [3iYEAP h + ~sDPT + fJ6ELE + 137DS5 + qb log (c~t2_1)

+ T[lZt_l I - E(zt_l) 4- 82;t_ 1 4- 81(ELE)zt_ 1 + 8e(DS5)zt_ 1

+ 8a(ELE)(D85)zt- 1]), (4)

where YEAR,, DPT, ELE and D85 are as defined earlier. Asymmetric effects are captured by 8 (for the entire sample period); 81 (for the period close to elections in the overall sample); 82 (in the post-Plaza Accord period); 8 a (close to elections in the post-Plaza Accord period).

The testable implications of this model are as follows: (1) if exchange rates are impacted by the electoral cycle, then we should capture this dynamic in the YEAR coefficients, (2) if the party in power matters, then the coefficient on DPT should be statistically significant, (3) ifa structural change did take place following the Plaza Accord, then we should be able to capture this in the coefficients on D85, (4) if asymmetric effects exist, then 8 and/or 82 should be significantly different from zero. However, no sign prediction is reasonable a priori and, (5) if political (electoral) uncertainty impacts exchange rates then ELE, 81 and/or 8a should be significantly different from zero.

5. Empirical Results

Diagnostics' Table 2 presents the diagnostics for the politico-economic models. A

correct specification of the conditional mean and variance requires that the estimated standardized residual have zero mean, unit variance and be serially uncorrelated and homoskedastie. The diagnostics suggest that the models for all four exchange rates are well specified. Furthermore, the politico- economic specification accounts for more of the excess kurtosis in the data relative to the benchmark specification. 4 The likelihood ratio statistic strongly rejects the hypothesis that political effects are jointly insignificant, suggesting that the politico-economic specification is an improvement over the benchmark specification. The chi-square test statistics with 15 degrees

4Results for the benchmark models are available from the authors. There m evidence of serial dependence in the conditional mean of the JY and BP under the benchmark specification. Adchtionally, the benchmark DM model fails to account for the time-varying heteroskedasticity in the data.

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TABLE 2. 1973-1992


Diagnostics for the EGARCH-M Politico-Economic Models:

JY BP DM CD

Mean - 0.00 - 0.01 - 0.01 0.01 Variance 1.05 1.01 1.02 1.00 Skewness - 0.84* 0.20* - 0.11" 0.56* K* 3.11 3.05 3.01 3.06 Excess-K 4.79 2.07 1.13 2.97 Ex-BK 6.77 4.56 1.49 3.59 LB(12) 18.78 18.34 14.27 9.69 LB2(12) 6.21 3.84 16.58 7.72 v 1.90" 1.95" 1.99" 1.95" )~2 6.73* 0.18 0.01 0.37 LLF - 1588.81 - 1642.93 - 1686.51 - 389.92

NOTES: K* is the theoretical kurtosis from the GED; Exeess-K is the excess kurtosis, measured as sample kurtosis minus K*; Ex-BK is the excess kurtosis under the benchmark specificataon, v is the scale parameter for the condttional density function. ~2 is the test statistic for H0: v = 2. LLF is the sample log-likelihood evaluated at the maximum. See also notes to Table 1

of freedom, were: 67 (JY), 67 (BP), 30 (DM) and 42 (CD). Finally, the estimated values of the scale parameter (v) are not significantly different from 2 (the normal density), except for the JYP

Univariate models were estimated for each currency. To facilitate dis- eussion, we present the estimates for the conditional mean and variance separately. Results for the benchmark model are not reported but are available on request.

The Conditional Mean Table 3 contains estimates for the conditional mean for all four cur-

rencies. Only the BP is impacted by the electoral cycle, whereas partisan effects appear to impact both the JY and BP. No such effects are evident for the D M or CD. Furthermore, none of the means is particularly affected by proximity to U.S. elections. There is evidence, however, of a structural shift in the conditional means of the JY and BP following the Plaza Accord.

While there does not appear to be any cross-moment interaction for any of the currencies, the 0 term for the DM and CD is highly significant

5However, the likelihood-ratio statistic for the test of the Normal density specification against the GED specification was consistently highly significant suggesting that the GED density does offer some improvement in precision

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TABLE 3. Conditional Mean: 1973-1992

4

~t t = ~ chYEABi. + c¢sDPT + c¢6ELE + otvD85 + )~cr t - 0 E t _ 1

Coeff JY BP DM CD

O~ 1 -0.04 (0.11) 0.33 (0.09)* -0.05 (0.12) -0.05 (0.07) c~2 -0.07 (0.12) 0.15 (0.08) -0.12 (0.12) -0.00 (0.07) ~3 0.12 (0.11) 0.23 (0.09)* 0.03 (0.12) -0.06 (0.08) ct4 0.03 (0.11) 0.47 (0.09)* 0.08 (0.10) -0.06 (0.07) ~5 -0.28 (0.13)* -0.41 (0.09)* -0.06 (0.14) 0.08 (0.04) oL 6 0.01 (0.10) -0.10 (0.12) -0.18 (0.12) 0.00 (0.06) ot v -0.22 (0.13) -0.33 (0.11)* -0.18 (0.11) -0.01 (0.04)

0.15 (0.24) -0.10 (0.14) 0.24 (0.17) -0.05 (0.07) 0 0.06 (0.03) 0.02 (0.03) 0.07 (0.03)* 0.33 (0.04)*

NOTES: See text for a discussion of the variables. Standard errors in parentheses. The * denotes significance at 5%.

indicating that forecasts of the conditional means of these exchange rates can be significantly improved by using past innovations.

The Conditional Variance The estimates for the conditional variance are in Table 4. The results

are distinctly different from those for the conditional mean. The volatilities of all four exchange rates were impacted by the electoral cycle, though no discernible pattern is forthcoming relative to the predictions of the PBC or RPBC models. Also, three of the four exchange rates were impacted by the party-in-power. While the volatility of the BP increased during the Repub- lican regimes, that of the JY and DM was higher during Carter's sole democratic regime. Furthermore, the volatilities of three of the four exchange rates significantly picked up close to a U.S. election and in the post-Plaza period.

Interestingly, the implied half-life (persistence) of shocks is much longer for the JY and DM than those reported in studies using smaller sam- ples (Theodossiou 1994). 6 Our estimates range from 69 to 77 weeks. This effect can be attributed to our longer sample. Half-life estimates from the JY and DM benchmark models for the period 1983-1992 (resembling the estimation period from previous studies), range from 3 to 5 weeks. However, estimates for the full sample from 1973 to 1992, range from 39 to 84 weeks.

6The half-life of a shock is calculated as: log(0.5)/log(qb).

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TABLE 4. Conditional Variance: 1973-1992

2 = exp 13iYEA~ + 135DPT + 13c, ELE + [~rD85 + qb log((rt2_l) Gt

+ 7[Izt_ll - E(zt_x) q- 5yot_ 1 -~- 51(ELE)zt_l + 52(D85)Yot_ 1

+ 53(ELE)(D85)zt- 1]) /

Coeff. JY BP DM CD

1~1 0.03 (0.00)* 0.10 (0.02)* 0.00 (0.00) -0 .36 (0.07)* 132 - 0.02 (0.01)* - 0.00 (0.03) - 0.03 (0.01)* - 0.29 (0.07)* 13a -0.01 (0.00)* 0.06 (0.02)* -0.01 (0.00) -0 .42 (0.09)* ~4 --0.02 (0.01)* 0.07 (0.03)* --0.02 (0.01)* --0.24 (0.07)* ~5 0.01 (0.00)* -0.06 (0.03)* 0.02 (0.00)* 0.05 (0.03) 136 0.03 (0.02)* 0.09 (0.03)* 0.05 (0.02)* -0.06 (0.03) 13r 0.01 (0.00)* 0.05 (0.02)* 0.00 (0.00) 0.10 (0.03)* ¢b 0.99 (0.00)* 0.90 (0.02)* 0.99 (0.00)* 0.82 (0.03)* 7 0.05 (0.01)* 0.28 (0.03)* 0.07 (0.01)* 0.26 (0.04)* 5 0.18 (0.02)* -0 .13 (0.09) -0 .12 (0.02)* 0.69 (0.09)* 51 - -1 .31 (0.63)* - -0 .19 (0.31) - -0 .32 (0.41) - -0 .62 (0.42)

52 -0.73 (0.25)* 0.25 (0.20) 0.35 (0.28) -0.51 (0.24)* 53 2.20 (1.17) -0 .61 (0.47) -3 .11 (1.14)* 0.47 (0.55) Z 2 (Ho:qb = 1) 15.23" 36.33* 20.71" 24.77* HL (weeks) 76.61 6.72 68.97 3.65

NOTES: See text for discussion of the variables. Standard errors in parentheses. * denotes significance at the 5% level.

Lothian and Taylor (1996), using annual data from a sample spanning two centuries, also find evidence of a high degree of persistence in currency markets.

Asymmetric Effects There is strong evidence that exchange rate volatility is impacted asym-

metrically by past innovations (this effect shows up even in the benchmark models for the JY and CD). For the full sample period, the volatilities of the JY and CD increased more when the dollar suddenly appreciated, rather than depreciated. The converse was true for the DM. However, following the Plaza Accord, a sudden U.S. dollar depreciation increased the volatilities of the JY and CD more than an unexpected U.S. dollar appreciation. No asymmetric effects were noticeable for the BP.

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TABLE 5. Summary of Politico-Economic Effects on Exchange Rates

Conditional Mean

JY BP DM CD

Electoral Year Effects NO YES NO NO Partisan Effects YES YES NO NO Political Uncertainty NO NO NO NO

Conditional Variance

JY BP DM CD

Electoral Year Effects YES YES YES YES Partisan Effects YES YES YES NO Political Uncertainty YES YES YES NO Asymmetric Effects YES NO YES YES

Furthermore, the asymmetric effect for the JY and DM was particularly pronounced in periods close to U.S. elections. For the JY, this effect is evident throughout the sample period (significant 51), while for the DM this is evident in the post-Plaza period (significant ~3). For both exchange rates, an unexpected U.S. dollar depreciation close to an election increased volatility while an unexpected U.S. dollar appreciation actually reduced volatility.

6. Conclusions The study of currency volatility has important economic implications

for financial engineering and risk premium theories. Our research, using data from the recent float, has added several new findings to the exchange rate literature (see Table 5 for a summary). First, we have shown that both the conditional mean and variance, the latter especially, of exchange rates are impacted by politico-economic dynamics. Second, we have demonstrated that exchange rate volatility is asymmetrically impacted by past innovations. In fact, for the JY and DM, this effect is particularly strong close to a U.S. midterm or presidential election. Perhaps the failure to find asymmetric effects previously is an artifact of the sample studied.

Our findings support the intuition that politics has a non-trivial impact on currency volatility. There is evidence that politics affects currency markets through two channels, i.e., via reelection-motivated policy and through investor expectations of policy uncertainty close to major U.S. elections. Fu- ture research should continue the effort to track plausible measures of po-

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litical uncertainty and such effects on asset markets. This endeavor could be strengthened in a multivariate modeling framework, thereby accounting for interaction and possible feedback among currencies in response to the same stimulus, as opposed to a univariate modeling approach such as presented in this study.

Received. January 1996 Final version. January 1997

References Alesina, Alberto. "Macroeconomic Policy in a Two-Party System as a Re-

peated Game." Quarterly Journal of Economics 102 (1987): 651-78. Alesina, Alberto, and Jeffrey Sachs. "Political Parties and the Business Cycle

in the United States, 1948-1984."Journal of Money, Credit and Banking 20 (February 1988): 63-82.

Alesina, Alberto, and Nouriel Roubini. "Political Cycles in OECD Econo- mies." In Monetary and Fiscal Policy Volume 2: Politics, edited by Tor- sten Persson and Guido Tabellini. Cambridge: MIT Press, 1994•

Almekinders, Geert J., and Sylvester C. W. Eijffinger. "Daily Bundesbank and Federal Reserve Interventions." Empirical Economics 19 (1994): 111-30.

Bachman, Daniel. "The Effect of Political Risk on the Forward Exchange Bias: The Case of Elections." Journal of International Money and Finance 11 (1992): 208-19.

Baillie, Richard T., and Tim Bollerslev. "The Message in Daily Exchange Rates: A Conditional-Variance Tale." Journal of Business & Economics Statistics 7 (1989): 297-305.

Baillie, Richard T., and Patrick C. McMahon. The Foreign Exchange Market: Theory and Econometric Evidence• Cambridge: Cambridge University Press, 1989.

Beck, Nathaniel. "Elections and the Fed: Is There a Political Monetary Cy- cle?" American Journal of Political Science 31 (1987): 194-216.

• "Politics and Monetary Policy." In Political Business Cycles: The Political Economy of Money, Inflation and Unemployment, edited by Thomas Willett. Durham: Duke University Press, 1988.

Bollerslev, Tim, Ray Y. Chou and Kenneth F. Kroner. "ARCH Modeling in Finance." Journal of Econometrics 52 (1992): 5-59.

Chappell, Henry Jr., and William R. Keech. "Party Differences in Macro- economic Policies and Outcomes." American Economic Review 76 (May 1986): 71-74.

Cukierman, A., and A. Meltzer. "A Theory of Ambiguity, Credibility, and

363

Bento J. Lobo and David Tufte

Inflation under Discretion and Asymmetric Information." Econometrica 53 (1986): 1099-1128.

Davidson, Lawrence S., Michele Fratianni and Jurgen yon Hagen. "Testing for Political Business Cycles." Journal of Policy Modeling 12 (1990): 35- 39.

Engle, Robert F., Takatoshi Ito and Wen-Ling Lin. "Meteor Showers or Heat Waves? Heteroskedastic Intra-Daily Volatility in the Foreign Ex- change Market." Econometrica 58, no. 3 (May 1990): 525-42.

Frey, Bruno S., and Friedrieh Sehneider. "An Empirical Study of Politico- Economic Interaction in the U.S." Review of Economics and Statistics 60/2 (1978): 174-83.

• "Politico-Economic Models in Competition with Alternative Mod- els: Which Predicts Better?" European Journal of Political Research 10 (1982): 241-54.

• "Do Governments Respond to Political Incentives?" European Jour- hal of Political Research 11/1 (1983): 125-26.

Golden, David and James Poterba. "The Price of Popularity: The Politieal Business Cycle Re-Examined." American Journal of Political Science 24 (1980): 696-714.

Havrilesky, Thomas. "Two Monetary and Fiscal Policy Myths." In Political Business Cycles: The Political Economy of Money, Inflation and Unem- ployment, edited by Thomas Willett. Durham: Duke University Press, 1988.

Haynes, Stephen, and Joe Stone. "Does the Political Business Cycle Dom- inate U.S. Unemployment and Inflation? Some New Evidence." In Po- litical Business Cycles: The Political Economy of Money, Inflation and Unemployment, edited by Thomas Willett. Durham: Duke University Press, 1988.

Hibbs, Douglas A. "Political Parties and Maeroeconomic Policies." American Political Science Review 71 (December 1977): 1467-87.

Koutmos, Gregory, and Panayiotis Theodossiou. "Time-Series Properties and Predictability of Greek Exchange Rates." Managerial and Decision Economics 15 (1994): 159-67.

Lothian, James R., and Mark P. Taylor. "Real Exchange Rate Behavior: The Recent Float from the Perspective of the Past Two Centuries." Journal of Political Economy 104 (1996): 488-509.

MacRae, Duncan. "A Political Model of the Business Cycle." Journal of Political Economy 85 (1977): 239-64.

McCallum, Bennett. "The Political Business Cycle: An Empirical Test." Southern Economic Journal 44 (1978): 504-15.

Nelson, Daniel B. "Conditional Heteroskedasticity in Asset Returns: A New Approach." Econometrica 59 (March 1991): 347-70.

364


Nordhaus, William. "The Political Business Cycle." Review of Economic Studies 42 (1975): 169-90.

Persson, Torsten, and Guido Tabellini, eds. Monetary and Fiscal Policy-- Vol 2: Politics. Cambridge: MIT Press, 1994.

Rogoff, K., and A. Sibert. "Equilibrium Political Business Cycles." Review of Economic Studies 55 (1988): 1-16.

Schneider, Friedrich, and Bruno S. Frey. "Pohtico-Economic Models of Macroeconomic Policy: A Review of the Empirical Evidence." In Political Business Cycles: The Political Economy of Money, Inflation and Unem- ployment, edited by Thomas D. Willet. Durham: Duke University Press, 1988.

Theodossiou, Panayiotis. "The Stochastic Properties of Major Canadian Ex- change Rates." The Financial Review 29, no. 2 (May 1994): 193-221.

Tufte, Edward. Political Control of the Economy. Princeton: Princeton Uni- versity Press, 1978.

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