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Presidential Elections and the Stock Market: Comparing Markov-Switching and Fractionally Integrated GARCH Models of Volatility

Published online by Cambridge University Press:  04 January 2017

David Leblang  and
Bumba Mukherjee
David Leblang
Affiliation:
Department of Political Science, University of Colorado, 106 Ketchum Hall, Boulder, CO 80309. e-mail: [email protected]
Bumba Mukherjee
Affiliation:
Department of Political Science, Florida State University, 554 Bellamy Building, Tallahassee, FL 32306. e-mail: [email protected]
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Abstract

Existing research on electoral politics and financial markets predicts that when investors expect left parties—Democrats (US), Labor (UK)—to win elections, market volatility increases. In addition, current econometric research on stock market volatility suggests that Markov-switching models provide more accurate volatility forecasts and fit stock price volatility data better than linear or nonlinear GARCH (generalized autoregressive conditional heteroskedasticity) models. Contrary to the existing literature, we argue here that when traders anticipate that the Democratic candidate will win the presidential election, stock market volatility decreases. Using two data sets from the 2000 U.S. presidential election, we test our claim by estimating several GARCH, exponential GARCH (EGARCH), fractionally integrated exponential GARCH (FIEGARCH), and Markov-switching models. We also conduct extensive forecasting tests—including RMSE and MAE statistics as well as realized volatility regressions—to evaluate these competing statistical models. Results from forecasting tests show, in contrast to prevailing claims, that GARCH and EGARCH models provide substantially more accurate forecasts than the Markov-switching models. Estimates from all the statistical models support our key prediction that stock market volatility decreases when traders anticipate a Democratic victory.

Type
Research Article
Information
Political Analysis , Volume 12 , Issue 3 , Summer 2004 , pp. 296 - 322
Copyright
Copyright © Society for Political Methodology 2004 

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