Presidential elections and the stock market

Comparing Markov-switching and fractionally integrated GARCH models of volatility

David Leblang, Subhanan Mukherjee

Research output: Contribution to journalArticle

21 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)296-322
Number of pages27
JournalPolitical Analysis
Volume12
Issue number3
DOIs
StatePublished - Jun 1 2004

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stock market
presidential election
financial market
econometrics
investor
candidacy
election
statistics
labor
regression
politics
market

All Science Journal Classification (ASJC) codes

  • Sociology and Political Science
  • Political Science and International Relations

Cite this

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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.",
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Presidential elections and the stock market : Comparing Markov-switching and fractionally integrated GARCH models of volatility. / Leblang, David; Mukherjee, Subhanan.

In: Political Analysis, Vol. 12, No. 3, 01.06.2004, p. 296-322.

Research output: Contribution to journalArticle

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