Existing models of market herding suffer from several drawbacks. Measures that assume herd behaviour is constant over time or independent of the economy are not only economically unreasonable, but describe the data poorly. First, if returns are stationary, then a two-regime model is required to describe the data. Second, existing models of time-varying herding cannot be estimated from daily or weekly data, and are unable to accommodate factors that explain changes in this behaviour. To overcome these deficiencies, this paper proposes a Markov switching herding model. By means of time-varying transition probabilities, the model is able to link variations in herding behaviour to proxies for sentiment or the macroeconomic environment. The evidence for the US stock market reveals that during periods of high volatility, investors disproportionately rely on fundamentals rather than on market consensus.

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CIGI Papers present in-depth analysis and discussion on governance-related subjects. They include policy papers that present CIGI experts' positions or contributions to policy debates, and background papers that contain research findings, insights and data that contribute to the development of policy positions.
  • Martin T. Bohl is professor of economics, Centre for Quantitative Economics, Westphalian Wilhelminian University of Münster. From 1999 to 2006, he was a professor of finance and capital markets at the European University Viadrina Frankfurt (Oder). His research focuses on monetary theory and policy as well as financial market research.

  • Pierre Siklos is a CIGI senior fellow. His research interests include applied time series analysis and monetary policy, with a focus on inflation and financial markets.

  • Arne C. Klein is an assistant lecturer in the Department of Economics at the Westphalian Wilhelminian University of Münster. From July to October 2011, he was a visiting scholar at Wilfrid Laurier University, Waterloo, Canada.