Herding Behavior And Volatility Clustering In Financial Markets PDF Download

Financial time series have been shown to exhibit market regularities, so-called stylized facts, which have challenged the rational expectations and efficient market theory. In order to explain those market regularities, behavioral finance economists developed a broad range of agent-based models consisting of agents with heterogeneous expectations on future prices. Agents were not only assumed to have heterogeneous expectations and different trading strategies, they were furthermore assumed to be able to switch between the strategies. The present paper focuses on one particular market regularity, which is volatility clustering of financial time series in the framework of the foreign exchange market. The goal is to explain the phenomenon of volatility clustering from a behavioral finance perspective. In a first step, an overview over common Forex market characteristics is provided, followed by some traditional models of exchange rate determination and the subsequent paradigm shift in the concept of expectations. After having presented the main behavioral explanations on volatility clustering, an agent-based model is introduced, capturing the idea of agent's inertia, as one possible driver of volatility clustering in financial markets. The introduced agent-based model represents an extension of the original model by Frank Westerhoff (2010). The present paper contributes to the behavioral finance literature by enlightening one novel aspect of agent's behavior that may affect price dynamics in financial markets.