Patterns and Pricing of Idiosyncratic Volatility in French Stock Market

Abstract

Purpose: The current research is to investigate the time series behavior of idiosyncratic volatility (IVOL) and its role in asset pricing in France in a twenty-year testing period. Design/methodology/approach: We test for the presence of trends in aggregate idiosyncratic and market volatility using Bunzel and Vogelsang’s (2005) t-dan test. We follow Bekaert et al. (2012) to test for regime shifts of both aggregate idiosyncratic and market volatilities. And then, we employ portfolio level analysis and cross-sectional univariate Fama-MacBeth regressions to examine the relationship between IVOL and cross-sectional stock returns in French stock market. Findings: First, we find that both idiosyncratic and market volatility do not exhibit long-term trends. Instead, their patterns are consistent with regime switching behavior. Second, though we initially find a strong significant negative IVOL effect in the French stock market which is robust in bi-variate Fama-MacBeth regressions, the negative IVOL effect is becoming marginal significant when we control for SIZE, BM, momentum, and short-term reversal simultaneously. Our new evidence suggests that there is a marginal IVOL effect in the French stock market adding to the increasing number of studies questioning the ubiquity of the negative IVOL puzzle. Originality/value: First, we present the first empirical evidence on examining the trends of both aggregate idiosyncratic and market volatilities, and the pricing role of IVOL in French stock market. We draw an attention for both academia and practitioners on an individual developed stock market. Second, we add new evidence to the mounting results questioning the ubiquity of the IVOL effect. This highlights the importance of country verification of so called anomalies in the US, even in developed markets. Finally, we confirm earlier evidence both aggregate idiosyncratic and market volatilities in the French stock market exhibits regime switching behavior rather than showing a long-term time trends.