Patterns and Pricing of Idiosyncratic Volatility in the French Stock Market

We investigate the time series behavior of idiosyncratic volatility and its role in asset pricing in France. We find that both aggregate idiosyncratic and market volatility exhibit regime switching behavior similar to that in the U.S. and other developed countries. Furthermore, we find a marginally significant negative IVOL effect in the French stock market. We add new evidence to the mounting results questioning the ubiquity of the IVOL effect which highlights the importance of country verification of so called anomalies in the US, even in developed markets

Extreme returns in emerging stock markets: Evidence of a MAX effect in South Korea

We investigate the significance of extreme positive returns (MAX) in the cross-sectional pricing of stocks in South Korea. Our results provide important out-of-sample evidence of a strong negative MAX effect similar to that documented by Bali et al. (2011) in the US stock market. For equal-weighted portfolios, the difference between returns on the portfolios with the highest and lowest maximum daily returns is −1.87% per month. The corresponding difference in alpha is −1.41% per month. The results are robust to controls for size, value, skewness, momentum, short-term reversal and idiosyncratic volatility. We also sort the portfolios by the average of the five highest daily returns within the month and report return and alpha spreads of −2.21% and −2.01% per month, respectively. However, unlike in Bali et al. (2011), the MAX effect cannot reverse the idiosyncratic volatility effect in the South Korean stock market. Our results imply investor preference for high-MAX stocks, consistent with cumulative prospect theory (CPT) where investors sub-optimally overweight the possibility that extreme returns will persist. The MAX effect is also consistent with the optimal expectations framework where investors derive utility from overestimating the probabilities of events in which their investments pay off well

Switching-regime regression for modeling and predicting a stock market return

It has been observed that certain economic and financial variables commonly exhibit switching behavior depending on their magnitude. This phenomenon in general cannot be naturally captured by the linear regression (LR), which assumes a linear relationship between the dependent and explanatory variables. To decipher investor behavior more appropriately by accounting for this observation, a switching-regime regression (SRR) is proposed and applied to the S&P 500 market return with respect to seven explanatory variables. It is shown that, compared with LR, the new regression results in a significantly improved adjusted R2, increasing from less than 4 % to over 50 %. In addition, SRR yields better out-of-sample forecasting performance, besides that the fitted values from the new regression even resemble the dip during the 2008 financial crisis, while those from LR do not. The study thus indicates that the switching-regime regression improves significantly the statistical properties including the goodness of fit as well as conforms more to investor behavior theory