This paper examines the cost of capital of French industry by comparing different multifactor models and analyzing the risk factor-sector returns relationship over a full sample and business cycles using the quantile regression approach. Specially, these models rely on the Fama and French (2018) and Carhart (1997) aggregate volatility augmented models. For robustness, we adopt a time-series regression methodology and test the asymmetry of the volatility’s risk factor by estimating its increasing (decreasing) price. This paper concludes that an industry sorting may produce better observation of risk factors-stock returns relationship. The QR approach suggests that some results are quantile dependent. The business cycle regime confirms that the models do not seem to be stable throughout the economic cycle. In addition, sectors have heterogeneous sensitivities to economic conditions. The time series regression confirms the QR approach and shows that risk premiums relevance and model performance depend on sectors. The asymmetric relation between aggregate volatility and stock returns is confirmed for the French stock market across sectors.
Keywords: Asset pricing models, Aggregate volatility, Fama and French (2018), Sectors/Industry, Business cycle, Quantile Regression approach.