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Evidence on aggregate volatility risk premium for the French stock market

机译：法国股市总波动率风险溢价的证据

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Purpose - The purpose of this paper is to examine alternative six- and seven-factor equity pricing models directed at capturing a new factor, aggregate volatility, in addition to market, size, book to market, profitability, investment premiums of the Fama and French (2015) and Fama and French's (2018) aggregate volatility augmented model. Design/methodology/approach - The models are tested using a time series regression and Fama and Macbeth's (1973) methodology. Findings - The authors show that both six- and seven-factor models best explain average excess returns on the French stock market. In fact, the authors outperform Fama and French's (2018) model. The authors use sensitivity of aggregate volatility of a stock VCAC as a proxy to construct the aggregate volatility risk factor. The spanning tests suggest that Fama and French's (1993, 2015, 2018) and Carhart's (1997) models do not explain the aggregate volatility risk factor FVCAC. The results show that the FVCAC factor earns significant as across the different multifactor models and even after controlling for the exposure to all the other in Fama and French's (2018) model. The asset pricing tests show that it is systematically priced. In fact, the authors find a significant and negative (positive) relation between the aggregate volatility risk factor and the excess returns in the French stock market when it is rising (falling), in addition, periods with downward market movements tend to coincide with high volatility. Originality/value - The authors contribute to the related literature in several ways. First, the authors test two new empirical six- and seven-factor model and the authors compare them to Fama and French's (2018) model. Second, the authors give new evidence about the VCAC, using it for the first time to the authors' knowledge, to construct a volatility risk premium.
机译：目的-本文的目的是研究六种因素和七种因素的股票定价模型，这些模型旨在获取新的因素，总体波动性，市场，规模，按市值计价，获利能力，Fama和French的投资溢价（2015）和Fama and French（2018）的总体波动率增强模型。设计/方法/方法-使用时间序列回归以及Fama和Macbeth（1973）的方法对模型进行测试。调查结果-作者表明，六因素模型和七因素模型均能最好地解释法国股票市场的平均超额收益。实际上，作者的表现优于Fama和French（2018）的模型。作者使用股票VCAC的总波动率敏感性作为构建总波动率风险因子的替代指标。跨度检验表明，Fama和French（1993、2015、2018）和Carhart（1997）模型不能解释总波动风险因子FVCAC。结果表明，即使在控制Fama和French（2018）模型中所有其他因素的敞口之后，FVCAC因子在不同的多因素模型中也获得了显着收益。资产定价测试表明，它是系统定价的。实际上，作者发现，总波动率风险因素与法国股市上涨（下跌）时的超额收益之间存在显着的负相关（正），此外，市场下跌的时期往往与高点相吻合。挥发性。原创性/价值-作者以多种方式为相关文献做出贡献。首先，作者测试了两个新的经验六因素模型和七因素模型，并将他们与Fama和French（2018）模型进行了比较。第二，作者提供了有关VCAC的新证据，这是作者第一次了解该VCAC，以构建波动性风险溢价。

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