Aggregate volatility risk and the cross-section of stock returns: Australian evidence

Anh, Van; Ang, Tze Chuan; Fang, Victor

doi:10.1016/j.pacfin.2015.12.006

Cited by 8 publications

(6 citation statements)

References 42 publications

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“…In fact, we find a consistent significant and negative (positive) relation between the aggregate volatility risk premium and the excess returns in the French stock market when it is rising ( falling). This is in line with previous research works (Ang et al, 2006, Mai et al, 2016, according to the multifactor models studies investors hedge the aggregate volatility risk, since the increase of aggregate volatility weaken their investment opportunities. The increase in demand for hedging pushes up asset prices positively correlated with global volatility and thus causes the decrease of the average excess returns.…”

Section: Discussionsupporting

confidence: 91%

“…Our findings are consistent with Whaley (2000) that the VCAC index is as "the investor fear gauge" of the French stock market. This correlation seems to be lower than the correlations reported in the American market by Ang et al (2006) and Mai et al (2016) (−0.66 and −0.42), respectively. The correlations between FVCAC and the other factors (SMB 5 , HML, RMW, CMA, WML) are low.…”

Section: Results[1]contrasting

confidence: 81%

“…The relation between FVCAC − and stock returns is significantly negative for the β MKT and β ΔVCAC portfolio. In contrast to Mai et al (2016), our results suggest that aggregate volatility risk factor explains very well the average excess returns when the aggregate volatility increases. This relation exists also when the aggregate volatility decreases but only for the size-book to market, size and investment, size and momentum and β MKT and β ΔVCAC sorts.…”

Section: Asymmetric Aggregate Volatility Risk Factorcontrasting

confidence: 83%

“…Ang et al (2006) confirm these results using changes in the implied volatility (VIX) as a proxy for aggregate volatility. Moreover, many researchers such as Beugelsdijk and Frijns (2010) and Mai et al (2016) find that the VIX is the best predictor of the future market volatility. In addition, other authors support higher predictive power for a two-factors model composed by market and risk aversion factors than Fama and French's (1993) three-factor model (see, e.g.…”

Section: Aggregate Volatility Risk Premiummentioning

confidence: 99%

“…Several studies such as Ang et al (2006), Delisle et al (2011), Mai et al (2016 document an asymmetric relation between aggregate volatility risk and stock returns. To examine if the pricing of aggregate volatility risk is asymmetric we perform the robustness test by dividing the sample into positive FVCAC + and negative FVCAC − aggregate volatility and zero otherwise.…”

Section: Asymmetric Aggregate Volatility Risk Factormentioning

confidence: 99%

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Evidence on aggregate volatility risk premium for the French stock market

Chakroun

Hmaied

2019

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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 αs 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.

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Section: Discussionsupporting

confidence: 91%