Abstract
This paper aims to evaluate the effects of the aggregate market volatility components - average volatility and average correlation - on the pricing of portfolios sorted by idiosyncratic volatility, using Brazilian data. The study investigates whether portfolios with high and low idiosyncratic volatility - in relation to the Fama and French model (1996) - have different exposures to innovations in average market volatility, and consequently, different expectations for return. The results are in line with those found for US data, although they portray the Brazilian reality. Decomposition of volatility allows the average volatility component, without the disturbance generated by the average correlation component, to better price the effects of a worsening or an improvement in the investment environment. This result is also identical to that found for US data. Average variance should thus command a risk premium. For US data, this premium is negative. According to Chen and Petkova (2012), the main reason for this negative sign is the high level of investment in research and development recorded by companies with high idiosyncratic volatility. As in Brazil this type of investment is significantly lower than in the US, it was expected that a result with the opposite sign would be found, which is in fact what occurred.
Highlights
For a factor model, with factors that reflect the return on tradable portfolios, the constant for the equation that describes the model, normally defined as α, serves as an indicator of how well specified the model is
The results found in the literature, concerning the US data, show that the average variance component better predicts the effects of a worsening or an improvement in the investment environment than total variance, as well as commanding a negative price of risk for expected return on the portfolios
Companies with a high level of research and development (R&D) would serve as a hedge in periods of market deterioration, which would lead investors to accept paying a premium for them. This effect would cause a differentiation in the price of risk of these assets; their sensitive loads to the factors that predict a worsening in the market would be positive, that is, they would perform better than other companies in poor scenarios, leading to a negative price of risk, that is, a reduction in their expected return, since they would be seen as lower risk companies
Summary
With factors that reflect the return on tradable portfolios, the constant for the equation that describes the model, normally defined as α, serves as an indicator of how well specified the model is. Ang, Hodrick, Xing, and Zhang (2006) show that volatility of market return is priced as a risk factor in asset portfolios Based on this evidence, their studies tested this measure as a factor missing in the Fama and French (1996) model. Portfolios are perceived as, more risky, and for this reason, have a high discount rate on their cash flows in the case of an increase in average volatility, reducing their price and with this increasing the expected return on them; this effect is captured by the positive risk price indicated in the results of this paper. As a result of this, it is believed that it contributes to a better understanding of the issue, and presents new evidence regarding portfolio pricing in Brazil
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