Search

Asset pricing models represent one of the most discussed and researched areas in finance. They are widely used in a theoretical and practical manner to model and predict risk and return to price securities and portfolios as well as in corporate finance to estimate the cost of capital and rank investment projects. They provide a usable measure of risk that helps managers and investors determine what return they deserve for putting their money at risk. The objective of this paper is to analyze the performance of the learning-augmented conditional CAPM model of Adrian and Franzoni (2009) when applied to the returns of the most liquid stocks transactioned in the Brazilian stock market from 1987 to 2010. Adrian and Franzoni (2009), in their paper, complemented the conditional CAPM literature by modeling a new type of time-variation in conditional betas. In this environment, investors form expectations about the long run level of factor loadings from the observation of realized returns of exogenous variables. As a direct consequence of this assumption, conditional betas are modeled using the Kalman filter. Using data of 25 portfolios sorted by size and book-to-market ratio, the authors concluded that the learning-augmented conditional CAPM is able to substantially reduce the pricing errors when compared to the original version of CAPM. Thus, we contribute to the pricing asset literature, as we evaluate whether this model is able to reduce pricing errors in relation to its original version when applied to Brazilian individual asset data. The results of this article showed a decreasing in the pricing errors of learning-augmented conditional CAPM in relation to CAPM in its original version. Our empirical results suggests that the learning about betas should be taken into account when estimating both conditional and unconditional CAPM. [ABSTRACT FROM AUTHOR]

The financial series are strongly characterized by its volatility, alternating periods of high and low volatility, i.e., the variance of these series is not constant, varying with time. Thus, the series may have heterocedastidade financial circumstances. The paper analyzes the behavior of the series of returns of the indices IVBX-2 and SMLL in 2008, aiming to identify the volatility of the series. The central axis of the work is theoretical theory of efficient markets, it believes that all relevant information is already incorporated into stock prices, so the price is a good measure of value of shares. The indices studied are characterized as representing hypothetical portfolio consisting of shares of lesser value and lesser liquidity, these characteristics imply a higher risk and higher return associated with investments in such portfolio. The methodology adopted was the use of ARCH models in the family. The implementation of the models indicate the series have similar behavior, with the coefficient which measures the persistence of shocks around 1 and the impacts of these shocks occur so asymmetrical, where the negative shocks have a greater effect than positive shocks. [ABSTRACT FROM AUTHOR]

Asset pricing models have been a constant theme in finance research. Since the Capital Asset Pricing Model (CAPM) proposed by Sharpe (1964), such models linearly relate the expected rate of returns of an asset or portfolio of assets with systemic risk factors. This research presents a test of a pricing model with risk factors based on statistical comoments and using a Brazilian dataset. The proposed model is an extension of the original CAPM with the addition of coskewness and cokurtosis between stock rates of return and the market portfolio rates of return. The effects of other variables such as market to book value, financial leverage and a negotiability index served as control variables. The sample consisted of 179 non-financial Brazilian companies traded on BM&FBovespa with data available from 2003 to 2007. Annual systemic moments were calculated from weekly rates of return. They were then tested on a pricing model in order to check for the existence of a risk premium associated with each of these risk measures. We employed a Generalized Method of Moments (GMM) panel data analysis. The use of GMM aims to address potential problems of endogeneity and simultaneous determination of the data, avoiding the occurrence of bias in the estimates. The estimation results show that the relationship between rates of return and covariance and cokurtosis are statistically significant. The results were robust to alternative model specifications. This research contributes to the literature by presenting empirical evidence that there is a Brazilian risk premium associated with systemic moments. [ABSTRACT FROM AUTHOR]

Stock market indices' rate of return distributions is a recurrent theme in financial literature, especially because some models require the normality assumption as premise. Following the works of Ingersoll Jr. (1987) and Hamada & Valdez (2004), the empirical distribution of the Ibovespa and S&P500 rates of returns from 1986 to 2006 was analyzed. As reported by some others authors, for both indices, the results evidenced that the normality assumption can be highly questionable, because other theoretical distributions from the elliptical family fitted the data better. The rates of return for the Brazilian and American indices showed leptokurtosis, and, in the Brazilian case, asymmetric shape, so that only distributions that deal with these parameters in a less restrictive way, as the Logistic or Log-logistic distributions, can show a better fit to the observed data. [ABSTRACT FROM AUTHOR]