Dividends And Stock Valuation: A Study From The Nineteenth To The Twenty-First Century Page 7

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specific to a few time periods (e.g. Goyal and Welch (2003)). As a result, there is uncertainty regarding
the importance one should give to dividends in the valuation of equities over time.
Similar to the mixed evidence regarding the predictive power for the dividend yield, researchers
have struggled to estimate the growth rate of dividends. Arnott and Bernstein (2002), for example,
provide an interesting historical perspective on the differences in how investors in the early 1900s viewed
dividends as compared to how they are viewed today. To handle some of these differences, dividend
growth rates have been modeled using a variety of different econometric models. For example, Bollerslev
and Hodrick (1995) and Donaldson and Kamstra (1996) use time-series models to predict dividend
behavior and find that a number of models do a reasonable job of explaining both changes in dividends
and changes in prices.
Despite the mixed evidence surrounding the value of the dividend-based valuation models and the
estimates of the dividends and their growth rates, empirical asset pricing models continue to include these
factors in their set of fundamental economic risk factors. This suggests that researchers continue to
believe these factors play a significant role in explaining the risk valued by investors. By studying what
influences the level and growth rate of dividends within the context of their relationship to the value of
the asset using dividend-based valuation techniques, our study provides new insights into what economic
risk factors should be included in asset pricing models.
2.2 Discount Rates and the Equity Premium
Previous studies have proposed a series of explanations for the relatively poor ability of asset
pricing models to explain the expected returns for equities. One of the most commonly proposed reasons
is the possible presence of a time-varying risk premium in the equities markets. To address this concern,
studies have employed a wide variety of different approaches. The approaches range from using GARCH
models to capture the time varying conditional volatility in betas within a CAPM framework (e.g.,
Bollerslev, Engle and Wooldridge (1988)) to using conditioning information to scale the estimated betas
in a multi-factor asset pricing model (e.g., Cochrane (1998)). Studies using these results have provided
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