In this dissertation, I test the implications of Campbell and Vuolteenaho's (2004) intertemporal capital asset pricing model (ICAPM) based on the return decomposition of Campbell (1991). The return decomposition states that the unexpected market return is composed of cash-flows news and discount-rate news. The previous literature uses VAR models to implement the return decomposition. My dissertation adds to the literature by proposing a state-space model approach. Importantly, the proposed approach has two advantages over the VAR approach. First, the state-space approach directly and simultaneously models both cash-flow news and discount-rate news by expressing the relation between returns and news in terms of linear regressions, whereas the VAR approach models discount-rate news and backs out cash-flow news as the residual between the unexpected market return and discount-rate news. Second, the information that I use to extract cash-flow news and discount-rate news is directly related to the market return, whereas the information used in the VAR approach is not. A Bayesian Markov chain Monte Carlo methodology is developed and used to estimate the state-space model. Based on the state-space model and the data set used in this dissertation, I am not able to explain the value premium because I do not find that value stocks have higher cash-flow betas than do growth stocks. The reason is likely that the cash-flow news and discount-rate news estimated in this dissertation are strongly negatively correlated. With this state-space model, I am now in a position to systematically examine the implications of Campbell and Vuolteenaho's (2004) ICAPM using other data sets and other time periods in future studies.
|Commitee:||Baptista, Alexandre M., Hwang, Min, Jostova, Gergana, Soyer, Refik|
|School:||The George Washington University|
|School Location:||United States -- District of Columbia|
|Source:||DAI-A 73/12(E), Dissertation Abstracts International|
|Keywords:||Cash-flow news, Discount-rate news, Intertemporal CAPM, Return decomposition, State space models|
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