This thesis consists of two essays which contribute to different but related aspects of
the empirical asset pricing literature. The common theme is that incorrect restrictions
can lead to inaccurate decisions. The first essay demonstrates that failure to account
for the Federal Reserve experiment can lead to incorrect assumptions about the explosiveness
of short-term interest rate volatility, while the second essay demonstrates
that we need to incorporate skewness to develop models that adequately account for
the cross-section of equity returns.
Essay 1 empirically compares the Markov-switching and stochastic volatility diffusion
models of the short rate. The evidence supports the Markov-switching diffusion
model. Estimates of the elasticity of volatility parameter for single-regime models
unanimously indicate an explosive volatility process, whereas the Markov-switching
models estimates are reasonable. We find that either Markov-switching or stochastic
volatility, but not both, is needed to adequately fit the data. A robust conclusion is
that volatility depends on the level of the short rate. Finally, the Markov-switching
model is the best for forecasting. A technical contribution of this paper is a presentation
of quasi-maximum likelihood estimation techniques for the Markov-switching
stochastic-volatility model.
Essay 2 proposes a new approach to estimating and testing nonlinear pricing models
using GMM. The methodology extends the GMM based conditional mean-variance
asset pricing tests of Harvey (1989) and He et al (1996) to include preferences over
moments higher than variance. In particular we explore the empirical usefulness of
the conditional coskewness of an assets return with the market return in explaining
the cross-section of equity returns. The methodology is both flexible and parsimonious.
We avoid modelling any asset specific parameters and avoid making restrictive
assumptions on the dynamics of co-moments. By using GMM to estimate the models'
parameters we also avoid making any assumptions about the distribution of the data.
The empirical results indicate that coskewness is useful in explaining the cross-section
of equity returns, and that both covariance and coskewness are time varying. We also
find that the usefulness of coskewness is robust to the inclusion of Fama and French's
(1993) SMB and HML factor returns.
There is an interesting debate raging in the empirical asset pricing literature comparing
the SDF versus beta methodologies. This paper's technique is a conditional
version of the beta methodology, which turns out to be directly comparable with
the SDF methodology with only minor modifications. Our SDF version imposes the
CAPM's restrictions that the coefficients in the pricing kernel are known functions of
the moments of market returns, which are modelled using macro-variables. We find
that the SDF implied by the three-moment CAPM provides a better fit in this data
set than current practice of parameterizing the coefficients on market returns in the
SDF. This has an interesting application to the current SDF versus beta methodology
debate. / Business, Sauder School of / Finance, Division of / Graduate
| Identifer | oai:union.ndltd.org:UBC/oai:circle.library.ubc.ca:2429/13589 |
| Date | 11 1900 |
| Creators | Smith, Daniel Robert |
| Source Sets | University of British Columbia |
| Language | English |
| Detected Language | English |
| Type | Text, Thesis/Dissertation |
| Format | 6075504 bytes, application/pdf |
| Rights | For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use. |
Page generated in 0.0182 seconds