We consider the stochastic volatility model with smooth transition and persistent la-
tent factors. We argue that this model has advantages over the conventional stochastic
model for the persistent volatility factor. Though the linear filtering is widely used
in the state space model, the simulation result, as well as theory, shows that it does
not work in our model. So we apply the density-based filtering method; in particular,
we develop two methods to get solutions. One is the conventional approach using
the Maximum Likelihood estimation and the other is the Bayesian approach using
Gibbs sampling. We do a simulation study to explore their characteristics, and we
apply both methods to actual macroeconomic data to extract the volatility generating
process and to compare macro fundamentals with them.
Next we extend our model into multivariate model extracting common and id-
iosyncratic volatility for multivariate processes. We think it is interesting to apply
this multivariate model into measuring time-varying uncertainty of macroeconomic
variables and studying the links to market returns via a consumption-based asset pric-
ing model. Motivated by Bansal and Yaron (2004), we extract a common volatility
factor using consumption and dividend growth, and we find that this factor predicts
post-war business cycle recessions quite well. Then, we estimate a long-run risk model
of asset prices incorporating this macroeconomic uncertainty. We find that both risk aversion and the intertemporal elasticity of substitution are estimated to be around
two, and our simulation results show that the model can match the first and second
moments of market return and risk-free rate, hence the equity premium.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/86017 |
| Date | 10 October 2008 |
| Creators | Lee, Hyoung Il |
| Contributors | Park, Joon Y. |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | electronic, born digital |
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