This thesis consists of three chapters in Bayesian financial econometrics. The first chapter proposes
new dynamic component models of returns and realized covariance (RCOV) matrices based on timevarying
Wishart distributions. Bayesian estimation and model comparison is conducted with a range of
multivariate GARCH models and existing RCOV models from the literature. The main method of model
comparison consists of a term-structure of density forecasts of returns for multiple forecast horizons. The
new joint return-RCOV models provide superior density forecasts for returns from forecast horizons of
1 day to 3 months ahead as well as improved point forecasts for realized covariances. Global minimum
variance portfolio selection is improved for forecast horizons up to 3 weeks out. The second chapter
proposes a full Bayesian nonparametric procedure to investigate the predictive power of exchange rates on
commodity prices for 3 commodity-exporting countries: Canada, Australia and New Zealand. I examine
the predictive effect of exchange rates on the entire distribution of commodity prices and how this effect
changes over time. A time-dependent infinite mixture of normal linear regression model is proposed for
the conditional distribution of the commodity price index. The mixing weights of the mixture follow a
Probit stick-breaking prior and are hence time-varying. As a result, I allow the conditional distribution of
the commodity price index given exchange rates to change over time nonparametrically. The empirical
study shows some new results on the predictive power of exchange rates on commodity prices. The
third chapter proposes a flexible way of modeling heterogeneous breakdowns in the volatility dynamics
of multivariate financial time series within the framework of MGARCH models. During periods of
normal market activities, volatility dynamics are modeled by a MGARCH specification. I refer to any
significant temporary deviation of the conditional covariance matrix from its implied GARCH dynamics
as a covariance breakdown, which is captured through a stochastic component that allows for changes in
the whole conditional covariance matrix. Bayesian inference is used and I propose an efficient posterior
sampling procedure. Empirical studies show the model can capture complex and erratic temporary
structural change in the volatility dynamics.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OTU.1807/34069 |
| Date | 13 December 2012 |
| Creators | Jin, Xin |
| Contributors | Maheu, John |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
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