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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.

Thesis (Ph. D.)--Rutgers University, 2008. / "Graduate Program in Philosophy." Includes bibliographical references (p. 184-187).

Thesis (Ph. D.)--Washington State University, December 2009. / Title from PDF title page (viewed on Feb. 12, 2010). "School of Economic Sciences." Includes bibliographical references (p. 64-66).

Thesis (Ph. D.)--University of Washington, 2003. / Vita. Includes bibliographical references (leaves 64-67).

With the increasingly economic activities, people have more and more interest in econometric models. There are two mainstream econometric models which are very popular in recent decades. One is quantile autoregressive (QAR) model which allows varying-coefficients in linear time series and greatly promotes the ranges of regression research. The first topic of this thesis is to focus on the modeling of QAR model. We propose two important measures, quantile correlation (QCOR) and quantile partial correlation (QPCOR). We then apply them to QAR models, and introduce two valuable quantities, the quantile autocorrelation function (QACF) and the quantile partial autocorrelation function (QPACF). This allows us to extend the Box-Jenkins three-stage procedure (model identification, model parameter estimation, and model diagnostic checking) from classical autoregressive models to quantile autoregressive models. Specifically, the QPACF of an observed time series can be employed to identify the autoregressive order, while the QACF of residuals obtained from the model can be used to assess the model adequacy. We not only demonstrate the asymptotic properties of QCOR, QPCOR, QACF and PQACF, but also show the large sample results of the QAR estimates and the quantile version of the Ljung- Box test. Moreover, we obtain the bootstrap approximations to the distributions of parameter estimators and proposed measures. Simulation studies indicate that the proposed methods perform well in finite samples, and an empirical example is presented to illustrate the usefulness of QAR model. The other important econometric model is autoregressive conditional duration (ACD) model which is developed with the purpose of depicting ultra high frequency (UHF) financial time series data. The second topic of this thesis is designed to incorporate ACD model with one of the extreme value distributions, i.e. Fréchet distribution. We apply the maximum likelihood estimation (MLE) to Fréchet ACD models and derive its generalized residuals for model adequacy checking. It is noteworthy that simulations show a relative greater sensitiveness in the linear parameters to sampling errors. This phenomenon successfully reflects the skewness of the Fréchet distribution and suggests a method to practitioners in proceeding model accuracy. Furthermore, we present the empirical sizes and powers for Box-Pierce, Ljung-Box and modified Box-Pierce statistics as comparisons of the proposed portmanteau statistic. In addition to the Fréchet ACD, we also systematically analyze theWeibull ACD, where the Weibull distribution is the other nonnegative extreme value distribution. The last topic of the thesis explains the estimation and diagnostic checking the Weibull ACD model. By investigating the MLE in this model, there exhibits a slight sensitiveness in linear parameters. However, there is an obvious phenomenon on the trade-off between the skewness of Weibull distribution and the sampling error when the simulations are conducted. Moreover, the asymptotic properties are also studied for the generalized residuals and a goodness-of-fit test is employed to obtain a portmanteau statistic. Through the simulation results in size and power, it shows that Weibull ACD is superior to Fréchet ACD in specifying the wrong model. This is meaningful in practice. / published_or_final_version / Statistics and Actuarial Science / Doctoral / Doctor of Philosophy

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The conventional approach in the theory and econometrics of investment is the partition of gross capital investment in two components: expansion (or net) investment and replacement investment. This thesis examines the latter component. A critical assessment of the literature and the empirical evidence reveal that the prevailing view of replacement known as the "proportional replacement hypothesis" is incorrectly specified and unsatisfactory. / This thesis examines a variety of data brought together under the same focus for the first time and comes up with two important findings. First, firms maintain the operating capacity of their equipment not by replacing the whole of the machine but by replacing worn out or defective parts. The cost of new parts along with that of labour and materials incurred in restoring the operating efficiency of machines are known as "repair expenditures". Data on these expenditures have been collected by Statistics Canada in its investment survey since 1947. Although in effect replacement expenditures, these data are not capitalized by firms and hence do not appear in our conventional investment statistics. Although they account for a significant proportion of capital expenditures they are completely ignored in the theory and econometrics of replacement. Second, expansion and maintenance of production capacity are not the only purposes for which firms invest funds. They also invest for a variety of other purposes, such as modernization, upgrading, retooling, revamping and pollution abatement, for example. These activities lower unit costs of production and enhance the profitability of the firm by initiating or responding to changes in the structure of demand, technology, the prices of factor inputs or the market structure. Such capital expenditures entail changes in capital-output and capital-input specificity. As the real world is characterized by capital and output heterogeneity, structural change therefore implies structural investment. / Important policy implications arise from the above findings. Tax incentives may be more effectively utilized when targeted toward firms undertaking structural investment rather than either expansion or replacement. Since repair expenditures are not included in standard investment statistics, the level of investment spending is significantly higher than conventionally thought. Also our capital stock data, particularly net capital figures, may be more deficient than previously presumed.