Essays on volatility models using EMM estimation

Thesis (Ph. D.)--University of Washington, 2006.Academic researchers and investment institutions have devoted a significant amount of their efforts over the past two decades to developing and testing sophisticated models of the volatility dynamics of different types of asset-pricing data. In a set of three essays, I analyze the various aspects of model specification issues by employing the efficient method of moments (EMM) technique. After an introduction to EMM methodology and procedure, my first essay offers a comprehensive comparison of univariate volatility models for US short rates. We find that a continuous-time two-factor SV model, a continuous-time three-factor SV model, and a discrete-time RS-in-volatility model with level effect can well explain the salient features of the short rate. We also show that either an SV model with a level effect or a RS model with a level effect, but not both, is needed for explaining the data. Our EMM estimates of the level effect are much lower than unity, but around 1/2 after incorporating the SV effect or the RS effect. The second essay applies appropriate filtering and smoothing algorithms on the simulated data from the preferred volatility models and on the series of the US short rate. The third essay aims to carry out a Monte Carlo experiment for estimating a Markov RS model using EMM. This dissertation spans the fields of Econometrics, Finance and Macroeconomics; it provides crucial insights for both academics and practitioners due to its implications for macroeconomic policy and for microeconomic decisions in the theory and practice of asset pricing, asset allocation, and risk management