|dc.description.abstract||This thesis consists of two main parts. The first part deals with
an analysis of realized volatility and its relationship with market microstructure problem. The second part of the thesis presents a time
trend analysis in a panel data framework, with a semiparametric approach.
Chapter 1 introduces the topics that I embark upon the thesis. In
particular, I motivate the interest in realized volatility and market microstructure problem in the first part of the thesis, with a factor model
approach. Then, in the second part, the motivation is on the estimation
of time varying coefficient trend functions in a panel data case, using
nonparametric estimation methods.
Chapter 2 proposes a literature review on realized volatility and
factor models, while focusing on the seminal papers and models that the
theoretical literature suggests and also provides the empirical evidence
observed in financial markets.
Chapter 3 develops a theoretical model to forecast the realized
volatility consistently and efficiently for large dimensional datasets and
also addresses the solution for noise problem coming out of volatility
estimation in the presence of market microstructure effects.
Chapter 4 provides the empirical analysis and results on a sample
of S&P 500 stocks following the methodology and models suggested in
Chapter 5 focuses on developing a semiparametric panel model to
explain the time trend function. Profile likelihood estimators (PLE)
are proposed and their statistical properties are studied. We apply our methods to the UK regional temperatures. Finally, forecasting based
on the proposed model is studied.
Chapter 6 concludes, summarizing the main results and contributions of the thesis.||en_US