Three essays on the econometric analysis of high-frequency data

Abstract

In three essays, this thesis deals with the econometric analysis of financial market data sampled at intraday frequencies. In particular, we focus on the dynamic and distributional modeling of high-frequency or intraday data, as well as its utilization for risk reduction in vast-dimensional portfolios. Chapter 1 presents a novel approach to model serially dependent positivevalued variables realizing a nontrivial proportion of zero outcomes. This is a typical phenomenon in financial high-frequency time series. We introduce a flexible point-mass mixture distribution, a tailor-made semiparametric specification test and a new type of multiplicative error model (MEM). Applying the proposed methodology to high-frequency cumulated trading volumes of liquid and illiquid NYSE stocks, we show that the model captures the dynamic and distributional properties of the data and is able to correctly predict future distributions. Chapter 2 addresses the problem that fixed symmetric kernel density estimators exhibit low precision for positive-valued variables with a large probability mass near zero, which is common in high-frequency data. We show that gamma kernel estimators are superior, while their relative performance depends on the specific density and kernel shape. We suggest a refined gamma kernel and a data-driven method for choosing the appropriate type of gamma kernel estimator. In a simulation study, we compare the refined estimator to the original gamma kernels and standard boundary-correction methods, demonstrating the superiority of the new approach. Chapter 3 turns to the open debate about the merits of high-frequency data in large-scale portfolio allocation. We consider the problem of constructing global minimum variance portfolios based on the constituents of the S&P 500. Covariance matrix predictions are obtained by applying a blocked realized kernel estimator, along with different smoothing windows, regularization methods and forecasting models. We show that forecasts based on high-frequency data can yield a significantly lower portfolio volatility than approaches using daily returns, implying noticeable utility gains for a risk-averse investor.