Measuring and Forecasting Financial Market Volatility using High-Frequency Data

Abstract

This dissertation consists of three studies on the use of intraday asset price data for accurate measurement and forecasting of financial market volatility. Chapter 2 proposes a refined heuristic bias-correction for the two time scales realized range-based volatility estimator in the presence of bid-ask bounce and non-trading. The merits are illustrated through simulations and an empirical forecasting application. Chapter 3 introduces a novel approach for estimating the covariance between asset returns using intraday high-low price ranges. The realized co-range estimator compares favourably to the realized covariance for plausible levels of microstructure noise and non-synchronous trading. The estimator is successfully implemented in a volatility timing strategy that deals with constructing mean-variance efficient asset allocation portfolios from stock, bond and gold futures. Chapter 4 introduces a mixed-frequency factor model for vast-dimensional covariance estimation. This original approach combines the use of high- and low-frequency data with a linear factor structure. We propose the use of highly liquid ETFs -- that are essentially free of microstructure frictions -- as factors such that factor covariances can be estimated with high precision from ultra-high-frequency data. The factor loadings are estimated from low-frequency data to bypass the potentially severe impacts of noise for individual stocks and to circumvent non-synchronicity issues between returns on stocks and liquid factors. Theoretical, simulation and empirical results illustrate that the mixed-frequency factor model is excellent, both compared to low-frequency factor models and to popular realized covariance estimators based on high-frequency data