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.

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Dr. M.P.E. Martens, Erasmus School of Economics (ESE), Erasmus University Rotterdam (EUR), Prof.dr. H.P. Boswijk, Prof.dr. M.J. McAleer, Prof.dr. R. Paap
D.J.C. van Dijk (Dick)
Erasmus University Rotterdam , Erasmus Research Institute of Management
ERIM Ph.D. Series Research in Management
Erasmus Research Institute of Management

Bannouh, K. (2013, January 11). Measuring and Forecasting Financial Market Volatility using High-Frequency Data (No. EPS-2013-273-F&A). ERIM Ph.D. Series Research in Management. Retrieved from