Measuring and forcasting financial market volatility using high-frequency data

K (Karim) Bannouh

Research output: Types of ThesisDoctoral ThesisInternal

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.
Original languageEnglish
Awarding Institution
  • Erasmus University Rotterdam
Supervisors/Advisors
  • van Dijk, Dick, Supervisor
  • Boswijk, HP, Doctoral committee member, External person
  • McAleer, Doctoral committee member
  • Paap, Richard, Doctoral committee member
  • Martens, Co-supervisor
Award date11 Jan 2013
Place of PublicationRotterdam
Print ISBNs9789058923172
Publication statusPublished - 11 Jan 2013

Research programs

  • EUR ESE 31

Fingerprint

Dive into the research topics of 'Measuring and forcasting financial market volatility using high-frequency data'. Together they form a unique fingerprint.

Cite this