Spring Seminar Series - March 9, 2006
University of Minnesota
School of Statistics
College of Liberal Arts

Multi-scale Jump and Volatility Analysis for High-Frequency Financial Data

Yazhen Wang
Department of Statistics
University of Connecticut

Thursday, March 9, 2006
3:30 PM, 115 Ford Hall
Minneapolis, East Bank Campus
Social at 3:00 PM, 300 Ford Hall

Abstract

Asset prices often contain jumps, and high-frequency financial data are inevitably contaminated with market microstructure noise. Existing methods can deal with noisy data for the continuous diffusion price model or handle the jump-diffusion price model without noise. In this talk I will present estimation of integrated volatility and jump variation for noisy high-frequency financial data with jumps. The proposed wavelet based multi-scale methodology can cope with both jumps in the price and market microstructure noise in the data, and estimate both integrated volatility and jump variation from the noisy data. We establish convergence rates for the proposed estimators of integrated volatility and jump variation. In particular, we show that the integrated volatility can be estimated asymptotically under the jump-diffusion price model as well as under the continuous diffusion price model. Simulations are conducted to assess the performance of the proposed estimators and to compare them with existing ones. Theoretical and numerical analysis show that the proposed estimators outperform existing methods for noisy high-frequency data under the jump-diffusion model, and have comparable performance for the continuous diffusion model and noiseless jump-diffusion model. The methods are illustrated by applications to two high-frequency exchange rate data sets.

This is a joint work with Jianqing Fan.