On-line Spot Volatility-Estimation and Decomposition with Nonlinear Market Microstructure Noise Models
Abstract
A technique for on-line estimation of spot volatility for high-frequency data is developed. The algorithm works directly on the transaction data and updates the volatility estimate immediately after the occurrence of a new transaction. Furthermore, a nonlinear market microstructure noise model is proposed that reproduces several stylized facts of high-frequency data. A computationally efficient particle filter is used that allows for the approximation of the unknown efficient prices and, in combination with a recursive EM algorithm, for the estimation of the volatility curve. We neither assume that the transaction times are equidistant nor do we use interpolated prices. We also make a distinction between volatility per time unit and volatility per transaction and provide estimators for both. More precisely we use a model with random time change where spot volatility is decomposed into spot volatility per transaction times the trading intensity - thus highlighting the influence of trading intensity on volatility.
Cite
@article{arxiv.1006.1860,
title = {On-line Spot Volatility-Estimation and Decomposition with Nonlinear Market Microstructure Noise Models},
author = {Rainer Dahlhaus and Jan C. Neddermeyer},
journal= {arXiv preprint arXiv:1006.1860},
year = {2013}
}
Comments
10 figures