Stephen Taylor, 2008
An econometric defence of pure-jump price dynamics
First draft December 2008. PDF file.
Abstract: Pure-jump stochastic processes are shown to be capable of explaining many empirical features of high-frequency asset prices. A simple pure-jump process can match the empirical bipower, realized variance and jump detection statistics of Andersen, Bollerslev and Dobrev (2007) at the two-minute frequency. A multi-frequency analysis of Spyder returns shows the theoretical predictions can also be aligned reasonably accurately with the empirical evidence across more than one sampling frequency.
Presented at Beijing University, Lancaster University and at an Alesund workshop and a Humboldt-Copenhagen conference, both on Financial Econometrics.
Stephen Taylor, 2015
Microstructure noise components of the S&P 500 index: variation,
persistence and distributions
First draft (different title) February 2014, latest draft (current title) October 2016. PDF file.
Abstract: By studying the differences between exchange-traded fund prices and
futures prices, new results are obtained about the distribution and persistence
of the microstructure noise component created by bid/ask spreads and discrete
price scales. The bivariate density is estimated from high-frequency prices, to
provide estimates of the probabilities of one-tick bid/ask spreads, marginal
noise densities and measures of dependence across the markets studied.
Properties of the residual microstructure noise, created by factors other than
discrete prices, are also estimated. The residual component has more variation
and less persistence than the discrete-price component during the period
examined, from January 2010 to December 2012.
Presented at the 8th SoFiE conference, in Aarhus, June 2015.
Ingmar Nolte, Stephen Taylor and Vera Zhao, 2016
More accurate volatility estimation and
forecasts using price durations
First draft January 2016,
latest draft March 2018. PDF file.
Abstract: We investigate price duration variance estimators that have long been ignored in the literature. We show i) how price duration estimators can be used for the estimation and forecasting of the integrated variance of an underlying semi-martingale price process and ii) how they are affected by a) important market microstructure noise effects such as the bid/ask spread, irregularly spaced observations in discrete time and discrete price levels, as well as b) price jumps. We develop i) simple-to-construct non-parametric estimators and ii) parametric price duration estimators using autoregressive conditional duration specifications. We provide guidance how these estimators can best be implemented in practice by optimally selecting a threshold parameter that defines a price duration event, or by averaging over a range of non-parametric duration estimators. We provide simulation and forecasting evidence that price duration estimators can extract relevant information from high-frequency data better and produce more accurate forecasts than competing realized volatility and option-implied variance estimators, when considered in isolation or as part of a forecasting combination setting.
Presented at the 9th SoFiE conference, in Hong Kong, June 2016.
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Last updated in April 2018