msperlin
Senior Member
Posts: 608
Joined: Jul 2006

Tue Jun 03, 08 09:46 AM


Quote
Originally posted by: JediWarrior I would like to know if any market practitioner finds interesting the calculation of volatility using tickbytick data. In my experience I find that taking into account estimators that take include extreme values, such as Parkinson´s, GarmanKlass, ... is interesting to estimate real volatility. But I have no idea if using tickbytick data adds up something. By the way, with these high frequancy data you need new estimators, as they are highly biased.
Any ideas on this? Any recommended papers?
Thanks in advance
I've been working with ACD models and one of the applications is to compute high frequency volatility. The idea is still quite simple, based on a a la garch type of formulation (lag dependence and clustering), the new model just changes the unit if time for the volatility (and mean equation) at each time t in order to input irregularly spaced data into the model.
Here some references for you to check out:
ENGLE, R, RUSSEL, J. R. (1998) ‘Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data’ Econometrica, Vol. 66, N. 5, pp 11271162. ENGLE, R. (2000) ‘The Econometrics of UltraHighFrequency Data’ Econometrica, Vol. 68, N. 1, p. 122. GERHARD, F. HAUTSCH, N. (2002) ‘Volatility Estimation on the Basis of Price Intensities’ Journal of Empirical Finance, v. 9, p. 5789.
And a comparison against realized vol is given here:
COEN, A., RACICOT, F. (2004) ‘Integrated Volatility and UHFGarch Models: A Comparison Using High Frequency Financial Data’ Working Paper, available at: http://ssrn.com/abstract=498222
It seems that realized is better.

My personal site with Matlab Code and research papers here
Edited: Tue Jun 03, 08 at 09:51 AM by msperlin

