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Can you reccomend model for high frequency data (1 second and less) (returns and volatility forecasting)? Most papers use ARMA, GARCH etc in 1 minute and lower time frame. PROBLEM ARMA does not know nothing about order imbalance and order flow correlation so i looking for model which can combine order book and time series forecasting model. Efficient estimation of volatility Zumbach 2002

Order Book Slope and Price Volatility , Petko Kalev EGARCH aproach. Anybody used it ?Or know something better? What do you use?

I know there are a lot or papers and question already about volatility/But just want to know expert opinion what to choose for high frequency?

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  • $\begingroup$ An apparently promising class of models are : HEAVY models : high frequency based volatility models. see Realising the future: forecasting with high frequency based volatility (HEAVY) models by Neil and Kevin Shephard $\endgroup$
    – Malick
    Commented Nov 28, 2015 at 13:19

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HF data have a lot of auto correlation so common models to deal with this problems are ARFIMA, FIGARCH, Fractional Integrated GARCH. Engle recently propose the multiplicative components GARCH for high frequency data, which can include a mean model like and ARMA. In this post they explain how to implement it in R with the rugarch package, it takes some time to run but it works. http://unstarched.net/2013/03/20/high-frequency-garch-the-multiplicative-component-garch-mcsgarch-model/#comment-8309

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