Forecasting and trading high frequency volatility on large indices

Abstract

The present paper analyses the forecastability and tradability of volatility on the large S&P500 index and the liquid SPY ETF, VIX index and VXX ETN. Even though there is already a huge array of literature on forecasting high frequency volatility, most publications only evaluate the forecast in terms of statistical errors. In practice, this kind of analysis is only a minor indication of the actual economic significance of the forecast that has been developed. For this reason, in our approach, we also include a test of our forecast through trading an appropriate volatility derivative. As a method we use parametric and artificial intelligence models. We also combine these models in order to achieve a hybrid forecast. We report that the results of all three model types are of similar quality. However, we observe that artificial intelligence models are able to achieve these results with a shorter input time frame and the errors are uniformly lower comparing with the parametric one. Similarly, the chosen models do not appear to differ much while the analysis of trading efficiency is performed. Finally, we notice that Sharpe ratios tend to improve for longer forecast horizons.

Keywords:

• Forecasting
• Realized volatility
• High-frequency data
• HAR-RV-J
• RNN
• Hybrid model
• Trading efficiency

JEL Classification:

• C53
• G1
• C4

Acknowledgements

We would like to thank participants at the Forecasting Financial Markets (FFM 2016), Quantitative Finance and Risk Analysis (QFRA2016) conferences, and at seminar talks in the University of Liverpool and Leibniz Universität Hannover. Remaining errors are ours.

Notes

No potential conflict of interest was reported by the authors.

1 For determining whether an asset return process has jumps by using high frequency data (see Carr and Wu 2003, Andersen et al. 2003, 2007a, b, 2012, Huang and Tauchen 2005, Barndorff-Nielsen and Shephard 2006, Fan and Wang 2007, Lee and Mykland 2008, Jiang and Oomen 2008, Aït-Sahalia and Jacod 2009, Lee and Hannig 2010, Christensen et al. 2014, Bajgrowicz et al. 2016).

2 In (Hyndman and Koehler (2006), and references therein), there is an interesting discussion and comparison among different measures of accuracy of univariate time series forecasts.

Additional information

Funding

This work was supported by the EPSRC and ESRC Centre for Doctoral Training on Quantification and Management of Risk & Uncertainty in Complex Systems & Environments (EP/L015927/1).