Forecasting and trading high frequency volatility on large indices

Fei Liu, Athanasios A. Pantelous, Hans Jörg von Mettenheim

Research output: Contribution to journalArticleResearchpeer-review

3 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)737-748
Number of pages12
JournalQuantitative Finance
Volume18
Issue number5
DOIs
Publication statusPublished - 1 May 2018
Externally publishedYes

Keywords

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

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