Skip to content

Research at St Andrews

Modelling and trading the U.S. implied volatility indices. Evidence from the VIX, VXN and VXD indices

Research output: Contribution to journalArticle

Open Access permissions

Open

Links

Author(s)

Ioannis Psaradellis, Georgios Sermpinis

School/Research organisations

Abstract

This paper concentrates on the modelling and trading of three daily market implied volatility indices issued on the Chicago Board Options Exchange (CBOE) using evolving combinations of prominent autoregressive and emerging heuristics models, with the aims of introducing an algorithm that provides a better approximation of the most popular U.S. volatility indices than those that have already been presented in the literature and determining whether there is the ability to produce profitable trading strategies. A heterogeneous autoregressive process (HAR) is combined with a genetic algorithm–support vector regression (GASVR) model in two hybrid algorithms. The algorithms’ statistical performances are benchmarked against the best forecasters on the VIX, VXN and VXD volatility indices. The trading performances of the forecasts are evaluated through a trading simulation based on VIX and VXN futures contracts, as well as on the VXZ exchange traded note based on the S&P 500 VIX mid-term futures index. Our findings indicate the existence of strong nonlinearities in all indices examined, while the GASVR algorithm improves the statistical significance of the HAR processes. The trading performances of the hybrid models reveal the possibility of economically significant profits.
Close

Details

Original languageEnglish
Pages (from-to)1268-1283
JournalInternational Journal of Forecasting
Volume32
Issue number4
Early online date17 Aug 2016
DOIs
Publication statusPublished - Oct 2016

    Research areas

  • Implied volatility indices, Heterogeneous autoregression, Heuristics, Volatility derivatives, Exchange traded notes

Discover related content
Find related publications, people, projects and more using interactive charts.

View graph of relations

Related by author

  1. Performance of technical trading rules: evidence from the crude oil market

    Psaradellis, I., Laws, J., Pantelous, A. & Sermpinis, G., 22 Nov 2019, In : European Journal of Finance. 25, 17, p. 1793-1815 23 p.

    Research output: Contribution to journalArticle

  2. Pairs Trading, Technical Analysis and Data Snooping: Mean Reversion vs. Momentum

    Psaradellis, I., Laws, J., Pantelous, A. & Sermpinis, G., 1 Jun 2018, J.P. Morgan Center for Commodities, University of Colorado, Denver Business School. (Global Commodities Applied Research Digest ; vol. 3, no. 1)

    Research output: Book/ReportCommissioned report

  3. Krill-Herd support vector regression and heterogeneous autoregressive leverage: evidence from forecasting and trading commodities

    Stasinakis, C., Sermpinis, G., Psaradellis, I. & Verousis, T., 2016, In : Quantitative Finance. 16, 12

    Research output: Contribution to journalArticle

Related by journal

  1. Non-Linear Predictability in Stock and Bond Returns: When and Where Is It Exploitable?

    McMillan, D. G., Guidolin, M., Hyde, S. & Ono, S., 2009, In : International Journal of Forecasting. 25, p. 373-399

    Research output: Contribution to journalArticle

  2. Non-Linear Forecasting FTSE Returns: Does Volume Help?

    McMillan, D. G., 2007, In : International Journal of Forecasting. 23, p. 115-126 12 p.

    Research output: Contribution to journalArticle

ID: 251506846

Top