IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2408.05659.html
   My bibliography  Save this paper

A GCN-LSTM Approach for ES-mini and VX Futures Forecasting

Author

Listed:
  • Nikolas Michael
  • Mihai Cucuringu
  • Sam Howison

Abstract

We propose a novel data-driven network framework for forecasting problems related to E-mini S\&P 500 and CBOE Volatility Index futures, in which products with different expirations act as distinct nodes. We provide visual demonstrations of the correlation structures of these products in terms of their returns, realized volatility, and trading volume. The resulting networks offer insights into the contemporaneous movements across the different products, illustrating how inherently connected the movements of the future products belonging to these two classes are. These networks are further utilized by a multi-channel Graph Convolutional Network to enhance the predictive power of a Long Short-Term Memory network, allowing for the propagation of forecasts of highly correlated quantities, combining the temporal with the spatial aspect of the term structure.

Suggested Citation

  • Nikolas Michael & Mihai Cucuringu & Sam Howison, 2024. "A GCN-LSTM Approach for ES-mini and VX Futures Forecasting," Papers 2408.05659, arXiv.org.
    • Handle: RePEc:arx:papers:2408.05659
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2408.05659
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chen, Yu-Lun & Tsai, Wei-Che, 2017. "Determinants of price discovery in the VIX futures market," Journal of Empirical Finance, Elsevier, vol. 43(C), pages 59-73.
    2. Bujar Huskaj & Marcus Nossman, 2013. "A Term Structure Model for VIX Futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 33(5), pages 421-442, May.
    3. Milena Vuletić & Felix Prenzel & Mihai Cucuringu, 2024. "Fin-GAN: forecasting and classifying financial time series via generative adversarial networks," Quantitative Finance, Taylor & Francis Journals, vol. 24(2), pages 175-199, January.
    4. Chao Zhang & Xingyue Pu & Mihai Cucuringu & Xiaowen Dong, 2023. "Graph Neural Networks for Forecasting Multivariate Realized Volatility with Spillover Effects," Papers 2308.01419, arXiv.org.
    5. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    6. Bangsgaard, Christine & Kokholm, Thomas, 2024. "The lead–lag relation between VIX futures and SPX futures," Journal of Financial Markets, Elsevier, vol. 67(C).
    7. Taylor, Nick, 2019. "Forecasting returns in the VIX futures market," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1193-1210.
    8. Clark, Peter K, 1973. "A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices," Econometrica, Econometric Society, vol. 41(1), pages 135-155, January.
    9. Nikolas Michael & Mihai Cucuringu & Sam Howison, 2022. "Option Volume Imbalance as a predictor for equity market returns," Papers 2201.09319, arXiv.org.
    10. Michael O'Neill & Gulasekaran Rajaguru, 2023. "Causality of price movements in VIX exchange-traded products and VIX futures contracts," Journal of Accounting Literature, Emerald Group Publishing Limited, vol. 46(2), pages 153-169, April.
    11. Zhongjin Lu & Yingzi Zhu, 2010. "Volatility components: The term structure dynamics of VIX futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 30(3), pages 230-256, March.
    12. Joerg Osterrieder & Daniel Kucharczyk & Silas Rudolf & Daniel Wittwer, 2020. "Neural networks and arbitrage in the VIX," Digital Finance, Springer, vol. 2(1), pages 97-115, September.
    13. Mougoue, Mbodja & Whyte, Ann Marie, 1996. "Stock returns and volatility: An empirical investigation of the German and French equity markets," Global Finance Journal, Elsevier, vol. 7(2), pages 253-263.
    14. Kurov, Alexander & Lasser, Dennis J., 2004. "Price Dynamics in the Regular and E-Mini Futures Markets," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 39(2), pages 365-384, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sebastian A. Gehricke & Jin E. Zhang, 2020. "Modeling VXX under jump diffusion with stochastic long‐term mean," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(10), pages 1508-1534, October.
    2. Sattarhoff, Cristina & Lux, Thomas, 2023. "Forecasting the variability of stock index returns with the multifractal random walk model for realized volatilities," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1678-1697.
    3. Julien Chevallier & Benoît Sévi, 2011. "On the realized volatility of the ECX CO 2 emissions 2008 futures contract: distribution, dynamics and forecasting," Annals of Finance, Springer, vol. 7(1), pages 1-29, February.
    4. Min Liu & Chien‐Chiang Lee & Wei‐Chong Choo, 2021. "An empirical study on the role of trading volume and data frequency in volatility forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 792-816, August.
    5. Torben G. Andersen & Luca Benzoni, 2008. "Realized volatility," Working Paper Series WP-08-14, Federal Reserve Bank of Chicago.
    6. Maki, Daiki, 2024. "Forecasting downside and upside realized volatility: The role of asymmetric information," The Journal of Economic Asymmetries, Elsevier, vol. 29(C).
    7. Ding, Y., 2021. "Conditional Heteroskedasticity in the Volatility of Asset Returns," Janeway Institute Working Papers 2111, Faculty of Economics, University of Cambridge.
    8. Shelton Peiris & Manabu Asai & Michael McAleer, 2017. "Estimating and Forecasting Generalized Fractional Long Memory Stochastic Volatility Models," JRFM, MDPI, vol. 10(4), pages 1-16, December.
    9. Clements, A.E. & Liao, Y., 2020. "Firm-specific information and systemic risk," Economic Modelling, Elsevier, vol. 90(C), pages 480-493.
    10. Min Liu & Wei‐Chong Choo & Chi‐Chuan Lee & Chien‐Chiang Lee, 2023. "Trading volume and realized volatility forecasting: Evidence from the China stock market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 76-100, January.
    11. repec:lan:wpaper:3324 is not listed on IDEAS
    12. Wenbo Ge & Pooia Lalbakhsh & Leigh Isai & Artem Lensky & Hanna Suominen, 2023. "Comparing Deep Learning Models for the Task of Volatility Prediction Using Multivariate Data," Papers 2306.12446, arXiv.org, revised Jun 2023.
    13. Mao, Xiuping & Ruiz, Esther & Veiga, Helena, 2017. "Threshold stochastic volatility: Properties and forecasting," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1105-1123.
    14. Fuertes, Ana-Maria & Izzeldin, Marwan & Kalotychou, Elena, 2009. "On forecasting daily stock volatility: The role of intraday information and market conditions," International Journal of Forecasting, Elsevier, vol. 25(2), pages 259-281.
    15. repec:lan:wpaper:592830 is not listed on IDEAS
    16. An-Sing Chen & Hui-Jyuan Gao & Mark Leung, 2008. "Is Trading Imbalance a Better Explanatory Factor in the Volatility Process? Intraday and Daily Evidence from E-mini S&P 500 Index Futures and Information-Based Hypotheses," Working Papers 0039, College of Business, University of Texas at San Antonio.
    17. Taylor, Nicholas, 2008. "Can idiosyncratic volatility help forecast stock market volatility?," International Journal of Forecasting, Elsevier, vol. 24(3), pages 462-479.
    18. Haugom, Erik & Langeland, Henrik & Molnár, Peter & Westgaard, Sjur, 2014. "Forecasting volatility of the U.S. oil market," Journal of Banking & Finance, Elsevier, vol. 47(C), pages 1-14.
    19. Leopoldo Catania & Roberto Di Mari & Paolo Santucci de Magistris, 2019. "Dynamic discrete mixtures for high frequency prices," Discussion Papers 19/05, University of Nottingham, Granger Centre for Time Series Econometrics.
    20. Alexander Kurov, 2008. "Information And Noise In Financial Markets: Evidence From The E‐Mini Index Futures," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 31(3), pages 247-270, September.
    21. Ezzat, Hassan & Kirkulak, Berna, 2014. "Information Arrival and Volatility: Evidence from the Saudi Arabia Stock Exchange (Tadawul)," MPRA Paper 61160, University Library of Munich, Germany.
    22. Haukvik, Nicole & Cheraghali, Hamid & Molnár, Peter, 2024. "The role of investors’ fear in crude oil volatility forecasting," Research in International Business and Finance, Elsevier, vol. 70(PB).

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2408.05659. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.