IDEAS home Printed from https://ideas.repec.org/p/cte/wsrepe/25985.html
   My bibliography  Save this paper

Modeling and forecasting the oil volatility index

Author

Listed:
  • Mariti, Massimo B.
  • Gonçalves Mazzeu, Joao Henrique
  • Lopes Moreira da Veiga, María Helena

Abstract

This paper models and forecasts the crude oil ETF volatility index (OVX). Themotivation lies on the evidence that the OVX has been used in the last years as an important alternative measure to track and analyze the volatility of future oil prices. The analysis of the OVX suggests that it presents similar features to those of the daily market volatility index. The main characteristic is the long range dependence that is modeled either by autoregressive fractional integrated moving averaging (ARFIMA) models or by heterogeneous autoregressive (HAR) specifications. Regarding the latter family of models, we first propose extensions of the HAR model that are based on the net and scale measures of oil prices changes. The aim is to improve the HAR model by including predictors that better capture the impact of oil price changes on the economy. Second, we test the forecasting performance of the new proposals and benchmarks with the model confidence set (MCS) and the Generalized-AutoContouR (G-ACR) tests interms of point forecasts and density forecasting, respectively. Our main findings are as follows: the new asymmetric proposals have superior predictive ability than the heterogeneous autoregressive leverage (HARL) model under two known loss functions. Regarding density forecasting, the best model is the one that includes the scale measureas a proxy of oil price changes and considers a flexible distribution for the errors.

Suggested Citation

  • Mariti, Massimo B. & Gonçalves Mazzeu, Joao Henrique & Lopes Moreira da Veiga, María Helena, 2017. "Modeling and forecasting the oil volatility index," DES - Working Papers. Statistics and Econometrics. WS 25985, Universidad Carlos III de Madrid. Departamento de Estadística.
  • Handle: RePEc:cte:wsrepe:25985
    as

    Download full text from publisher

    File URL: https://e-archivo.uc3m.es/rest/api/core/bitstreams/1f56552f-a2f4-49fc-a83c-baf8651829d2/content
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Mauro Bernardi & Leopoldo Catania, 2014. "The Model Confidence Set package for R," Papers 1410.8504, arXiv.org.
    2. Chia-Lin Chang & Michael Mcaleer, 2009. "Daily Tourist Arrivals, Exchange Rates and Voatility for Korea and Taiwan," Korean Economic Review, Korean Economic Association, vol. 25, pages 241-267.
    3. Basher, Syed A. & Sadorsky, Perry, 2006. "Oil price risk and emerging stock markets," Global Finance Journal, Elsevier, vol. 17(2), pages 224-251, December.
    4. Adam Clements & Joanne Fuller, 2012. "Forecasting increases in the VIX: A time-varying long volatility hedge for equities," NCER Working Paper Series 88, National Centre for Econometric Research.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xiao, Jihong & Liu, Hong, 2023. "The time-varying impact of uncertainty on oil market fear: Does climate policy uncertainty matter?," Resources Policy, Elsevier, vol. 82(C).
    2. Shobande Olatunji Abdul & Shodipe Oladimeji Tomiwa, 2020. "Re-Evaluation of World Population Figures: Politics and Forecasting Mechanics," Economics and Business, Sciendo, vol. 34(1), pages 104-125, February.
    3. Izzeldin, Marwan & Muradoğlu, Yaz Gülnur & Pappas, Vasileios & Sivaprasad, Sheeja, 2021. "The impact of Covid-19 on G7 stock markets volatility: Evidence from a ST-HAR model," International Review of Financial Analysis, Elsevier, vol. 74(C).
    4. Olatunji Abdul Shobande & Oladimeji Tomiwa Shodipe, 2021. "Monetary Policy Interdependency in Fisher Effect: A Comparative Evidence," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 10(1), pages 203-226.
    5. Lu Wang & Shan Li & Chao Liang, 2024. "Exploring the impact of oil security attention on oil volatility: A new perspective," International Finance, Wiley Blackwell, vol. 27(1), pages 61-80, April.

    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. Kearney, Fearghal & Shang, Han Lin & Sheenan, Lisa, 2019. "Implied volatility surface predictability: The case of commodity markets," Journal of Banking & Finance, Elsevier, vol. 108(C).
    2. Chang, Chia-Lin, 2015. "Modelling a latent daily Tourism Financial Conditions Index," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 113-126.
    3. Billé, Anna Gloria & Gianfreda, Angelica & Del Grosso, Filippo & Ravazzolo, Francesco, 2023. "Forecasting electricity prices with expert, linear, and nonlinear models," International Journal of Forecasting, Elsevier, vol. 39(2), pages 570-586.
    4. Radosław Puka & Bartosz Łamasz & Marek Michalski, 2021. "Using Artificial Neural Networks to Support the Decision-Making Process of Buying Call Options Considering Risk Appetite," Energies, MDPI, vol. 14(24), pages 1-24, December.
    5. Roman Mestre, 2021. "A wavelet approach of investing behaviors and their effects on risk exposures," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-37, December.
    6. Morema, Kgotso & Bonga-Bonga, Lumengo, 2020. "The impact of oil and gold price fluctuations on the South African equity market: Volatility spillovers and financial policy implications," Resources Policy, Elsevier, vol. 68(C).
    7. Zhang, Hao & Cai, Guixin & Yang, Dongxiao, 2020. "The impact of oil price shocks on clean energy stocks: Fresh evidence from multi-scale perspective," Energy, Elsevier, vol. 196(C).
    8. Jammazi, Rania, 2012. "Oil shock transmission to stock market returns: Wavelet-multivariate Markov switching GARCH approach," Energy, Elsevier, vol. 37(1), pages 430-454.
    9. Arturo Lorenzo Valdés & Rocío Durán Vázquez & Leticia Armenta Fraire, 2012. "Conditional Correlation Between Oil and Stock Market Returns: The Case of Mexico," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 7(1), pages 49-63, Enero-Jun.
    10. Lyócsa, Štefan & Molnár, Peter & Todorova, Neda, 2017. "Volatility forecasting of non-ferrous metal futures: Covariances, covariates or combinations?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 51(C), pages 228-247.
    11. Westerlund, Joakim & Sharma, Susan Sunila, 2019. "Panel evidence on the ability of oil returns to predict stock returns in the G7 area," Energy Economics, Elsevier, vol. 77(C), pages 3-12.
    12. Aynur Pala, 2014. "The Effect of Valuation Ratios, Gold Price, and Petroleum Price on Equity Returns: A Comparison of Static Panel and Quantile Regressions," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 4(1), pages 80-89, January.
    13. Mehmet Balcilar & Rangan Gupta & Christian Pierdzioch, 2022. "Oil-Price Uncertainty and International Stock Returns: Dissecting Quantile-Based Predictability and Spillover Effects Using More than a Century of Data," Energies, MDPI, vol. 15(22), pages 1-26, November.
    14. Ewing, Bradley T. & Malik, Farooq, 2016. "Volatility spillovers between oil prices and the stock market under structural breaks," Global Finance Journal, Elsevier, vol. 29(C), pages 12-23.
    15. Agiomirgianakis, George & Serenis, Dimitrios & Tsounis, Nicholas, 2017. "Effective timing of tourism policy: The case of Singapore," Economic Modelling, Elsevier, vol. 60(C), pages 29-38.
    16. Radosław Puka & Bartosz Łamasz & Marek Michalski, 2021. "Effectiveness of Artificial Neural Networks in Hedging against WTI Crude Oil Price Risk," Energies, MDPI, vol. 14(11), pages 1-26, June.
    17. Khaled Bataineh, 2024. "Crude Oil Prices and the Egyptian Economy Evidence from the Stock Market," International Journal of Energy Economics and Policy, Econjournals, vol. 14(1), pages 383-392, January.
    18. Khan, Muhammad Imran & Teng, Jian-Zhou & Khan, Muhammad Kamran & Jadoon, Arshad Ullah & Khan, Muhammad Fayaz, 2021. "The impact of oil prices on stock market development in Pakistan: Evidence with a novel dynamic simulated ARDL approach," Resources Policy, Elsevier, vol. 70(C).
    19. Hammami Algia & Bouri Abdelfatteh, 2018. "The Conditional Relationship between Oil Price Risk and Return Stock Market: a Comparative Study of Advanced and Emerging Countries," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 9(4), pages 1321-1347, December.
    20. Dutta, Anupam & Bouri, Elie & Rothovius, Timo & Azoury, Nehme & Uddin, Gazi Salah, 2024. "Does oil price volatility matter for the US transportation industry?," Energy, Elsevier, vol. 290(C).

    More about this item

    Keywords

    Heterogeneous autoregression;

    JEL classification:

    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    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:cte:wsrepe:25985. 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: Ana Poveda (email available below). General contact details of provider: http://portal.uc3m.es/portal/page/portal/dpto_estadistica .

    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.