IDEAS home Printed from https://ideas.repec.org/p/pre/wpaper/202009.html
   My bibliography  Save this paper

Investor Happiness and Predictability of the Realized Volatility of Oil Price

Author

Listed:
  • Matteo Bonato

    (Department of Economics and Econometrics, University of Johannesburg, Auckland Park, South Africa and IPAG Business School, 184 Boulevard Saint-Germain, 75006 Paris, France)

  • Konstantinos Gkillas

    (Department of Business Administration, University of Patras – University Campus, Rio, P.O. Box 1391, 26500 Patras, Greece)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa)

  • Christian Pierdzioch

    (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany)

Abstract

We use the heterogeneous autoregressive realized volatility (HAR-RV) model to analyze both in sample and out-of-sample whether a measure of investor happiness predicts the daily realized volatility of oil-price returns, where we use high-frequency intradaily data to measure realized volatility. Full-sample estimates reveal that realized volatility is significantly negatively linked to investor happiness at a short forecast horizon. Similarly, out-of-sample results indicate that investor happiness significantly improves accuracy of forecasts of realized volatility at a short forecast horizon. Results for a medium and a long forecast horizon are insignificant. We argue that our results shed light on the role played by speculation in oil products and the potential function of oil-related products as a hedge against risks in traditional financial assets.

Suggested Citation

  • Matteo Bonato & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2020. "Investor Happiness and Predictability of the Realized Volatility of Oil Price," Working Papers 202009, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:202009
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Olson, Eric & Vivian, Andrew & Wohar, Mark E., 2019. "What is a better cross-hedge for energy: Equities or other commodities?," Global Finance Journal, Elsevier, vol. 42(C).
    2. Liu, Jing & Ma, Feng & Yang, Ke & Zhang, Yaojie, 2018. "Forecasting the oil futures price volatility: Large jumps and small jumps," Energy Economics, Elsevier, vol. 72(C), pages 321-330.
    3. Zhang, Wei & Wang, Pengfei & Li, Xiao & Shen, Dehua, 2018. "Twitter’s daily happiness sentiment and international stock returns: Evidence from linear and nonlinear causality tests," Journal of Behavioral and Experimental Finance, Elsevier, vol. 18(C), pages 50-53.
    4. Deven Bathia & Don Bredin & Dirk Nitzsche, 2016. "International Sentiment Spillovers in Equity Returns," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 21(4), pages 332-359, October.
    5. Ji, Qiang & Guo, Jian-Feng, 2015. "Oil price volatility and oil-related events: An Internet concern study perspective," Applied Energy, Elsevier, vol. 137(C), pages 256-264.
    6. Ole E. Barndorff-Nielsen & Neil Shephard, 2006. "Econometrics of Testing for Jumps in Financial Economics Using Bipower Variation," The Journal of Financial Econometrics, Society for Financial Econometrics, vol. 4(1), pages 1-30.
    7. Hong, Harrison & Yogo, Motohiro, 2012. "What does futures market interest tell us about the macroeconomy and asset prices?," Journal of Financial Economics, Elsevier, vol. 105(3), pages 473-490.
    8. Phan, Dinh Hoang Bach & Sharma, Susan Sunila & Narayan, Paresh Kumar, 2016. "Intraday volatility interaction between the crude oil and equity markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 40(C), pages 1-13.
    9. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    10. 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.
    11. Demirer, Riza & Gupta, Rangan & Suleman, Tahir & Wohar, Mark E., 2018. "Time-varying rare disaster risks, oil returns and volatility," Energy Economics, Elsevier, vol. 75(C), pages 239-248.
    12. Bollerslev, Tim & Ghysels, Eric, 1996. "Periodic Autoregressive Conditional Heteroscedasticity," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(2), pages 139-151, April.
    13. Malcolm Baker & Jeffrey Wurgler, 2006. "Investor Sentiment and the Cross‐Section of Stock Returns," Journal of Finance, American Finance Association, vol. 61(4), pages 1645-1680, August.
    14. Michael McAleer & Marcelo Medeiros, 2008. "Realized Volatility: A Review," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 10-45.
    15. Qadan, Mahmoud & Nama, Hazar, 2018. "Investor sentiment and the price of oil," Energy Economics, Elsevier, vol. 69(C), pages 42-58.
    16. Zhi Da & Joseph Engelberg & Pengjie Gao, 2015. "Editor's Choice The Sum of All FEARS Investor Sentiment and Asset Prices," Review of Financial Studies, Society for Financial Studies, vol. 28(1), pages 1-32.
    17. Andersen, Torben G. & Dobrev, Dobrislav & Schaumburg, Ernst, 2012. "Jump-robust volatility estimation using nearest neighbor truncation," Journal of Econometrics, Elsevier, vol. 169(1), pages 75-93.
    18. Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2020. "Forecasting realized oil-price volatility: The role of financial stress and asymmetric loss," Journal of International Money and Finance, Elsevier, vol. 104(C).
    19. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    20. Olson, Eric & Vivian, Andrew & Wohar, Mark E., 2017. "Do commodities make effective hedges for equity investors?," Research in International Business and Finance, Elsevier, vol. 42(C), pages 1274-1288.
    21. Manabu Asai & Rangan Gupta & Michael McAleer, 2019. "The Impact of Jumps and Leverage in Forecasting the Co-Volatility of Oil and Gold Futures," Energies, MDPI, vol. 12(17), pages 1-17, September.
    22. Degiannakis, Stavros & Filis, George, 2017. "Forecasting oil price realized volatility using information channels from other asset classes," Journal of International Money and Finance, Elsevier, vol. 76(C), pages 28-49.
    23. Bahloul, Walid & Balcilar, Mehmet & Cunado, Juncal & Gupta, Rangan, 2018. "The role of economic and financial uncertainties in predicting commodity futures returns and volatility: Evidence from a nonparametric causality-in-quantiles test," Journal of Multinational Financial Management, Elsevier, vol. 45(C), pages 52-71.
    24. John Y. Campbell, 2008. "Viewpoint: Estimating the equity premium," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 41(1), pages 1-21, February.
    25. Andersen, Torben G. & Bollerslev, Tim & Huang, Xin, 2011. "A reduced form framework for modeling volatility of speculative prices based on realized variation measures," Journal of Econometrics, Elsevier, vol. 160(1), pages 176-189, January.
    26. Deeney, Peter & Cummins, Mark & Dowling, Michael & Bermingham, Adam, 2015. "Sentiment in oil markets," International Review of Financial Analysis, Elsevier, vol. 39(C), pages 179-185.
    27. Liu, Lily Y. & Patton, Andrew J. & Sheppard, Kevin, 2015. "Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes," Journal of Econometrics, Elsevier, vol. 187(1), pages 293-311.
    28. Amaya, Diego & Christoffersen, Peter & Jacobs, Kris & Vasquez, Aurelio, 2015. "Does realized skewness predict the cross-section of equity returns?," Journal of Financial Economics, Elsevier, vol. 118(1), pages 135-167.
    29. Guo, Jian-Feng & Ji, Qiang, 2013. "How does market concern derived from the Internet affect oil prices?," Applied Energy, Elsevier, vol. 112(C), pages 1536-1543.
    30. Malcolm Baker & Jeffrey Wurgler, 2007. "Investor Sentiment in the Stock Market," Journal of Economic Perspectives, American Economic Association, vol. 21(2), pages 129-152, Spring.
    31. Muller, Ulrich A. & Dacorogna, Michel M. & Dave, Rakhal D. & Olsen, Richard B. & Pictet, Olivier V. & von Weizsacker, Jacob E., 1997. "Volatilities of different time resolutions -- Analyzing the dynamics of market components," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 213-239, June.
    32. Konstantinos Gkillas & Rangan Gupta & Mark E. Wohar, 2020. "Oil shocks and volatility jumps," Review of Quantitative Finance and Accounting, Springer, vol. 54(1), pages 247-272, January.
    33. Zhang, Wei & Li, Xiao & Shen, Dehua & Teglio, Andrea, 2016. "Daily happiness and stock returns: Some international evidence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 460(C), pages 201-209.
    34. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," The Journal of Financial Econometrics, Society for Financial Econometrics, vol. 7(2), pages 174-196, Spring.
    35. John Y. Campbell, 2007. "Estimating the Equity Premium," NBER Working Papers 13423, National Bureau of Economic Research, Inc.
    36. Asai, Manabu & Gupta, Rangan & McAleer, Michael, 2020. "Forecasting volatility and co-volatility of crude oil and gold futures: Effects of leverage, jumps, spillovers, and geopolitical risks," International Journal of Forecasting, Elsevier, vol. 36(3), pages 933-948.
    37. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    38. John Elder & Apostolos Serletis, 2010. "Oil Price Uncertainty," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(6), pages 1137-1159, September.
    39. Mei, Dexiang & Liu, Jing & Ma, Feng & Chen, Wang, 2017. "Forecasting stock market volatility: Do realized skewness and kurtosis help?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 481(C), pages 153-159.
    40. You, Wanhai & Guo, Yawei & Peng, Cheng, 2017. "Twitter's daily happiness sentiment and the predictability of stock returns," Finance Research Letters, Elsevier, vol. 23(C), pages 58-64.
    41. Zhiguang (Gerald) Wang, 2009. "Volatility Risk," Issue Briefs 2009513, South Dakota State University, Department of Economics.
    42. Deven Bathia & Don Bredin, 2013. "An examination of investor sentiment effect on G7 stock market returns," The European Journal of Finance, Taylor & Francis Journals, vol. 19(9), pages 909-937, October.
    43. Ole E. Barndorff-Nielsen, 2004. "Power and Bipower Variation with Stochastic Volatility and Jumps," The Journal of Financial Econometrics, Society for Financial Econometrics, vol. 2(1), pages 1-37.
    44. Lu-Tao Zhao & Li-Na Liu & Zi-Jie Wang & Ling-Yun He, 2019. "Forecasting Oil Price Volatility in the Era of Big Data: A Text Mining for VaR Approach," Sustainability, MDPI, vol. 11(14), pages 1-20, July.
    45. Yue-Jun Zhang & Shu-Hui Li, 2019. "The impact of investor sentiment on crude oil market risks: evidence from the wavelet approach," Quantitative Finance, Taylor & Francis Journals, vol. 19(8), pages 1357-1371, August.
    46. Arjun Chatrath & Hong Miao & Sanjay Ramchander & Tianyang Wang, 2015. "The Forecasting Efficacy of Risk‐Neutral Moments for Crude Oil Volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(3), pages 177-190, April.
    47. Sévi, Benoît, 2014. "Forecasting the volatility of crude oil futures using intraday data," European Journal of Operational Research, Elsevier, vol. 235(3), pages 643-659.
    48. Kenneth J. Singleton, 2014. "Investor Flows and the 2008 Boom/Bust in Oil Prices," Management Science, INFORMS, vol. 60(2), pages 300-318, February.
    49. Bonato, Matteo, 2019. "Realized correlations, betas and volatility spillover in the agricultural commodity market: What has changed?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 62(C), pages 184-202.
    50. Raffaella Giacomini & Barbara Rossi, 2010. "Forecast comparisons in unstable environments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 595-620.
    51. Marcel Prokopczuk & Lazaros Symeonidis & Chardin Wese Simen, 2016. "Do Jumps Matter for Volatility Forecasting? Evidence from Energy Markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 36(8), pages 758-792, August.
    52. Lux, Thomas & Segnon, Mawuli & Gupta, Rangan, 2016. "Forecasting crude oil price volatility and value-at-risk: Evidence from historical and recent data," Energy Economics, Elsevier, vol. 56(C), pages 117-133.
    53. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    54. Henriques, Irene & Sadorsky, Perry, 2010. "Can environmental sustainability be used to manage energy price risk?," Energy Economics, Elsevier, vol. 32(5), pages 1131-1138, September.
    55. Yong Jiang & Chao-Qun Ma & Xiao-Guang Yang & Yi-Shuai Ren, 2018. "Time-Varying Volatility Feedback of Energy Prices: Evidence from Crude Oil, Petroleum Products, and Natural Gas Using a TVP-SVM Model," Sustainability, MDPI, vol. 10(12), pages 1-17, December.
    56. Chen, Yixiang & Ma, Feng & Zhang, Yaojie, 2019. "Good, bad cojumps and volatility forecasting: New evidence from crude oil and the U.S. stock markets," Energy Economics, Elsevier, vol. 81(C), pages 52-62.
    57. Reboredo, Juan C. & Ugolini, Andrea, 2018. "The impact of Twitter sentiment on renewable energy stocks," Energy Economics, Elsevier, vol. 76(C), pages 153-169.
    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. Sapkota, Niranjan, 2022. "News-based sentiment and bitcoin volatility," International Review of Financial Analysis, Elsevier, vol. 82(C).
    2. Rangan Gupta & Christian Pierdzioch, 2021. "Climate Risks and the Realized Volatility Oil and Gas Prices: Results of an Out-of-Sample Forecasting Experiment," Energies, MDPI, vol. 14(23), pages 1-18, December.
    3. Afees A. Salisu & Rangan Gupta & Elie Bouri & Qiang Ji, 2020. "Forecasting Oil Volatility Using a GARCH-MIDAS Approach: The Role of Global Economic Conditions," Working Papers 202051, University of Pretoria, Department of Economics.
    4. Salisu, Afees A. & Gupta, Rangan & Demirer, Riza, 2022. "Global financial cycle and the predictability of oil market volatility: Evidence from a GARCH-MIDAS model," Energy Economics, Elsevier, vol. 108(C).
    5. Sheng, Xin & Kim, Won Joong & Gupta, Rangan & Ji, Qiang, 2023. "The impacts of oil price volatility on financial stress: Is the COVID-19 period different?," International Review of Economics & Finance, Elsevier, vol. 85(C), pages 520-532.
    6. Sami Ben Jabeur & Rabeh Khalfaoui & Wissal Ben Arfi, 2021. "The effect of green energy, global environmental indexes, and stock markets in predicting oil price crashes: Evidence from explainable machine learning," Post-Print hal-03797577, HAL.
    7. Demirer, Riza & Gupta, Rangan & Pierdzioch, Christian & Shahzad, Syed Jawad Hussain, 2020. "The predictive power of oil price shocks on realized volatility of oil: A note," Resources Policy, Elsevier, vol. 69(C).
    8. Luo, Jiawen & Demirer, Riza & Gupta, Rangan & Ji, Qiang, 2022. "Forecasting oil and gold volatilities with sentiment indicators under structural breaks," Energy Economics, Elsevier, vol. 105(C).
    9. Yaojie Zhang & Mengxi He & Danyan Wen & Yudong Wang, 2022. "Forecasting Bitcoin volatility: A new insight from the threshold regression model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 633-652, April.
    10. Yan, Juan & Haroon, Muhammad, 2023. "Financing efficiency in natural resource markets mobilizing private and public capital for a green recovery," Resources Policy, Elsevier, vol. 85(PB).
    11. Rangan Gupta & Christian Pierdzioch, 2023. "Do U.S. economic conditions at the state level predict the realized volatility of oil-price returns? A quantile machine-learning approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-22, December.
    12. Guliyev, Hasraddin & Mustafayev, Eldayag, 2022. "Predicting the changes in the WTI crude oil price dynamics using machine learning models," Resources Policy, Elsevier, vol. 77(C).
    13. Afees A. Salisu & Rangan Gupta & Elie Bouri & Qiang Ji, 2022. "Mixed‐frequency forecasting of crude oil volatility based on the information content of global economic conditions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 134-157, January.
    14. Kuang, Wei, 2022. "The economic value of high-frequency data in equity-oil hedge," Energy, Elsevier, vol. 239(PA).
    15. Rangan Gupta & Christian Pierdzioch, 2021. "Forecasting the Volatility of Crude Oil: The Role of Uncertainty and Spillovers," Energies, MDPI, vol. 14(14), pages 1-15, July.
    16. Çepni, Oğuzhan & Gupta, Rangan & Pienaar, Daniel & Pierdzioch, Christian, 2022. "Forecasting the realized variance of oil-price returns using machine learning: Is there a role for U.S. state-level uncertainty?," Energy Economics, Elsevier, vol. 114(C).
    17. Elie Bouri & Riza Demirer & Rangan Gupta & Christian Pierdzioch, 2020. "Infectious Diseases, Market Uncertainty and Oil Market Volatility," Energies, MDPI, vol. 13(16), pages 1-8, August.

    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. Riza Demirer & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2022. "Risk aversion and the predictability of crude oil market volatility: A forecasting experiment with random forests," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 73(8), pages 1755-1767, August.
    2. Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2020. "Forecasting realized oil-price volatility: The role of financial stress and asymmetric loss," Journal of International Money and Finance, Elsevier, vol. 104(C).
    3. Bonato, Matteo & Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2021. "A note on investor happiness and the predictability of realized volatility of gold," Finance Research Letters, Elsevier, vol. 39(C).
    4. Demirer, Riza & Gupta, Rangan & Pierdzioch, Christian & Shahzad, Syed Jawad Hussain, 2020. "The predictive power of oil price shocks on realized volatility of oil: A note," Resources Policy, Elsevier, vol. 69(C).
    5. Matteo Bonato & Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2024. "Business applications and state‐level stock market realized volatility: A forecasting experiment," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 456-472, March.
    6. Luo, Jiawen & Demirer, Riza & Gupta, Rangan & Ji, Qiang, 2022. "Forecasting oil and gold volatilities with sentiment indicators under structural breaks," Energy Economics, Elsevier, vol. 105(C).
    7. Elie Bouri & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2021. "Forecasting Realized Volatility of Bitcoin: The Role of the Trade War," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 29-53, January.
    8. Bonato, Matteo & Gupta, Rangan & Lau, Chi Keung Marco & Wang, Shixuan, 2020. "Moments-based spillovers across gold and oil markets," Energy Economics, Elsevier, vol. 89(C).
    9. Demirer, Riza & Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2019. "Time-varying risk aversion and realized gold volatility," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    10. Degiannakis, Stavros & Filis, George, 2022. "Oil price volatility forecasts: What do investors need to know?," Journal of International Money and Finance, Elsevier, vol. 123(C).
    11. Chen, Yixiang & Ma, Feng & Zhang, Yaojie, 2019. "Good, bad cojumps and volatility forecasting: New evidence from crude oil and the U.S. stock markets," Energy Economics, Elsevier, vol. 81(C), pages 52-62.
    12. Rangan Gupta & Christian Pierdzioch, 2021. "Climate Risks and the Realized Volatility Oil and Gas Prices: Results of an Out-of-Sample Forecasting Experiment," Energies, MDPI, vol. 14(23), pages 1-18, December.
    13. Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian & Yoon, Seong-Min, 2021. "OPEC news and jumps in the oil market," Energy Economics, Elsevier, vol. 96(C).
    14. 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.
    15. Yang, Cai & Gong, Xu & Zhang, Hongwei, 2019. "Volatility forecasting of crude oil futures: The role of investor sentiment and leverage effect," Resources Policy, Elsevier, vol. 61(C), pages 548-563.
    16. Bu, Ruijun & Hizmeri, Rodrigo & Izzeldin, Marwan & Murphy, Anthony & Tsionas, Mike, 2023. "The contribution of jump signs and activity to forecasting stock price volatility," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 144-164.
    17. Asai, Manabu & Gupta, Rangan & McAleer, Michael, 2020. "Forecasting volatility and co-volatility of crude oil and gold futures: Effects of leverage, jumps, spillovers, and geopolitical risks," International Journal of Forecasting, Elsevier, vol. 36(3), pages 933-948.
    18. Rangan Gupta & Jacobus Nel & Christian Pierdzioch, 2023. "Investor Confidence and Forecastability of US Stock Market Realized Volatility: Evidence from Machine Learning," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 24(1), pages 111-122, January.
    19. Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2018. "Forecasting (Good and Bad) Realized Exchange-Rate Volatility: Is there a Role for Realized Skewness and Kurtosis?," Working Papers 201879, University of Pretoria, Department of Economics.
    20. Elie Bouri & Riza Demirer & Rangan Gupta & Christian Pierdzioch, 2020. "Infectious Diseases, Market Uncertainty and Oil Market Volatility," Energies, MDPI, vol. 13(16), pages 1-8, August.

    More about this item

    Keywords

    Investor Happiness; Oil market; Realized Volatility; Forecasting;
    All these keywords.

    JEL classification:

    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market

    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:pre:wpaper:202009. 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: Rangan Gupta (email available below). General contact details of provider: https://edirc.repec.org/data/decupza.html .

    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.