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The tail dependence structure between investor sentiment and commodity markets

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  • Maghyereh, Aktham
  • Abdoh, Hussein

Abstract

A growing body of literature considers investor sentiment as the partial driver of change in commodity prices. In contrast with previous studies that have almost exclusively focused on linear relationship, this empirical paper investigates the entire dynamic dependence of the quantile of investor sentiment and that of ten important commodities. To do so, we use the novel quantile cross-spectral dependence approach of Baruník and Kley (2019) and the nonparametric causality-in-quantiles test proposed by Balcilar et al. (2017a) over the period 1998–2018. Overall, the results show that the inter-dependence between sentiment and commodity differs according to return quantile and time frequency.

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  • Maghyereh, Aktham & Abdoh, Hussein, 2020. "The tail dependence structure between investor sentiment and commodity markets," Resources Policy, Elsevier, vol. 68(C).
    • Handle: RePEc:eee:jrpoli:v:68:y:2020:i:c:s0301420720302828
    DOI: 10.1016/j.resourpol.2020.101789
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    1. P. Hartmann & S. Straetmans & C. G. de Vries, 2004. "Asset Market Linkages in Crisis Periods," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 313-326, February.
    2. Ali, Sajid & Bouri, Elie & Czudaj, Robert Lukas & Shahzad, Syed Jawad Hussain, 2020. "Revisiting the valuable roles of commodities for international stock markets," Resources Policy, Elsevier, vol. 66(C).
    3. Jiancheng Shen & Mohammad Najand & Feng Dong & Wu He, 2017. "News and social media emotions in the commodity market," Review of Behavioral Finance, Emerald Group Publishing Limited, vol. 9(2), pages 148-168, July.
    4. Charlot, Philippe & Darné, Olivier & Moussa, Zakaria, 2016. "Commodity returns co-movements: Fundamentals or “style” effect?," Journal of International Money and Finance, Elsevier, vol. 68(C), pages 130-160.
    5. Stephen Morris, 1996. "Speculative Investor Behavior and Learning," The Quarterly Journal of Economics, Oxford University Press, vol. 111(4), pages 1111-1133.
    6. Baur, Dirk G. & McDermott, Thomas K., 2010. "Is gold a safe haven? International evidence," Journal of Banking & Finance, Elsevier, vol. 34(8), pages 1886-1898, August.
    7. Bahloul, Walid & Bouri, Abdelfettah, 2016. "The impact of investor sentiment on returns and conditional volatility in U.S. futures markets," Journal of Multinational Financial Management, Elsevier, vol. 36(C), pages 89-102.
    8. Qadan, Mahmoud & Nama, Hazar, 2018. "Investor sentiment and the price of oil," Energy Economics, Elsevier, vol. 69(C), pages 42-58.
    9. Racine, Jeff & Li, Qi, 2004. "Nonparametric estimation of regression functions with both categorical and continuous data," Journal of Econometrics, Elsevier, vol. 119(1), pages 99-130, March.
    10. Bekiros, Stelios D. & Diks, Cees G.H., 2008. "The nonlinear dynamic relationship of exchange rates: Parametric and nonparametric causality testing," Journal of Macroeconomics, Elsevier, vol. 30(4), pages 1641-1650, December.
    11. Dirk G. Baur & Brian M. Lucey, 2010. "Is Gold a Hedge or a Safe Haven? An Analysis of Stocks, Bonds and Gold," The Financial Review, Eastern Finance Association, vol. 45(2), pages 217-229, May.
    12. Balcilar, Mehmet & Gupta, Rangan & Sousa, Ricardo M. & Wohar, Mark E., 2017. "Do cay and cayMS predict stock and housing returns? Evidence from a nonparametric causality test," International Review of Economics & Finance, Elsevier, vol. 48(C), pages 269-279.
    13. Maitra, Debasish & Dash, Saumya Ranjan, 2017. "Sentiment and stock market volatility revisited: A time–frequency domain approach," Journal of Behavioral and Experimental Finance, Elsevier, vol. 15(C), pages 74-91.
    14. Lobato, Ignacio N & Savin, N E, 1998. "Real and Spurious Long-Memory Properties of Stock-Market Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(3), pages 261-268, July.
    15. Kent Daniel & David Hirshleifer & Avanidhar Subrahmanyam, 1998. "Investor Psychology and Security Market Under- and Overreactions," Journal of Finance, American Finance Association, vol. 53(6), pages 1839-1885, December.
    16. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521770415.
    17. Adams, Zeno & Glück, Thorsten, 2015. "Financialization in commodity markets: A passing trend or the new normal?," Journal of Banking & Finance, Elsevier, vol. 60(C), pages 93-111.
    18. Lee A. Smales, 2013. "Impact Of Macroeconomic Announcements On Interest Rate Futures: High-Frequency Evidence From Australia," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 36(3), pages 371-388, September.
    19. Steven J. Nooijen & Simon A. Broda, 2016. "Predicting Equity Markets with Digital Online Media Sentiment: Evidence from Markov-switching Models," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 17(4), pages 321-335, October.
    20. Robert J. Barro, 2006. "Rare Disasters and Asset Markets in the Twentieth Century," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 121(3), pages 823-866.
    21. Mehmet Balcilar & Stelios Bekiros & Rangan Gupta, 2017. "The role of news-based uncertainty indices in predicting oil markets: a hybrid nonparametric quantile causality method," Empirical Economics, Springer, vol. 53(3), pages 879-889, November.
    22. Smales, Lee A., 2015. "Asymmetric volatility response to news sentiment in gold futures," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 34(C), pages 161-172.
    23. Barberis, Nicholas & Shleifer, Andrei & Vishny, Robert, 1998. "A model of investor sentiment," Journal of Financial Economics, Elsevier, vol. 49(3), pages 307-343, September.
    24. Algieri, Bernardina & Leccadito, Arturo, 2017. "Assessing contagion risk from energy and non-energy commodity markets," Energy Economics, Elsevier, vol. 62(C), pages 312-322.
    25. Chau, Frankie & Deesomsak, Rataporn & Koutmos, Dimitrios, 2016. "Does investor sentiment really matter?," International Review of Financial Analysis, Elsevier, vol. 48(C), pages 221-232.
    26. Diks, Cees & Panchenko, Valentyn, 2006. "A new statistic and practical guidelines for nonparametric Granger causality testing," Journal of Economic Dynamics and Control, Elsevier, vol. 30(9-10), pages 1647-1669.
    27. Baumöhl, Eduard & Shahzad, Syed Jawad Hussain, 2019. "Quantile coherency networks of international stock markets," Finance Research Letters, Elsevier, vol. 31(C), pages 119-129.
    28. Syed jawad hussain Shahzad & Saba Ameer & Muhammad Shahbaz, 2016. "Disaggregating the correlation under bearish and bullish markets: A Quantile-quantile approach," Economics Bulletin, AccessEcon, vol. 36(4), pages 2465-2473.
    29. Nguyen, Duc Khuong & Sensoy, Ahmet & Sousa, Ricardo M. & Salah Uddin, Gazi, 2020. "U.S. equity and commodity futures markets: Hedging or financialization?," Energy Economics, Elsevier, vol. 86(C).
    30. Yang, Baochen & Pu, Yingjian & Su, Yunpeng, 2020. "The financialization of Chinese commodity markets," Finance Research Letters, Elsevier, vol. 34(C).
    31. Jeong, Kiho & Härdle, Wolfgang K. & Song, Song, 2012. "A Consistent Nonparametric Test For Causality In Quantile," Econometric Theory, Cambridge University Press, vol. 28(4), pages 861-887, August.
    32. Mollick, André Varella & Assefa, Tibebe Abebe, 2013. "U.S. stock returns and oil prices: The tale from daily data and the 2008–2009 financial crisis," Energy Economics, Elsevier, vol. 36(C), pages 1-18.
    33. Nishiyama, Yoshihiko & Hitomi, Kohtaro & Kawasaki, Yoshinori & Jeong, Kiho, 2011. "A consistent nonparametric test for nonlinear causality—Specification in time series regression," Journal of Econometrics, Elsevier, vol. 165(1), pages 112-127.
    34. Shahzad, Syed Jawad Hussain & Raza, Naveed & Balcilar, Mehmet & Ali, Sajid & Shahbaz, Muhammad, 2017. "Can economic policy uncertainty and investors sentiment predict commodities returns and volatility?," Resources Policy, Elsevier, vol. 53(C), pages 208-218.
    35. Maghyereh, Aktham & Awartani, Basel & Abdoh, Hussein, 2020. "The effects of investor emotions sentiments on crude oil returns: A time and frequency dynamics analysis," International Economics, Elsevier, vol. 162(C), pages 110-124.
    36. Boubaker, Heni & Sghaier, Nadia, 2013. "Portfolio optimization in the presence of dependent financial returns with long memory: A copula based approach," Journal of Banking & Finance, Elsevier, vol. 37(2), pages 361-377.
    37. Mehmet Balcilar & Shinhye Chang & Rangan Gupta & Vanessa Kasongo & Clement Kyei, 2016. "The relationship between oil and agricultural commodity prices in south africa: a quantile causality approach," Journal of Developing Areas, Tennessee State University, College of Business, vol. 50(2), pages 137-152, April-Jun.
    38. Jozef Baruník & Tobias Kley, 2019. "Quantile coherency: A general measure for dependence between cyclical economic variables," The Econometrics Journal, Royal Economic Society, vol. 22(2), pages 131-152.
    39. Bing Han, 2008. "Investor Sentiment and Option Prices," Review of Financial Studies, Society for Financial Studies, vol. 21(1), pages 387-414, January.
    40. Gao, Lin & Süss, Stephan, 2015. "Market sentiment in commodity futures returns," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 84-103.
    41. Balcilar, Mehmet & Bonato, Matteo & Demirer, Riza & Gupta, Rangan, 2017. "The effect of investor sentiment on gold market return dynamics: Evidence from a nonparametric causality-in-quantiles approach," Resources Policy, Elsevier, vol. 51(C), pages 77-84.
    42. Brian J. Henderson & Neil D. Pearson & Li Wang, 2015. "Editor's Choice New Evidence on the Financialization of Commodity Markets," Review of Financial Studies, Society for Financial Studies, vol. 28(5), pages 1285-1311.
    43. Corbet, Shaen & Gurdgiev, Constantin & Meegan, Andrew, 2018. "Long-term stock market volatility and the influence of terrorist attacks in Europe," The Quarterly Review of Economics and Finance, Elsevier, vol. 68(C), pages 118-131.
    44. Manuel Ammann & Roman Frey & Michael Verhofen, 2014. "Do Newspaper Articles Predict Aggregate Stock Returns?," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 15(3), pages 195-213, July.
    45. Nguyen, Quynh Nga & Aboura, Sofiane & Chevallier, Julien & Zhang, Lyuyuan & Zhu, Bangzhu, 2020. "Local Gaussian correlations in financial and commodity markets," European Journal of Operational Research, Elsevier, vol. 285(1), pages 306-323.
    46. Reboredo, Juan C. & Rivera-Castro, Miguel A., 2013. "A wavelet decomposition approach to crude oil price and exchange rate dependence," Economic Modelling, Elsevier, vol. 32(C), pages 42-57.
    47. Chakrabarty, Anindya & De, Anupam & Gunasekaran, Angappa & Dubey, Rameshwar, 2015. "Investment horizon heterogeneity and wavelet: Overview and further research directions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 45-61.
    48. Walid Bahloul & Rangan Gupta, 2018. "Impact of macroeconomic news surprises and uncertainty for major economies on returns and volatility of oil futures," International Economics, CEPII research center, issue 156, pages 247-253.
    49. Tiwari, Aviral Kumar & Dar, Arif Billah & Bhanja, Niyati, 2013. "Oil price and exchange rates: A wavelet based analysis for India," Economic Modelling, Elsevier, vol. 31(C), pages 414-422.
    50. Aktham Maghyereh & Basel Awartani & Abul Hassan, 2018. "Can gold be used as a hedge against the risks of Sharia-compliant securities? Application for Islamic portfolio management," Journal of Asset Management, Palgrave Macmillan, vol. 19(6), pages 394-412, October.
    51. Marshall, John F, 1994. "The Role of the Investment Horizon in Optimal Portfolio Sequencing (An Intuitive Demonstration in Discrete Time)," The Financial Review, Eastern Finance Association, vol. 29(4), pages 557-576, November.
    52. Lobato, Ignacio N & Savin, N E, 1998. "Real and Spurious Long-Memory Properties of Stock-Market Data: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(3), pages 280-283, July.
    53. Chen, Xirong & Li, Degui & Li, Qi & Li, Zheng, 2019. "Nonparametric estimation of conditional quantile functions in the presence of irrelevant covariates," Journal of Econometrics, Elsevier, vol. 212(2), pages 433-450.
    54. Adams, Zeno & Glueck, Thorsten, 2014. "Financialization in Commodity Markets: A Passing Trend or the New Normal?," Working Papers on Finance 1413, University of St. Gallen, School of Finance, revised Aug 2015.
    55. Riordan, Ryan & Storkenmaier, Andreas & Wagener, Martin & Sarah Zhang, S., 2013. "Public information arrival: Price discovery and liquidity in electronic limit order markets," Journal of Banking & Finance, Elsevier, vol. 37(4), pages 1148-1159.
    56. Gençay, Ramazan & Dacorogna, Michel & Muller, Ulrich A. & Pictet, Olivier & Olsen, Richard, 2001. "An Introduction to High-Frequency Finance," Elsevier Monographs, Elsevier, edition 1, number 9780122796715.
    57. Eric C. Chang & Chao Chen & Son‐Nan Chen, 1990. "Risk and return in copper, platinum, and silver futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 10(1), pages 29-39, February.
    58. J. Michael Harrison & David M. Kreps, 1978. "Speculative Investor Behavior in a Stock Market with Heterogeneous Expectations," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 92(2), pages 323-336.
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    More about this item

    Keywords

    Sentiment; Commodity; Quantile cross-spectral dependence; Causality-in-quantiles;
    All these keywords.

    JEL classification:

    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market

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