IDEAS home Printed from https://ideas.repec.org/a/eee/pacfin/v86y2024ics0927538x24001811.html
   My bibliography  Save this article

Market uncertainty and information content in complex seasonality of prices

Author

Listed:
  • Tang, Wenjin
  • Bu, Hui
  • Ji, Yuqiong
  • Li, Zhongfei

Abstract

In the context of heightened economic and financial market uncertainty, identifying and measuring uncertainty have become an important research question. This study offers a novel approach to uncover uncertainty and its sources by leveraging the complex seasonality inherent in futures prices. Our findings indicate that the temporal hierarchical forecasting method is not universally effective across 35 different commodity futures. However, further analysis reveals that this method outperforms single time series forecasting models when its manually set seasonal components align with those identified through singular spectrum analysis (SSA). The study demonstrates the efficacy of SSA in revealing complex seasonality patterns in various futures prices. Additionally, our event analysis results indicate that shifts in the seasonality patterns of commodity futures prices provide a new perspective for understanding uncertainty and exploring its origins. This research contributes to the literature by extending market uncertainty measures through the linkage with complex seasonality analysis, and offering valuable insights for risk management.

Suggested Citation

  • Tang, Wenjin & Bu, Hui & Ji, Yuqiong & Li, Zhongfei, 2024. "Market uncertainty and information content in complex seasonality of prices," Pacific-Basin Finance Journal, Elsevier, vol. 86(C).
  • Handle: RePEc:eee:pacfin:v:86:y:2024:i:c:s0927538x24001811
    DOI: 10.1016/j.pacfin.2024.102430
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0927538X24001811
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.pacfin.2024.102430?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bakas, Dimitrios & Triantafyllou, Athanasios, 2018. "The impact of uncertainty shocks on the volatility of commodity prices," Journal of International Money and Finance, Elsevier, vol. 87(C), pages 96-111.
    2. Li, Jingyu & Liu, Ranran & Yao, Yanzhen & Xie, Qiwei, 2022. "Time-frequency volatility spillovers across the international crude oil market and Chinese major energy futures markets: Evidence from COVID-19," Resources Policy, Elsevier, vol. 77(C).
    3. Joëts, Marc & Mignon, Valérie & Razafindrabe, Tovonony, 2017. "Does the volatility of commodity prices reflect macroeconomic uncertainty?," Energy Economics, Elsevier, vol. 68(C), pages 313-326.
    4. Zhi Da & Joseph Engelberg & Pengjie Gao, 2015. "Editor's Choice The Sum of All FEARS Investor Sentiment and Asset Prices," The Review of Financial Studies, Society for Financial Studies, vol. 28(1), pages 1-32.
    5. Borgards, Oliver & Czudaj, Robert L. & Hoang, Thi Hong Van, 2021. "Price overreactions in the commodity futures market: An intraday analysis of the Covid-19 pandemic impact," Resources Policy, Elsevier, vol. 71(C).
    6. Azzimonti, Marina, 2018. "Partisan conflict and private investment," Journal of Monetary Economics, Elsevier, vol. 93(C), pages 114-131.
    7. Dario Caldara & Matteo Iacoviello, 2022. "Measuring Geopolitical Risk," American Economic Review, American Economic Association, vol. 112(4), pages 1194-1225, April.
    8. Kang, Wensheng & Ratti, Ronald A., 2013. "Structural oil price shocks and policy uncertainty," Economic Modelling, Elsevier, vol. 35(C), pages 314-319.
    9. Qadan, Mahmoud & Aharon, David Y. & Eichel, Ron, 2019. "Seasonal patterns and calendar anomalies in the commodity market for natural resources," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
    10. Ramiah, Vikash & Wallace, Damien & Veron, Jose Francisco & Reddy, Krishna & Elliott, Robert, 2019. "The effects of recent terrorist attacks on risk and return in commodity markets," Energy Economics, Elsevier, vol. 77(C), pages 13-22.
    11. Erten, Bilge & Ocampo, José Antonio, 2013. "Super Cycles of Commodity Prices Since the Mid-Nineteenth Century," World Development, Elsevier, vol. 44(C), pages 14-30.
    12. Taylor, James W., 2010. "Triple seasonal methods for short-term electricity demand forecasting," European Journal of Operational Research, Elsevier, vol. 204(1), pages 139-152, July.
    13. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    14. Burke, Paul J. & Yang, Hewen, 2016. "The price and income elasticities of natural gas demand: International evidence," Energy Economics, Elsevier, vol. 59(C), pages 466-474.
    15. Agnello, Luca & Castro, Vítor & Hammoudeh, Shawkat & Sousa, Ricardo M., 2020. "Global factors, uncertainty, weather conditions and energy prices: On the drivers of the duration of commodity price cycle phases," Energy Economics, Elsevier, vol. 90(C).
    16. Golyandina, Nina & Korobeynikov, Anton, 2014. "Basic Singular Spectrum Analysis and forecasting with R," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 934-954.
    17. Meng Qin & Lian-Hong Qiu & Ran Tao & Muhammad Umar & Chi-Wei Su & Wen Jiao, 2020. "The inevitable role of El Niño: a fresh insight into the oil market," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 33(1), pages 1943-1962, January.
    18. Liu, Yang & Han, Liyan & Xu, Yang, 2021. "The impact of geopolitical uncertainty on energy volatility," International Review of Financial Analysis, Elsevier, vol. 75(C).
    19. Back, Janis & Prokopczuk, Marcel & Rudolf, Markus, 2013. "Seasonality and the valuation of commodity options," Journal of Banking & Finance, Elsevier, vol. 37(2), pages 273-290.
    20. Taylor, James W. & de Menezes, Lilian M. & McSharry, Patrick E., 2006. "A comparison of univariate methods for forecasting electricity demand up to a day ahead," International Journal of Forecasting, Elsevier, vol. 22(1), pages 1-16.
    21. Ewald, Christian-Oliver & Haugom, Erik & Lien, Gudbrand & Størdal, Ståle & Wu, Yuexiang, 2022. "Trading time seasonality in commodity futures: An opportunity for arbitrage in the natural gas and crude oil markets?," Energy Economics, Elsevier, vol. 115(C).
    22. Brandon Julio & Youngsuk Yook, 2012. "Political Uncertainty and Corporate Investment Cycles," Journal of Finance, American Finance Association, vol. 67(1), pages 45-84, February.
    23. Fanelli, Viviana & Maddalena, Lucia & Musti, Silvana, 2016. "Modelling electricity futures prices using seasonal path-dependent volatility," Applied Energy, Elsevier, vol. 173(C), pages 92-102.
    24. Athanasopoulos, George & Ahmed, Roman A. & Hyndman, Rob J., 2009. "Hierarchical forecasts for Australian domestic tourism," International Journal of Forecasting, Elsevier, vol. 25(1), pages 146-166.
    25. Athanasopoulos, George & Hyndman, Rob J. & Kourentzes, Nikolaos & Petropoulos, Fotios, 2017. "Forecasting with temporal hierarchies," European Journal of Operational Research, Elsevier, vol. 262(1), pages 60-74.
    26. Umar, Zaghum & Jareño, Francisco & Escribano, Ana, 2021. "Oil price shocks and the return and volatility spillover between industrial and precious metals," Energy Economics, Elsevier, vol. 99(C).
    27. Kyle Jurado & Sydney C. Ludvigson & Serena Ng, 2015. "Measuring Uncertainty," American Economic Review, American Economic Association, vol. 105(3), pages 1177-1216, March.
    28. Aloui, Riadh & Gupta, Rangan & Miller, Stephen M., 2016. "Uncertainty and crude oil returns," Energy Economics, Elsevier, vol. 55(C), pages 92-100.
    29. Scotti, Chiara, 2016. "Surprise and uncertainty indexes: Real-time aggregation of real-activity macro-surprises," Journal of Monetary Economics, Elsevier, vol. 82(C), pages 1-19.
    30. Brix, Anne Floor & Lunde, Asger & Wei, Wei, 2018. "A generalized Schwartz model for energy spot prices — Estimation using a particle MCMC method," Energy Economics, Elsevier, vol. 72(C), pages 560-582.
    31. Richter, Martin & Sørensen, Carsten, 2002. "Stochastic Volatility and Seasonality in Commodity Futures and Options: The Case of Soybeans," Working Papers 2002-4, Copenhagen Business School, Department of Finance.
    32. Arismendi, Juan C. & Back, Janis & Prokopczuk, Marcel & Paschke, Raphael & Rudolf, Markus, 2016. "Seasonal Stochastic Volatility: Implications for the pricing of commodity options," Journal of Banking & Finance, Elsevier, vol. 66(C), pages 53-65.
    33. Ji, Qiang & Liu, Bing-Yue & Zhao, Wan-Li & Fan, Ying, 2020. "Modelling dynamic dependence and risk spillover between all oil price shocks and stock market returns in the BRICS," International Review of Financial Analysis, Elsevier, vol. 68(C).
    34. Hyndman, Rob J. & Lee, Alan J. & Wang, Earo, 2016. "Fast computation of reconciled forecasts for hierarchical and grouped time series," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 16-32.
    35. Zhang, G. Peter & Qi, Min, 2005. "Neural network forecasting for seasonal and trend time series," European Journal of Operational Research, Elsevier, vol. 160(2), pages 501-514, January.
    36. David Heath & Robert Jarrow & Andrew Morton, 2008. "Bond Pricing And The Term Structure Of Interest Rates: A New Methodology For Contingent Claims Valuation," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 13, pages 277-305, World Scientific Publishing Co. Pte. Ltd..
    37. Kang, Wensheng & Perez de Gracia, Fernando & Ratti, Ronald A., 2017. "Oil price shocks, policy uncertainty, and stock returns of oil and gas corporations," Journal of International Money and Finance, Elsevier, vol. 70(C), pages 344-359.
    38. Fatma Pinar Erdem & Ibrahim Unalmis, 2016. "Revisiting super-cycles in commodity prices," Central Bank Review, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, vol. 16(4), pages 137-142.
    39. Erik Haugom & Guttorm A. Hoff & Peter Molnár & Maria Mortensen & Sjur Westgaard, 2018. "The Forward Premium in the Nord Pool Power Market," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 54(8), pages 1793-1807, June.
    40. Hyndman, Rob J. & Ahmed, Roman A. & Athanasopoulos, George & Shang, Han Lin, 2011. "Optimal combination forecasts for hierarchical time series," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2579-2589, September.
    41. Yousaf, Imran & Beljid, Makram & Chaibi, Anis & Ajlouni, Ahmed AL, 2022. "Do volatility spillover and hedging among GCC stock markets and global factors vary from normal to turbulent periods? Evidence from the global financial crisis and Covid-19 pandemic crisis," Pacific-Basin Finance Journal, Elsevier, vol. 73(C).
    42. Viviana Fanelli & Maren Diane Schmeck, 2019. "On the seasonality in the implied volatility of electricity options," Quantitative Finance, Taylor & Francis Journals, vol. 19(8), pages 1321-1337, August.
    43. Mei, Dexiang & Ma, Feng & Liao, Yin & Wang, Lu, 2020. "Geopolitical risk uncertainty and oil future volatility: Evidence from MIDAS models," Energy Economics, Elsevier, vol. 86(C).
    44. Kannadhasan, M. & Das, Debojyoti, 2020. "Do Asian emerging stock markets react to international economic policy uncertainty and geopolitical risk alike? A quantile regression approach," Finance Research Letters, Elsevier, vol. 34(C).
    45. 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.
    46. Hassani, Hossein & Heravi, Saeed & Zhigljavsky, Anatoly, 2009. "Forecasting European industrial production with singular spectrum analysis," International Journal of Forecasting, Elsevier, vol. 25(1), pages 103-118.
    47. Andreasson, Pierre & Bekiros, Stelios & Nguyen, Duc Khuong & Uddin, Gazi Salah, 2016. "Impact of speculation and economic uncertainty on commodity markets," International Review of Financial Analysis, Elsevier, vol. 43(C), pages 115-127.
    48. Zhang, Yongmin & Wang, Ruizhi, 2022. "COVID-19 impact on commodity futures volatilities," Finance Research Letters, Elsevier, vol. 47(PA).
    49. Noguera-Santaella, José, 2016. "Geopolitics and the oil price," Economic Modelling, Elsevier, vol. 52(PB), pages 301-309.
    50. Ewald, Christian & Zou, Yihan, 2021. "Analytic formulas for futures and options for a linear quadratic jump diffusion model with seasonal stochastic volatility and convenience yield: Do fish jump?," European Journal of Operational Research, Elsevier, vol. 294(2), pages 801-815.
    51. Sari, Ramazan & Soytas, Ugur & Hacihasanoglu, Erk, 2011. "Do global risk perceptions influence world oil prices?," Energy Economics, Elsevier, vol. 33(3), pages 515-524, May.
    52. Manela, Asaf & Moreira, Alan, 2017. "News implied volatility and disaster concerns," Journal of Financial Economics, Elsevier, vol. 123(1), pages 137-162.
    53. Lorenz Schneider & Bertrand Tavin, 2024. "Seasonal volatility in agricultural markets : modelling and empirical investigations," Post-Print hal-04514341, HAL.
    54. Coussin, Maximilien, 2022. "Singular spectrum analysis for real-time financial cycles measurement," Journal of International Money and Finance, Elsevier, vol. 120(C).
    55. Hylleberg, S. (ed.), 1992. "Modelling Seasonality," OUP Catalogue, Oxford University Press, number 9780198773184.
    56. Reboredo, Juan C. & Uddin, Gazi Salah, 2016. "Do financial stress and policy uncertainty have an impact on the energy and metals markets? A quantile regression approach," International Review of Economics & Finance, Elsevier, vol. 43(C), pages 284-298.
    57. Hélyette Geman & Vu-Nhat Nguyen, 2005. "Soybean Inventory and Forward Curve Dynamics," Management Science, INFORMS, vol. 51(7), pages 1076-1091, July.
    58. repec:dau:papers:123456789/1937 is not listed on IDEAS
    59. Hassani, Hossein, 2007. "Singular Spectrum Analysis: Methodology and Comparison," MPRA Paper 4991, University Library of Munich, Germany.
    60. Yong Jiang & Yi-Shuai Ren & Chao-Qun Ma & Jiang-Long Liu & Basil Sharp, 2018. "Does the price of strategic commodities respond to U.S. Partisan Conflict?," Papers 1810.08396, arXiv.org, revised Feb 2020.
    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. Qadan, Mahmoud & Idilbi, Yasmeen, 2022. "Presidential honeymoons, political cycles and the commodity market," Resources Policy, Elsevier, vol. 77(C).
    2. Yang Liu & Liyan Han & Libo Yin, 2018. "Does news uncertainty matter for commodity futures markets? Heterogeneity in energy and non‐energy sectors," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(10), pages 1246-1261, October.
    3. Gong, Xu & Xu, Jun, 2022. "Geopolitical risk and dynamic connectedness between commodity markets," Energy Economics, Elsevier, vol. 110(C).
    4. Dash, Saumya Ranjan & Maitra, Debasish, 2021. "Do oil and gas prices influence economic policy uncertainty differently: Multi-country evidence using time-frequency approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 81(C), pages 397-420.
    5. Ding, Qian & Huang, Jianbai & Zhang, Hongwei, 2021. "The time-varying effects of financial and geopolitical uncertainties on commodity market dynamics: A TVP-SVAR-SV analysis," Resources Policy, Elsevier, vol. 72(C).
    6. Mokni, Khaled & Al-Shboul, Mohammed & Assaf, Ata, 2021. "Economic policy uncertainty and dynamic spillover among precious metals under market conditions: Does COVID-19 have any effects?," Resources Policy, Elsevier, vol. 74(C).
    7. Xiao, Jihong & Wang, Yudong & Wen, Danyan, 2023. "The predictive effect of risk aversion on oil returns under different market conditions," Energy Economics, Elsevier, vol. 126(C).
    8. Tan, Xueping & Zhong, Yiran & Vivian, Andrew & Geng, Yong & Wang, Ziyi & Zhao, Difei, 2024. "Towards an era of multi-source uncertainty: A systematic and bibliometric analysis," International Review of Financial Analysis, Elsevier, vol. 95(PB).
    9. Karanasos, M. & Yfanti, S., 2021. "On the Economic fundamentals behind the Dynamic Equicorrelations among Asset classes: Global evidence from Equities, Real estate, and Commodities," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
    10. Xiao, Jihong & Wang, Yudong, 2022. "Macroeconomic uncertainty, speculation, and energy futures returns: Evidence from a quantile regression," Energy, Elsevier, vol. 241(C).
    11. António Afonso & José Alves & João Jalles & Sofia Monteiro & João Tovar Jalles, 2024. "Energy Price Dynamics in the Face of Uncertainty Shocks and the Role of Exchange Rate Regimes: A Global Cross-Country Analysis," CESifo Working Paper Series 11384, CESifo.
    12. Xiao, Jihong & Wen, Fenghua & He, Zhifang, 2023. "Impact of geopolitical risks on investor attention and speculation in the oil market: Evidence from nonlinear and time-varying analysis," Energy, Elsevier, vol. 267(C).
    13. Liu, Yang & Han, Liyan & Xu, Yang, 2021. "The impact of geopolitical uncertainty on energy volatility," International Review of Financial Analysis, Elsevier, vol. 75(C).
    14. Yi‐Ting Peng & Tsangyao Chang & Omid Ranjbar, 2022. "Analyzing the degree of persistence of economic policy uncertainty using linear and non‐linear fourier quantile unit root tests," Manchester School, University of Manchester, vol. 90(4), pages 453-471, July.
    15. Gupta, Rangan & Ma, Jun & Risse, Marian & Wohar, Mark E., 2018. "Common business cycles and volatilities in US states and MSAs: The role of economic uncertainty," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 317-337.
    16. Lyu, Yongjian & Wei, Yu & Hu, Yingyi & Yang, Mo, 2021. "Good volatility, bad volatility and economic uncertainty: Evidence from the crude oil futures market," Energy, Elsevier, vol. 222(C).
    17. Ioannis Dokas & Georgios Oikonomou & Minas Panagiotidis & Eleftherios Spyromitros, 2023. "Macroeconomic and Uncertainty Shocks’ Effects on Energy Prices: A Comprehensive Literature Review," Energies, MDPI, vol. 16(3), pages 1-35, February.
    18. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    19. Zhang, Jiahao & Zhang, Yifeng & Wei, Yu & Wang, Zhuo, 2024. "Normal and extreme impact and connectedness between fossil energy futures markets and uncertainties: Does El Niño-Southern Oscillation matter?," International Review of Economics & Finance, Elsevier, vol. 89(PB), pages 188-215.
    20. Nonejad, Nima, 2023. "Modeling the out-of-sample predictive relationship between equity premium, returns on the price of crude oil and economic policy uncertainty using multivariate time-varying dimension models," Energy Economics, Elsevier, vol. 126(C).

    More about this item

    Keywords

    Market uncertainty; Seasonality; Spectrum analysis; Global financial crisis; COVID-19 pandemic; Russia-Ukraine conflict;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • 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

    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:eee:pacfin:v:86:y:2024:i:c:s0927538x24001811. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/pacfin .

    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.