IDEAS home Printed from https://ideas.repec.org/a/eee/jocoma/v31y2023ics2405851323000338.html
   My bibliography  Save this article

Determinants and dynamic interactions of trader positions in the gold futures market

Author

Listed:
  • Chen, Yu-Lun
  • Mo, Wan-Shin

Abstract

We investigate the determinants of different traders’ trading positions in the gold futures market. With a threshold value determined endogenously by our model, we find that when the gold futures price falls below the threshold, money managers adopt positive feedback trading strategies while swap dealers adopt negative feedback trading strategies. When the futures price rises above the threshold, money managers turn to negative feedback trading and swap dealers reduce the intensity of their negative feedback. In addition, money managers and swap dealers play the transmitter role in trading spillovers to other traders, and their trading transmitter role weakens during periods with high gold prices.

Suggested Citation

  • Chen, Yu-Lun & Mo, Wan-Shin, 2023. "Determinants and dynamic interactions of trader positions in the gold futures market," Journal of Commodity Markets, Elsevier, vol. 31(C).
    • Handle: RePEc:eee:jocoma:v:31:y:2023:i:c:s2405851323000338
    DOI: 10.1016/j.jcomm.2023.100343
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S2405851323000338
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jcomm.2023.100343?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. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    2. Changyun Wang, 2003. "The behavior and performance of major types of futures traders," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 23(1), pages 1-31, January.
    3. Nina Karnaukh & Angelo Ranaldo & Paul Söderlind, 2015. "Understanding FX Liquidity," Review of Financial Studies, Society for Financial Studies, vol. 28(11), pages 3073-3108.
    4. Newey, Whitney K & West, Kenneth D, 1987. "Hypothesis Testing with Efficient Method of Moments Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 28(3), pages 777-787, October.
    5. 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.
    6. Chen, Yu-Lun & Yang, J. Jimmy, 2021. "Trader positions in VIX futures," Journal of Empirical Finance, Elsevier, vol. 61(C), pages 1-17.
    7. David Gabauer, 2020. "Volatility impulse response analysis for DCC‐GARCH models: The role of volatility transmission mechanisms," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 788-796, August.
    8. Andreas Röthig & Carl Chiarella, 2007. "Investigating nonlinear speculation in cattle, corn, and hog futures markets using logistic smooth transition regression models," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 27(8), pages 719-737, August.
    9. De Long, J Bradford, et al, 1990. "Positive Feedback Investment Strategies and Destabilizing Rational Speculation," Journal of Finance, American Finance Association, vol. 45(2), pages 379-395, June.
    10. Baur, Dirk G. & Kuck, Konstantin, 2020. "The timing of the flight to gold: An intra-day analysis of gold and the S&P500," Finance Research Letters, Elsevier, vol. 33(C).
    11. Ciner, Cetin & Gurdgiev, Constantin & Lucey, Brian M., 2013. "Hedges and safe havens: An examination of stocks, bonds, gold, oil and exchange rates," International Review of Financial Analysis, Elsevier, vol. 29(C), pages 202-211.
    12. Chau, Frankie & Deesomsak, Rataporn & Lau, Marco C.K., 2011. "Investor sentiment and feedback trading: Evidence from the exchange-traded fund markets," International Review of Financial Analysis, Elsevier, vol. 20(5), pages 292-305.
    13. Paul H. Cootner, 1960. "Returns to Speculators: Telser versus Keynes," Journal of Political Economy, University of Chicago Press, vol. 68, pages 396-396.
    14. Smales, Lee A., 2014. "News sentiment in the gold futures market," Journal of Banking & Finance, Elsevier, vol. 49(C), pages 275-286.
    15. Ji, Qiang & Li, Jianping & Sun, Xiaolei, 2019. "Measuring the interdependence between investor sentiment and crude oil returns: New evidence from the CFTC's disaggregated reports," Finance Research Letters, Elsevier, vol. 30(C), pages 420-425.
    16. Gębka, Bartosz & Wohar, Mark E., 2013. "The determinants of quantile autocorrelations: Evidence from the UK," International Review of Financial Analysis, Elsevier, vol. 29(C), pages 51-61.
    17. Boako, Gideon & Tiwari, Aviral Kumar & Ibrahim, Muazu & Ji, Qiang, 2019. "Analysing dynamic dependence between gold and stock returns: Evidence using stochastic and full-range tail dependence copula models," Finance Research Letters, Elsevier, vol. 31(C).
    18. Dedi, Valentina & Mandilaras, Alex, 2022. "Trader positions and the price of oil in the futures market," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 448-460.
    19. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    20. Sarantis, Nicholas, 1999. "Modeling non-linearities in real effective exchange rates," Journal of International Money and Finance, Elsevier, vol. 18(1), pages 27-45, January.
    21. Drake, Pamela Peterson, 2022. "The gold-stock market relationship during COVID-19," Finance Research Letters, Elsevier, vol. 44(C).
    22. Boako, Gideon & Alagidede, Paul, 2016. "Global commodities and African stocks: A ‘market of one?’," International Review of Financial Analysis, Elsevier, vol. 44(C), pages 226-237.
    23. John R. Nofsinger & Richard W. Sias, 1999. "Herding and Feedback Trading by Institutional and Individual Investors," Journal of Finance, American Finance Association, vol. 54(6), pages 2263-2295, December.
    24. Bilgin, Mehmet Huseyin & Gozgor, Giray & Lau, Chi Keung Marco & Sheng, Xin, 2018. "The effects of uncertainty measures on the price of gold," International Review of Financial Analysis, Elsevier, vol. 58(C), pages 1-7.
    25. Hung, Ngo Thai & Vo, Xuan Vinh, 2021. "Directional spillover effects and time-frequency nexus between oil, gold and stock markets: Evidence from pre and during COVID-19 outbreak," International Review of Financial Analysis, Elsevier, vol. 76(C).
    26. Ordu-Akkaya, Beyza Mina & Ugurlu-Yildirim, Ecenur & Soytas, Ugur, 2019. "The role of trading volume, open interest and trader positions on volatility transmission between spot and futures markets," Resources Policy, Elsevier, vol. 61(C), pages 410-422.
    27. Jun Cai & Yan‐Leung Cheung & Michael C. S. Wong, 2001. "What moves the gold market?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 21(3), pages 257-278, March.
    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. Charteris, Ailie & Kallinterakis, Vasileios, 2021. "Feedback trading in retail-dominated assets: Evidence from the gold bullion coin market," International Review of Financial Analysis, Elsevier, vol. 75(C).
    2. Kamal, Javed Bin & Wohar, Mark & Kamal, Khaled Bin, 2022. "Do gold, oil, equities, and currencies hedge economic policy uncertainty and geopolitical risks during covid crisis?," Resources Policy, Elsevier, vol. 78(C).
    3. Golitsis, Petros & Gkasis, Pavlos & Bellos, Sotirios K., 2022. "Dynamic spillovers and linkages between gold, crude oil, S&P 500, and other economic and financial variables. Evidence from the USA," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    4. Mensi, Walid & Hanif, Waqas & Vo, Xuan Vinh & Choi, Ki-Hong & Yoon, Seong-Min, 2023. "Upside/Downside spillovers between oil and Chinese stock sectors: From the global financial crisis to global pandemic," The North American Journal of Economics and Finance, Elsevier, vol. 67(C).
    5. Sobti, Neharika & Sehgal, Sanjay & Ilango, Balakrishnan, 2021. "How do macroeconomic news surprises affect round-the-clock price discovery of gold?," International Review of Financial Analysis, Elsevier, vol. 78(C).
    6. Maghyereh, Aktham & Awartani, Basel & Virk, Nader S., 2022. "Asymmetric risk transmissions between oil, gold and US equities: Recent evidence from the realized variance of the futures prices," Resources Policy, Elsevier, vol. 79(C).
    7. Evrim Mandacı, Pınar & Cagli, Efe Çaglar & Taşkın, Dilvin, 2020. "Dynamic connectedness and portfolio strategies: Energy and metal markets," Resources Policy, Elsevier, vol. 68(C).
    8. Kang, Sanghoon & Hernandez, Jose Arreola & Sadorsky, Perry & McIver, Ronald, 2021. "Frequency spillovers, connectedness, and the hedging effectiveness of oil and gold for US sector ETFs," Energy Economics, Elsevier, vol. 99(C).
    9. Mensi, Walid & Al Rababa'a, Abdel Razzaq & Vo, Xuan Vinh & Kang, Sang Hoon, 2021. "Asymmetric spillover and network connectedness between crude oil, gold, and Chinese sector stock markets," Energy Economics, Elsevier, vol. 98(C).
    10. Akihiro Omura & Neda Todorova, 2019. "The quantile dependence of commodity futures markets on news sentiment," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(7), pages 818-837, July.
    11. Le, TN-Lan & Abakah, Emmanuel Joel Aikins & Tiwari, Aviral Kumar, 2021. "Time and frequency domain connectedness and spill-over among fintech, green bonds and cryptocurrencies in the age of the fourth industrial revolution," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    12. Lei, Heng & Xue, Minggao & Liu, Huiling & Ye, Jing, 2023. "Precious metal as a safe haven for global ESG stocks: Portfolio implications for socially responsible investing," Resources Policy, Elsevier, vol. 80(C).
    13. Mensi, Walid & Vo, Xuan Vinh & Kang, Sang Hoon, 2021. "Precious metals, oil, and ASEAN stock markets: From global financial crisis to global health crisis," Resources Policy, Elsevier, vol. 73(C).
    14. O'Connor, Fergal A. & Lucey, Brian M. & Batten, Jonathan A. & Baur, Dirk G., 2015. "The financial economics of gold — A survey," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 186-205.
    15. Mensi, Walid & Vo, Xuan Vinh & Ko, Hee-Un & Kang, Sang Hoon, 2023. "Frequency spillovers between green bonds, global factors and stock market before and during COVID-19 crisis," Economic Analysis and Policy, Elsevier, vol. 77(C), pages 558-580.
    16. Semeyutin, Artur & Downing, Gareth, 2022. "Co-jumps in the U.S. interest rates and precious metals markets and their implications for investors," International Review of Financial Analysis, Elsevier, vol. 81(C).
    17. Min Bai & Ly Ho, 2023. "How do gold and oil react to the COVID-19 pandemic: A review," Energy & Environment, , vol. 34(7), pages 2876-2902, November.
    18. Rizvi, Syed Kumail Abbas & Naqvi, Bushra & Mirza, Nawazish & Umar, Muhammad, 2022. "Safe haven properties of green, Islamic, and crypto assets and investor's proclivity towards treasury and gold," Energy Economics, Elsevier, vol. 115(C).
    19. Huynh, Toan Luu Duc & Hille, Erik & Nasir, Muhammad Ali, 2020. "Diversification in the age of the 4th industrial revolution: The role of artificial intelligence, green bonds and cryptocurrencies," Technological Forecasting and Social Change, Elsevier, vol. 159(C).
    20. Nekhili, Ramzi & Mensi, Walid & Vo, Xuan Vinh, 2021. "Multiscale spillovers and connectedness between gold, copper, oil, wheat and currency markets," Resources Policy, Elsevier, vol. 74(C).

    More about this item

    Keywords

    Gold futures; Money managers; Swap dealers; Logistic smooth transition autoregressive (LSTAR) model;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G40 - Financial Economics - - Behavioral Finance - - - General

    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:jocoma:v:31:y:2023:i:c:s2405851323000338. 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/jcomm .

    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.