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Precious Metals Under the Microscope: A High-Frequency Analysis

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  • Caporin, Massimiliano
  • Ranaldo, Angelo
  • Velo, Gabriel G.

Abstract

Taking advantage of a trades-and-quotes high-frequency database, we document the main stylized facts and dynamic properties of spot precious metals, i.e. gold, silver, palladium, and platinum. We analyze the behaviors of spot prices, returns, volume, and selected liquidity measures. We find clear evidence of periodic patterns matching the trading hours of the most active markets round-the-clock. The time series of spot returns have thus properties similar to those of traditional financial assets with fat tails, asymmetry, periodic behaviors in the conditional variances, and volatility clustering. The gold (platinum) is the most (least) liquid and less (most) volatile asset. Commonality in liquidities of precious metals is very strong.

Suggested Citation

  • Caporin, Massimiliano & Ranaldo, Angelo & Velo, Gabriel G., 2014. "Precious Metals Under the Microscope: A High-Frequency Analysis," Working Papers on Finance 1409, University of St. Gallen, School of Finance.
  • Handle: RePEc:usg:sfwpfi:2014:09
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    Cited by:

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    3. Jonathan Batten & Brian Lucey & Frank McGroarty & Maurice Peat & Andrew Urquhart, 2017. "Stylized facts of intraday precious metals," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-21, April.
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    5. Bao, Dun, 2020. "Dynamics and correlation of platinum-group metals spot prices," Resources Policy, Elsevier, vol. 68(C).
    6. Guglielmo Maria Caporale & Alex Plastun, 2021. "Gold and oil prices: abnormal returns, momentum and contrarian effects," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 35(3), pages 353-368, September.
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    8. Joel Verghese & Phaik Nie Chin, 2022. "Factors affecting investors’ intention to purchase gold and silver bullion: evidence from Malaysia," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 27(1), pages 41-51, March.
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    10. He, Kaijian & Chen, Yanhui & Tso, Geoffrey K.F., 2017. "Price forecasting in the precious metal market: A multivariate EMD denoising approach," Resources Policy, Elsevier, vol. 54(C), pages 9-24.
    11. Vigne, Samuel A. & Lucey, Brian M. & O’Connor, Fergal A. & Yarovaya, Larisa, 2017. "The financial economics of white precious metals — A survey," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 292-308.
    12. Zhang, Hanxiong & Auer, Benjamin R. & Vortelinos, Dimitrios I., 2018. "Performance ranking (dis)similarities in commodity markets," Global Finance Journal, Elsevier, vol. 35(C), pages 115-137.
    13. He, Kaijian & Liu, Youjin & Yu, Lean & Lai, Kin Keung, 2016. "Multiscale dependence analysis and portfolio risk modeling for precious metal markets," Resources Policy, Elsevier, vol. 50(C), pages 224-233.
    14. Renata G. Alcoforado & Alfredo D. Egídio dos Reis & Wilton Bernardino & José António C. Santos, 2023. "Modelling Risk for Commodities in Brazil: An Application for Live Cattle Spot and Futures Prices," Commodities, MDPI, vol. 2(4), pages 1-19, November.
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    More about this item

    Keywords

    precious metals; high-frequency data; liquidity; commonality in liquidity; intradaily periodicity;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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