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Quantile connectedness between cryptocurrency and commodity futures

Author

Listed:
  • Joo, Young C.
  • Park, Sung Y.

Abstract

This study investigates the quantile dependence and spillovers for return and volatility of Bitcoin and futures of crude oil, copper, natural gas, and gold. We apply quantile vector autoregression and quantile connectedness approaches using a rolling-window method to examine spillover dynamics. The empirical results reveal that return spillovers increase when asset returns deviate from normal market conditions, and volatility spillovers are particularly increased during bullish market conditions. Moreover, the study finds that under bearish and normal market conditions, Bitcoin is a major recipient of return spillovers from all futures, and crude oil and copper are major transmitters of return spillovers to natural gas and gold, respectively. However, during bullish market states, Bitcoin becomes a major transmitter of return spillovers to other futures. Under unstable market conditions, gold is a major transmitter of volatility spillover to crude oil and natural gas. Furthermore, the directional link from Bitcoin to other futures is stronger when market issues exist.

Suggested Citation

  • Joo, Young C. & Park, Sung Y., 2023. "Quantile connectedness between cryptocurrency and commodity futures," Finance Research Letters, Elsevier, vol. 58(PC).
    • Handle: RePEc:eee:finlet:v:58:y:2023:i:pc:s1544612323008449
    DOI: 10.1016/j.frl.2023.104472
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    More about this item

    Keywords

    Quantile connectedness; Spillovers; Commodity futures; Cryptocurrency;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • 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
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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