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Jump activity analysis for affine jump-diffusion models: Evidence from the commodity market

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  • Da Fonseca, José
  • Ignatieva, Katja

Abstract

This study performs a joint analysis of jump activity for commodities and their respective volatility indexes; it also compares the results thereof to those for equities. Exploiting a property of affine jump-diffusion models (i.e., that a volatility index quoted on the market is an affine function of the instantaneous volatility state variable), we perform a test of common jumps for multidimensional processes to assess whether an asset and its volatility jump together. Applying this test to the crude oil pair USO/OVX, the gold pair GLD/GVZ, the S&P500/VIX, and three stock/volatility index pairs, we find strong evidence that these assets and their respective volatility indexes do not jump together. However, a copula analysis shows that for the equity index and individual stocks, there is a dependency between the jump sizes in the asset and in the volatility index. In contrast, for the commodity market, this dependency occurs only after decomposing the jump sizes that affect the asset into positive and negative components.

Suggested Citation

  • Da Fonseca, José & Ignatieva, Katja, 2019. "Jump activity analysis for affine jump-diffusion models: Evidence from the commodity market," Journal of Banking & Finance, Elsevier, vol. 99(C), pages 45-62.
  • Handle: RePEc:eee:jbfina:v:99:y:2019:i:c:p:45-62
    DOI: 10.1016/j.jbankfin.2018.11.014
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    Cited by:

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    2. Kang, Boda & Nikitopoulos, Christina Sklibosios & Prokopczuk, Marcel, 2020. "Economic determinants of oil futures volatility: A term structure perspective," Energy Economics, Elsevier, vol. 88(C).
    3. Ye, Wuyi & Xia, Wenjing & Wu, Bin & Chen, Pengzhan, 2022. "Using implied volatility jumps for realized volatility forecasting: Evidence from the Chinese market," International Review of Financial Analysis, Elsevier, vol. 83(C).
    4. Wang, Haiying & Yuan, Ying & Li, Yiou & Wang, Xunhong, 2021. "Financial contagion and contagion channels in the forex market: A new approach via the dynamic mixture copula-extreme value theory," Economic Modelling, Elsevier, vol. 94(C), pages 401-414.
    5. Liu, Feng & Shao, Shuai & Li, Xin & Pan, Na & Qi, Yu, 2023. "Economic policy uncertainty, jump dynamics, and oil price volatility," Energy Economics, Elsevier, vol. 120(C).
    6. Bouri, Elie & Roubaud, David & Shahzad, Syed Jawad Hussain, 2020. "Do Bitcoin and other cryptocurrencies jump together?," The Quarterly Review of Economics and Finance, Elsevier, vol. 76(C), pages 396-409.
    7. Bin Wu & Pengzhan Chen & Wuyi Ye, 2021. "Jump activity analysis of the equity index and the corresponding volatility: Evidence from the Chinese market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(7), pages 1055-1073, July.
    8. Imen Bedoui-Belghith & Slaheddine Hallara & Faouzi Jilani, 2023. "Crisis transmission degree measurement under crisis propagation model," SN Business & Economics, Springer, vol. 3(1), pages 1-27, January.
    9. Ignatieva, Katja & Wong, Patrick, 2022. "Modelling high frequency crude oil dynamics using affine and non-affine jump–diffusion models," Energy Economics, Elsevier, vol. 108(C).
    10. Jiling Cao & Xinfeng Ruan & Wenjun Zhang, 2020. "Inferring information from the S&P 500, CBOE VIX, and CBOE SKEW indices," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(6), pages 945-973, June.

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    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
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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