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Stochastic resonance of drawdown risk in energy market prices

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  • Dong, Yang
  • Wen, Shu-hui
  • Hu, Xiao-bing
  • Li, Jiang-Cheng

Abstract

We investigate the stochastic resonance dynamic behavior characteristics of price drawdown time series driven by internal and external periodic information and discuss the influence of information resonance on price drawdown risk, based on price drawdown, periodic Heston model and signal power amplification. A theoretical model of the periodic price drawdown time series is proposed to describe the energy price drawdown time series with the periodic Heston model. Then the signal power amplification (SPA) is employed to measure the stochastic resonance phenomenon in the internal and external periodic information environment of complex energy dynamics system. Combined with the real data of WTI spot price of NYMEX, the least square method of drawdown time series distribution is used to estimate the parameters of the model. The probability density functions of drawdown between the theoretical model and the real data are compared, and a good agreement can be found between both the simulated data from the proposed model and the real data. After the stochastic simulation of signal power amplification under internal and external periodic information respectively, the results show that: (i) In the functions of SPA versus price volatility parameters (noise correlation strength or periodic information strength), inverse resonance phenomenon can be observed, that is to say, there is the best system price volatility parameter, noise correlation strength and information strength corresponding to the least risk of the price drawdown; (ii) In the SPA versus amplitude of volatility fluctuations, the correlation induce multiple inverse stochastic resonance phenomenon can be observed; (iii) The increase of growth rate strengthens SPA and weakens drawdown risk of energy market prices.

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  • Dong, Yang & Wen, Shu-hui & Hu, Xiao-bing & Li, Jiang-Cheng, 2020. "Stochastic resonance of drawdown risk in energy market prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    • Handle: RePEc:eee:phsmap:v:540:y:2020:i:c:s0378437119317479
    DOI: 10.1016/j.physa.2019.123098
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    2. Bianca Reichert & Adriano Mendon a Souza, 2022. "Can the Heston Model Forecast Energy Generation? A Systematic Literature Review," International Journal of Energy Economics and Policy, Econjournals, vol. 12(1), pages 289-295.

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