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Modeling dynamic volatility under uncertain environment with fuzziness and randomness

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Listed:
  • Xianfei Hui
  • Baiqing Sun
  • Hui Jiang
  • Yan Zhou

Abstract

The problem related to predicting dynamic volatility in financial market plays a crucial role in many contexts. We build a new generalized Barndorff-Nielsen and Shephard (BN-S) model suitable for uncertain environment with fuzziness and randomness. This new model considers the delay phenomenon between price fluctuation and volatility changes, solves the problem of the lack of long-range dependence of classic models. Through the experiment of Dow Jones futures price, we find that compared with the classical model, this method effectively combines the uncertain environmental characteristics, which makes the prediction of dynamic volatility has more ideal performance.

Suggested Citation

  • Xianfei Hui & Baiqing Sun & Hui Jiang & Yan Zhou, 2022. "Modeling dynamic volatility under uncertain environment with fuzziness and randomness," Papers 2204.12657, arXiv.org, revised Oct 2022.
  • Handle: RePEc:arx:papers:2204.12657
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    References listed on IDEAS

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    1. Petra Posedel Šimović & Azra Tafro, 2021. "Pricing the Volatility Risk Premium with a Discrete Stochastic Volatility Model," Mathematics, MDPI, vol. 9(17), pages 1-15, August.
    2. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    3. Ole E. Barndorff‐Nielsen & Neil Shephard, 2002. "Econometric analysis of realized volatility and its use in estimating stochastic volatility models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 253-280, May.
    4. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    5. Humayra Shoshi & Indranil SenGupta, 2020. "Hedging and machine learning driven crude oil data analysis using a refined Barndorff-Nielsen and Shephard model," Papers 2004.14862, arXiv.org, revised Feb 2021.
    6. Yang, Ben-Zhang & Yue, Jia & Wang, Ming-Hui & Huang, Nan-Jing, 2019. "Volatility swaps valuation under stochastic volatility with jumps and stochastic intensity," Applied Mathematics and Computation, Elsevier, vol. 355(C), pages 73-84.
    7. Michael Grabchak & Eliana Christou, 2021. "A note on calculating expected shortfall for discrete time stochastic volatility models," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-16, December.
    8. Shantanu Awasthi & Indranil SenGupta, 2020. "First exit-time analysis for an approximate Barndorff-Nielsen and Shephard model with stationary self-decomposable variance process," Papers 2006.07167, arXiv.org, revised Jan 2021.
    9. Marwan Izzeldin & Mike G. Tsionas & Panayotis G. Michaelides, 2019. "Multivariate stochastic volatility with large and moderate shocks," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(3), pages 887-917, June.
    10. Chen, Rongda & Zhou, Hanxian & Yu, Lean & Jin, Chenglu & Zhang, Shuonan, 2021. "An efficient method for pricing foreign currency options," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
    11. Nicholas Salmon & Indranil SenGupta, 2021. "Fractional Barndorff-Nielsen and Shephard model: applications in variance and volatility swaps, and hedging," Papers 2105.02325, arXiv.org.
    12. Shapiro, Arnold F., 2009. "Fuzzy random variables," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 307-314, April.
    13. Indranil SenGupta & William Nganje & Erik Hanson, 2021. "Refinements of Barndorff-Nielsen and Shephard Model: An Analysis of Crude Oil Price with Machine Learning," Annals of Data Science, Springer, vol. 8(1), pages 39-55, March.
    14. Corsaro, Stefania & Kyriakou, Ioannis & Marazzina, Daniele & Marino, Zelda, 2019. "A general framework for pricing Asian options under stochastic volatility on parallel architectures," European Journal of Operational Research, Elsevier, vol. 272(3), pages 1082-1095.
    15. Baule, Rainer & Shkel, David, 2021. "Model risk and model choice in the case of barrier options and bonus certificates," Journal of Banking & Finance, Elsevier, vol. 133(C).
    16. Bian, Liu & Li, Zhi, 2021. "Fuzzy simulation of European option pricing using sub-fractional Brownian motion," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    17. Li, Yuze & Jiang, Shangrong & Li, Xuerong & Wang, Shouyang, 2021. "The role of news sentiment in oil futures returns and volatility forecasting: Data-decomposition based deep learning approach," Energy Economics, Elsevier, vol. 95(C).
    18. Nicholas Salmon & Indranil SenGupta, 2021. "Fractional Barndorff-Nielsen and Shephard model: applications in variance and volatility swaps, and hedging," Annals of Finance, Springer, vol. 17(4), pages 529-558, December.
    19. Samitas, Aristeidis & Kampouris, Elias & Kenourgios, Dimitris, 2020. "Machine learning as an early warning system to predict financial crisis," International Review of Financial Analysis, Elsevier, vol. 71(C).
    20. Ole E. Barndorff‐Nielsen & Neil Shephard, 2001. "Non‐Gaussian Ornstein–Uhlenbeck‐based models and some of their uses in financial economics," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 167-241.
    21. Xiaoyu Tan & Shenghong Li & Shuyi Wang, 2020. "Pricing European-Style Options in General Lévy Process with Stochastic Interest Rate," Mathematics, MDPI, vol. 8(5), pages 1-10, May.
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