A GMM approach to estimate the roughness of stochastic volatility
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Citations
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- Li, Jia & Phillips, Peter C. B. & Shi, Shuping & Yu, Jun, 2022.
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- Jia Li & Peter C. B. Phillips & Shuping Shi & Jun Yu, 2022. "Weak Identification of Long Memory with Implications for Inference," Cowles Foundation Discussion Papers 2334, Cowles Foundation for Research in Economics, Yale University.
- Angelini, Daniele & Bianchi, Sergio, 2023. "Nonlinear biases in the roughness of a Fractional Stochastic Regularity Model," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
- Carsten Chong & Marc Hoffmann & Yanghui Liu & Mathieu Rosenbaum & Gr'egoire Szymanski, 2022. "Statistical inference for rough volatility: Central limit theorems," Papers 2210.01216, arXiv.org, revised Jul 2023.
- Shuping Shi & Jun Yu, 2023. "Volatility Puzzle: Long Memory or Antipersistency," Management Science, INFORMS, vol. 69(7), pages 3861-3883, July.
- Ofelia Bonesini & Antoine Jacquier & Alexandre Pannier, 2023. "Rough volatility, path-dependent PDEs and weak rates of convergence," Papers 2304.03042, arXiv.org.
- Peter Christensen, 2024. "Roughness Signature Functions," Papers 2401.02819, arXiv.org.
- Saad Mouti, 2023. "Rough volatility: evidence from range volatility estimators," Papers 2312.01426, arXiv.org.
- Alexandre Pannier, 2023. "Path-dependent PDEs for volatility derivatives," Papers 2311.08289, arXiv.org, revised Jan 2024.
- Carsten Chong & Marc Hoffmann & Yanghui Liu & Mathieu Rosenbaum & Gr'egoire Szymanski, 2022. "Statistical inference for rough volatility: Minimax Theory," Papers 2210.01214, arXiv.org, revised Feb 2024.
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