On the universality of the volatility formation process: when machine learning and rough volatility agree

DOI10.3934/fmf.2024002zbMath1537.91304MaRDI QIDQ6549691

Mathieu Rosenbaum, Jian-Fei Zhang

Publication date: 4 June 2024

Published in: Frontiers of Mathematical Finance (Search for Journal in Brave)

zbMATH Keywords

universalityrough volatilityLSTMZumbach effectquadratic rough Hestonvolatility formation

Mathematics Subject Classification ID

Applications of statistics to actuarial sciences and financial mathematics (62P05) Artificial neural networks and deep learning (68T07) Fractional processes, including fractional Brownian motion (60G22) Financial markets (91G15)

Cites Work

• The Model Confidence Set
• Volatility forecast comparison using imperfect volatility proxies
• Perfect hedging in rough Heston models
• The microstructural foundations of leverage effect and rough volatility
• The fine structure of volatility feedback. II: Overnight and intra-day effects
• The fine-structure of volatility feedback. I: Multi-scale self-reflexivity
• From rough to multifractal volatility: the log S-fBm model
• ESTIMATORS FOR LONG-RANGE DEPENDENCE: AN EMPIRICAL STUDY
• Volatility conditional on price trends
• Volatility is rough
• Quadratic Hawkes processes for financial prices
• Apparent criticality and calibration issues in the Hawkes self-excited point process model: application to high-frequency financial data
• Pricing under rough volatility
• Universal features of price formation in financial markets: perspectives from deep learning
• The characteristic function of rough Heston models
• No‐arbitrage implies power‐law market impact and rough volatility
• Volatility is (mostly) path-dependent
• A GMM approach to estimate the roughness of stochastic volatility

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