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Pré-Publication, Document De Travail Année : 2023

STATISTICAL INFERENCE FOR ROUGH VOLATILITY: MINIMAX THEORY

Résumé

Rough volatility models have gained considerable interest in the quantitative finance community in recent years. In this paradigm, the volatility of the asset price is driven by a fractional Brownian motion with a small value for the Hurst parameter H. In this work, we provide a rigorous statistical analysis of these models. To do so, we establish minimax lower bounds for parameter estimation and design procedures based on wavelets attaining them. We notably obtain an optimal speed of convergence of n −1/(4H+2) for estimating H based on n sampled data, extending results known only for the easier case H > 1/2 so far. We therefore establish that the parameters of rough volatility models can be inferred with optimal accuracy in all regimes.
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Dates et versions

hal-03949577 , version 1 (20-01-2023)

Identifiants

  • HAL Id : hal-03949577 , version 1

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Carsten Chong, Marc Hoffmann, Yanghui Liu, Grégoire Szymanski, Mathieu Rosenbaum. STATISTICAL INFERENCE FOR ROUGH VOLATILITY: MINIMAX THEORY. 2023. ⟨hal-03949577⟩
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