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ANR EFFI France-Japan seminar

As part of the ANR 2022-2025 project "Efficient inference for large and high-frequency data", the fourth seminar will be held both online and in presential, starting Tuesday December 5th, 2023 from 9:15 a.m. to 11:30 a.m. (Paris time). 


9h15-10h15, Teppei Ogihara, University of Tokyo, Local asymptotic normality for discretely observed jump-diffusion processes

Abstract: We consider efficient parameter estimation for a parametric jump-diffusion process model using the local asymptotic normality. To establish the local asymptotic normality for diffusion processes as presented in Gobet (AIHP PS 2002), the Aronson estimates played a crucial role in the proof. However, obtaining Aronson-type estimates for jump-diffusion processes is challenging.  Instead, we employ a scheme that leverages the $L^2$ regularity condition from Jeganathan (Sankhya Ser. A 1982), eliminating the need for Aronson-type estimates. Our talk is based on joint research with Yuma Uehara at Kansai University.

10h30-11h30, Grégoire Szymanski, École Polytechnique, Statistical inference for rough volatility

Abstract: 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 and we build a consistent estimator for $H$ satisfying a CLT. We also discuss the numerical implementation of this estimator and apply it to high-frequency financial data.

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