Research Seminar Series in Statistics and Mathematics
The Institute for Statistics and Mathematics (Department of Finance, Accounting and Statistics) cordially invites everyone interested to attend the talks in our Research Seminar Series, where internationally renowned scholars from leading universities present and discuss their (working) papers.
The list of talks for the winter term 2018/19 is available via the following link: https://www.wu.ac.at/en/statmath/resseminar
Stochastic volatility modeling has been in the center of finance and econometrics since the groundbreaking results of Black & Scholes and Merton on their famous deterministic volatility model. It is a very natural approach to resolve the shortcomings of the Black-Scholes-Merton model and to match both time series and option data much more accurately. In the last few years, the by now classical continuous time stochastic volatility models based on low dimensional diffusions, e.g. the Heston or the SABR model, have been challenged and might be fully replaced by two modern developments, namely rough volatility and local stochastic volatility.
The rough volatility paradigm asserts that the trajectories of assets' volatility are rougher than Brownian motion, a revolutionary perspective that has radically changed certain persistent paradigms. It considers volatility as stochastic Volterra process and provides a universal approach to capture econometric and microstructural foundations of markets.
Rough volatility is complemented by local stochastic volatility which combines classical stochastic volatility with perfect calibration to the implied volatility smiles or skews, a theoretically and practically still very intricate task. In this talk we provide a novel infinite dimensional point of view on both directions. It allows to dissolve a generic non-Markovanity of the at first sight naturally low dimensional volatility process. This approach enables in particular to treat the challenging problem of multivariate rough covariance models for more than one asset. We also consider (non-parametric) estimation techniques and tread new paths to calibration by using machine learning methods.
Kindly note that on November 16 two talks are scheduled at our institute:
9:00 – 10:10 Clara Grazian (University of Oxford)
11:00 – 12:10 Christa Cuchiero (University of Vienna)
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