no code implementations • 29 Aug 2023 • Owen Futter, Blanka Horvath, Magnus Wiese
We achieve this by representing a trading strategy as a linear functional applied to the signature of a path (which we refer to as "Signature Trading" or "Sig-Trading").
no code implementations • 5 Jul 2023 • Yannick Limmer, Blanka Horvath
This is achieved through an interplay of three modular components: (i) a (deep) hedging engine, (ii) a data-generating process (that is model agnostic permitting a large variety of classical models as well as machine learning-based market generators), and (iii) a notion of distance on model space to measure deviations between our market prognosis and reality.
1 code implementation • 27 Jun 2023 • Zacharia Issa, Blanka Horvath
In this work we present a non-parametric online market regime detection method for multidimensional data structures using a path-wise two-sample test derived from a maximum mean discrepancy-based similarity metric on path space that uses rough path signatures as a feature map.
1 code implementation • NeurIPS 2023 • Zacharia Issa, Blanka Horvath, Maud Lemercier, Cristopher Salvi
Neural SDEs are continuous-time generative models for sequential data.
no code implementations • 22 Oct 2021 • Blanka Horvath, Zacharia Issa, Aitor Muguruza
The problem of rapid and automated detection of distinct market regimes is a topic of great interest to financial mathematicians and practitioners alike.
no code implementations • 10 May 2021 • Masaaki Fukasawa, Blanka Horvath, Peter Tankov
In this chapter we first briefly review the existing approaches to hedging in rough volatility models.
no code implementations • 3 Feb 2021 • Blanka Horvath, Josef Teichmann, Zan Zuric
We investigate the performance of the Deep Hedging framework under training paths beyond the (finite dimensional) Markovian setup.
no code implementations • 21 Jun 2020 • Hans Bühler, Blanka Horvath, Terry Lyons, Imanol Perez Arribas, Ben Wood
Neural network based data-driven market simulation unveils a new and flexible way of modelling financial time series without imposing assumptions on the underlying stochastic dynamics.
2 code implementations • 28 Jan 2019 • Blanka Horvath, Aitor Muguruza, Mehdi Tomas
We present a neural network based calibration method that performs the calibration task within a few milliseconds for the full implied volatility surface.
Mathematical Finance
no code implementations • 5 Feb 2018 • Blanka Horvath, Antoine Jacquier, Peter Tankov
We discuss the pricing and hedging of volatility options in some rough volatility models.
1 code implementation • 8 Nov 2017 • Blanka Horvath, Antoine Jacquier, Aitor Muguruza
The non-Markovian nature of rough volatility processes makes Monte Carlo methods challenging and it is in fact a major challenge to develop fast and accurate simulation algorithms.
Probability Pricing of Securities 60F17, 60F05, 60G15, 60G22, 91G20, 91G60, 91B25