5 citations · 9 across the 10 of their papers we have counts for
19 papers
Signature Trading: A Path-Dependent Extension of the Mean-Variance Framework with Exogenous Signals
Owen Futter, Blanka Horvath, Magnus Wiese
In this article we introduce a portfolio optimisation framework, in which the use of rough path signatures (Lyons, 1998) provides a novel method of incorporating path-dependencies…
Robust Hedging GANs
Yannick Limmer, Blanka Horvath
The availability of deep hedging has opened new horizons for solving hedging problems under a large variety of realistic market conditions. At the same time, any model - be it a tr…
Non-parametric online market regime detection and regime clustering for multidimensional and path-dependent data structures
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 di…
Non-adversarial training of Neural SDEs with signature kernel scores
Zacharia Issa, Blanka Horvath, Maud Lemercier +1
Neural SDEs are continuous-time generative models for sequential data. State-of-the-art performance for irregular time series generation has been previously obtained by training th…
Optimal Stopping via Distribution Regression: a Higher Rank Signature Approach
Blanka Horvath, Maud Lemercier, Chong Liu +2
Distribution Regression on path-space refers to the task of learning functions mapping the law of a stochastic process to a scalar target. The learning procedure based on the notio…
Clustering Market Regimes using the Wasserstein Distance
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. In this paper, we outline…
Hedging under rough volatility
Masaaki Fukasawa, Blanka Horvath, Peter Tankov
In this chapter we first briefly review the existing approaches to hedging in rough volatility models. Next, we present a simple but general result which shows that in a one-factor…
Deep Hedging under Rough Volatility
Blanka Horvath, Josef Teichmann, Zan Zuric
We investigate the performance of the Deep Hedging framework under training paths beyond the (finite dimensional) Markovian setup. In particular we analyse the hedging performance…
A Data-driven Market Simulator for Small Data Environments
Hans Bühler, Blanka Horvath, Terry Lyons +2
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 dynam…
On deep calibration of (rough) stochastic volatility models
Christian Bayer, Blanka Horvath, Aitor Muguruza +2
Techniques from deep learning play a more and more important role for the important task of calibration of financial models. The pioneering paper by Hernandez [Risk, 2017] was a ca…
Deep Learning Volatility
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. The framework is consiste…
Volatility options in rough volatility models
Blanka Horvath, Antoine Jacquier, Peter Tankov
We discuss the pricing and hedging of volatility options in some rough volatility models. First, we develop efficient Monte Carlo methods and asymptotic approximations for computin…
Dirichlet Forms and Finite Element Methods for the SABR Model
Blanka Horvath, Oleg Reichmann
We propose a deterministic numerical method for pricing vanilla options under the SABR stochastic volatility model, based on a finite element discretization of the Kolmogorov prici…
Functional central limit theorems for rough volatility
Blanka Horvath, Antoine Jacquier, Aitor Muguruza +1
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.…
Asymptotic behaviour of randomised fractional volatility models
B. Horvath, A. Jacquier, C. Lacombe
We study the asymptotic behaviour of a class of small-noise diffusions driven by fractional Brownian motion, with random starting points. Different scalings allow for different asy…
Short-time near-the-money skew in rough fractional volatility models
Christian Bayer, Peter K. Friz, Archil Gulisashvili +2
We consider rough stochastic volatility models where the driving noise of volatility has fractional scaling, in the "rough" regime of Hurst parameter . This regime recentl…
Functional Analytic (Ir-)Regularity Properties of SABR-type Processes
Leif Doering, Blanka Horvath, Josef Teichmann
The SABR model is a benchmark stochastic volatility model in interest rate markets, which has received much attention in the past decade. Its popularity arose from a tractable asym…
On the probability of hitting the boundary for Brownian motions on the SABR plane
Archil Gulisashvili, Blanka Horvath, Antoine Jacquier
Starting from the hyperbolic Brownian motion as a time-changed Brownian motion, we explore a set of probabilistic models--related to the SABR model in mathematical finance--which c…
Mass at zero in the uncorrelated SABR model and implied volatility asymptotics
Archil Gulisashvili, Blanka Horvath, Antoine Jacquier
We study the mass at the origin in the uncorrelated SABR stochastic volatility model, and derive several tractable expressions, in particular when time becomes small or large. As a…