8 papers
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…