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Found 11 papers, 4 papers with code
Signature Trading: A Path-Dependent Extension of the Mean-Variance Framework with Exogenous Signals
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").
Robust Hedging GANs
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.
Non-parametric online market regime detection and regime clustering for multidimensional and path-dependent data structures
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.
Non-adversarial training of Neural SDEs with signature kernel scores
Neural SDEs are continuous-time generative models for sequential data.
Clustering Market Regimes using the Wasserstein Distance
The problem of rapid and automated detection of distinct market regimes is a topic of great interest to financial mathematicians and practitioners alike.
Hedging under rough volatility
In this chapter we first briefly review the existing approaches to hedging in rough volatility models.
Deep Hedging under Rough Volatility
We investigate the performance of the Deep Hedging framework under training paths beyond the (finite dimensional) Markovian setup.
A Data-driven Market Simulator for Small Data Environments
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.
Deep Learning Volatility
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
Volatility options in rough volatility models
We discuss the pricing and hedging of volatility options in some rough volatility models.
Functional central limit theorems for rough volatility
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
