Imanol Pérez Arribas
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View article: Non-parametric Pricing and Hedging of Exotic Derivatives
Non-parametric Pricing and Hedging of Exotic Derivatives Open
In the spirit of Arrow-Debreu, we introduce a family of financial derivatives that act as primitive securities in that exotic derivatives can be approximated by their linear combinations. We call these financial derivatives signature payof…
View article: Sig-SDEs model for quantitative finance
Sig-SDEs model for quantitative finance Open
Mathematical models, calibrated to data, have become ubiquitous to make key decision processes in modern quantitative finance. In this work, we propose a novel framework for data-driven model selection by integrating a classical quantitati…
View article: A Data-driven Market Simulator for Small Data Environments
A Data-driven Market Simulator for Small Data Environments Open
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. Though in this sense generative market simulation is m…
View article: Anomaly detection on streamed data
Anomaly detection on streamed data Open
We introduce powerful but simple methodology for identifying anomalous observations against a corpus of `normal' observations. All data are observed through a vector-valued feature map. Our approach depends on the choice of corpus and that…
View article: Optimal Execution with Rough Path Signatures
Optimal Execution with Rough Path Signatures Open
We present a method for obtaining approximate solutions to the problem of optimal execution, based on a signature method. The framework is general, only requiring that the price process is a geometric rough path and the price impact functi…
View article: Deep Signature Transforms
Deep Signature Transforms Open
The signature is an infinite graded sequence of statistics known to characterise a stream of data up to a negligible equivalence class. It is a transform which has previously been treated as a fixed feature transformation, on top of which …
View article: Deep Signatures
Deep Signatures Open
The signature is an infinite graded sequence of statistics known to characterise a stream of data up to a negligible equivalence class. It is a transform which has previously been treated as a fixed feature transformation, on top of which …
View article: Numerical method for model-free pricing of exotic derivatives using rough path signatures
Numerical method for model-free pricing of exotic derivatives using rough path signatures Open
We estimate prices of exotic options in a discrete-time model-free setting when the trader has access to market prices of a rich enough class of exotic and vanilla options. This is achieved by estimating an unobservable quantity called "im…
View article: Model-free pricing and hedging in discrete time using rough path signatures
Model-free pricing and hedging in discrete time using rough path signatures Open
We make use of a family of primitive securities, in the spirit of Arrow-Debreu, to price and hedge in a model-free way path-dependent exotic derivatives in discrete time. These primitive securities are called signature payoffs. First, we s…
View article: Deep Signature Transforms
Deep Signature Transforms Open
The signature is an infinite graded sequence of statistics known to characterise a stream of data up to a negligible equivalence class. It is a transform which has previously been treated as a fixed feature transformation, on top of which …
View article: Derivatives pricing using signature payoffs
Derivatives pricing using signature payoffs Open
We introduce signature payoffs, a family of path-dependent derivatives that are given in terms of the signature of the price path of the underlying asset. We show that these derivatives are dense in the space of continuous payoffs, a resul…
View article: Labelling as an unsupervised learning problem
Labelling as an unsupervised learning problem Open
Unravelling hidden patterns in datasets is a classical problem with many potential applications. In this paper, we present a challenge whose objective is to discover nonlinear relationships in noisy cloud of points. If a set of point satis…