Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Arribas P. Pérez"'
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 model-free,
Externí odkaz:
http://arxiv.org/abs/2006.14498
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 quantitative setup wi
Externí odkaz:
http://arxiv.org/abs/2006.00218
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 a model may
Externí odkaz:
http://arxiv.org/abs/1905.08494
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 "implied expec
Externí odkaz:
http://arxiv.org/abs/1905.01720
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 function is a con
Externí odkaz:
http://arxiv.org/abs/1905.00728
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 payoffs. We show
Externí odkaz:
http://arxiv.org/abs/1905.00711
Autor:
Arribas, Imanol Perez
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 result that is e
Externí odkaz:
http://arxiv.org/abs/1809.09466
Autor:
Lyons, Terry, Arribas, Imanol Perez
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 satisfies a nonl
Externí odkaz:
http://arxiv.org/abs/1805.03911
Mobile technologies offer opportunities for higher resolution monitoring of health conditions. This opportunity seems of particular promise in psychiatry where diagnoses often rely on retrospective and subjective recall of mood states. However, getti
Externí odkaz:
http://arxiv.org/abs/1707.07124
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