Zobrazeno 1 - 10
of 2 954
pro vyhledávání: '"Gonon, A."'
Hedging exotic options in presence of market frictions is an important risk management task. Deep hedging can solve such hedging problems by training neural network policies in realistic simulated markets. Training these neural networks may be delica
Externí odkaz:
http://arxiv.org/abs/2410.22568
This paper investigates systemic risk measures for stochastic financial networks of explicitly modelled bilateral liabilities. We extend the notion of systemic risk measures from Biagini, Fouque, Fritelli and Meyer-Brandis (2019) to graph structured
Externí odkaz:
http://arxiv.org/abs/2410.07222
Autor:
Gonon, Lukas, Jentzen, Arnulf, Kuckuck, Benno, Liang, Siyu, Riekert, Adrian, von Wurstemberger, Philippe
The approximation of solutions of partial differential equations (PDEs) with numerical algorithms is a central topic in applied mathematics. For many decades, various types of methods for this purpose have been developed and extensively studied. One
Externí odkaz:
http://arxiv.org/abs/2408.13222
We present a unified theory for Mahalanobis-type anomaly detection on Banach spaces, using ideas from Cameron-Martin theory applied to non-Gaussian measures. This approach leads to a basis-free, data-driven notion of anomaly distance through the so-c
Externí odkaz:
http://arxiv.org/abs/2407.11873
We devise a novel method for nowcasting implied volatility based on neural operators. Better known as implied volatility smoothing in the financial industry, nowcasting of implied volatility means constructing a smooth surface that is consistent with
Externí odkaz:
http://arxiv.org/abs/2406.11520
Randomised signature has been proposed as a flexible and easily implementable alternative to the well-established path signature. In this article, we employ randomised signature to introduce a generative model for financial time series data in the sp
Externí odkaz:
http://arxiv.org/abs/2406.10214
This paper benchmarks and improves existing GPU matrix multiplication algorithms specialized for Kronecker-sparse matrices, whose sparsity patterns are described by Kronecker products. These matrices have recently gained popularity as replacements fo
Externí odkaz:
http://arxiv.org/abs/2405.15013
Analyzing the behavior of ReLU neural networks often hinges on understanding the relationships between their parameters and the functions they implement. This paper proves a new bound on function distances in terms of the so-called path-metrics of th
Externí odkaz:
http://arxiv.org/abs/2405.15006
This work introduces the first toolkit around path-norms that fully encompasses general DAG ReLU networks with biases, skip connections and any operation based on the extraction of order statistics: max pooling, GroupSort etc. This toolkit notably al
Externí odkaz:
http://arxiv.org/abs/2310.01225
We derive quantitative error bounds for deep neural networks (DNNs) approximating option prices on a $d$-dimensional risky asset as functions of the underlying model parameters, payoff parameters and initial conditions. We cover a general class of st
Externí odkaz:
http://arxiv.org/abs/2309.14784