2020
DOI: 10.48550/arxiv.2006.14498
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A Data-driven Market Simulator for Small Data Environments

Abstract: 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, the concrete modelling choices are nevertheless decisive for the features of the simulated paths. We give a brief overview of currently used generative modelling approaches and performance evaluation metrics for financial time series, and address some of the chall… Show more

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Cited by 6 publications
(12 citation statements)
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“…In order to achieve this, raw financial data needs to undergo transformations to be modelled without the hindrances of stylized facts and model assumptions need to be examined in order to justify their usage. Possible transformations include those described as quest for invariants in [23] or the reformulation into their signatures as proposed in [18].…”
Section: Choice Of Filtrationmentioning
confidence: 99%
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“…In order to achieve this, raw financial data needs to undergo transformations to be modelled without the hindrances of stylized facts and model assumptions need to be examined in order to justify their usage. Possible transformations include those described as quest for invariants in [23] or the reformulation into their signatures as proposed in [18].…”
Section: Choice Of Filtrationmentioning
confidence: 99%
“…Classically, comparison between the similarity of distributional properties relies on statistical measures such as QQ Plots, Tail Behaviour measures, correlation measures etc as used in [11] or [30]. These approaches however might face a variety of problems as described in [18], given by different usages of the scenario generation (see examples from introduction), be it optimization with respect to certain options or a portfolio performance. Other difficulties in performance evaluation arise for example also due to the non continuity of the given data or the intractability of the true underlying distribution.…”
Section: Choice Of Evaluationmentioning
confidence: 99%
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“…To address this data scarcity challenge, there has been much work in the area of using generative machine learning models to simulate realistic samples from the same distribution as the historical market data. To name a few: (Arribas, Salvi, and Szpruch 2020;Buehler et al 2020;Wang 2021, 2022;Cuchiero, Khosrawi, and Teichmann 2020;De Meer Pardo, Schwendner, and Wunsch 2021;Gierjatowicz et al 2020;Ni et al 2020Ni et al , 2021Wiese et al 2019Wiese et al , 2020.…”
Section: Introductionmentioning
confidence: 99%