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A regularity structure for rough volatility

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arxiv 1710.07481 v1 pith:L2YGAIUV submitted 2017-10-20 q-fin.PR math.PR

A regularity structure for rough volatility

classification q-fin.PR math.PR
keywords volatilityroughstochasticmodelsregularityanalyzecapturecaused
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A new paradigm recently emerged in financial modelling: rough (stochastic) volatility, first observed by Gatheral et al. in high-frequency data, subsequently derived within market microstructure models, also turned out to capture parsimoniously key stylized facts of the entire implied volatility surface, including extreme skews that were thought to be outside the scope of stochastic volatility. On the mathematical side, Markovianity and, partially, semi-martingality are lost. In this paper we show that Hairer's regularity structures, a major extension of rough path theory, which caused a revolution in the field of stochastic partial differential equations, also provides a new and powerful tool to analyze rough volatility models.

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  1. Piecewise Symmetric Tensors

    math.RT 2026-07 accept novelty 7.0

    Every k-tensor is the unique sum of an m-piecewise-symmetric tensor and a (k-m)-piecewise-alternating tensor; the latter space is the annihilator of level-k signatures of m-segment paths.