2022/07/26 by Christa Cuchiero, Cuchiero, Christa, Guido Gazzani +4 · 20 citations
Economics, Econometrics and Finance · Engineering · Mathematics · #62P05 #65C20 #91B70 #Capital Investment and Risk Analysis #Computational Finance (q-fin.CP) #FOS: Economics and business #FOS: Mathematics #Mathematical Finance (q-fin.MF) #Probability (math.PR) #Reservoir Engineering and Simulation Methods #Stochastic processes and financial applications #math.PR #msc:62P05 #msc:65C20 #msc:91B70 #q-fin.CP #q-fin.MF
paper · pdf · doi:10.48550/arxiv.2207.13136
arxiv created 2022/07/26 · openalex publication_date 2022/07/26 · arxiv updated 2022/07/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We consider asset price models whose dynamics are described by linear functions of the (time extended) signature of a primary underlying process, which can range from a (market-inferred) Brownian motion to a general multidimensional continuous semimartingale. The framework is universal in the sense that classical models can be approximated arbitrarily well and that the model's parameters can be learned from all sources of available data by simple methods. We provide conditions guaranteeing absence of arbitrage as well as tractable option pricing formulas for so-called sig-payoffs, exploiting the polynomial nature of generic primary processes. One of our main focus lies on calibration, where we consider both time-series and implied volatility surface data, generated from classical stochastic volatility models and also from S&P500 index market data. For both tasks the linearity of the model turns out to be the crucial tractability feature which allows to get fast and accurate calibrations results.