@article{RitschelCherstvyMetzler2021,
  author    = {Ritschel, Stefan and Cherstvy, Andrey G. and Metzler, Ralf},
  title     = {Universality of delay-time averages for financial time series},
  series = {Journal of physics. Complexity},
  volume    = {2},
  journal   = {Journal of physics. Complexity},
  number    = {4},
  publisher = {IOP Publ. Ltd.},
  address   = {Bristol},
  issn      = {2632-072X},
  doi       = {10.1088/2632-072X/ac2220},
  pages     = {30},
  year      = {2021},
  abstract  = {We analyze historical data of stock-market prices for multiple financial indices using the concept of delay-time averaging for the financial time series (FTS). The region of validity of our recent theoretical predictions [Cherstvy A G et al 2017 New J. Phys. 19 063045] for the standard and delayed time-averaged mean-squared 'displacements' (TAMSDs) of the historical FTS is extended to all lag times. As the first novel element, we perform extensive computer simulations of the stochastic differential equation describing geometric Brownian motion (GBM) which demonstrate a quantitative agreement with the analytical long-term price-evolution predictions in terms of the delayed TAMSD (for all stock-market indices in crisis-free times). Secondly, we present a robust procedure of determination of the model parameters of GBM via fitting the features of the price-evolution dynamics in the FTS for stocks and cryptocurrencies. The employed concept of single-trajectory-based time averaging can serve as a predictive tool (proxy) for a mathematically based assessment and rationalization of probabilistic trends in the evolution of stock-market prices.},
  language  = {en}
}
@article{MeyerAghionKantz2022,
  author    = {Meyer, Philipp and Aghion, Erez and Kantz, Holger},
  title     = {Decomposing the effect of anomalous diffusion enables direct calculation of the Hurst exponent and model classification for single random paths},
  series = {Journal of physics / Institute of Physics. A, Mathematical, nuclear and general},
  volume    = {55},
  journal   = {Journal of physics / Institute of Physics. A, Mathematical, nuclear and general},
  number    = {27},
  publisher = {IOP Publ. Ltd.},
  address   = {Bristol},
  issn      = {1751-8113},
  doi       = {10.1088/1751-8121/ac72d4},
  pages     = {22},
  year      = {2022},
  abstract  = {Recently, a large number of research teams from around the world collaborated in the so-called 'anomalous diffusion challenge'. Its aim: to develop and compare new techniques for inferring stochastic models from given unknown time series, and estimate the anomalous diffusion exponent in data. We use various numerical methods to directly obtain this exponent using the path increments, and develop a questionnaire for model selection based on feature analysis of a set of known stochastic processes given as candidates. Here, we present the theoretical background of the automated algorithm which we put for these tasks in the diffusion challenge, as a counter to other pure data-driven approaches.},
  language  = {en}
}