Results 71 to 80 of about 64,415 (198)
Measure‐valued processes for energy markets
Mathematical Finance, Volume 35, Issue 2, Page 520-566, April 2025.Abstract We introduce a framework that allows to employ (non‐negative) measure‐valued processes for energy market modeling, in particular for electricity and gas futures. Interpreting the process' spatial structure as time to maturity, we show how the Heath–Jarrow–Morton approach can be translated to this framework, thus guaranteeing arbitrage free ...
Christa Cuchiero +3 more
wiley +1 more source
The fundamental theorem of asset pricing with and without transaction costs
Mathematical Finance, Volume 35, Issue 2, Page 567-609, April 2025.Abstract We prove a version of the fundamental theorem of asset pricing (FTAP) in continuous time that is based on the strict no‐arbitrage condition and that is applicable to both frictionless markets and markets with proportional transaction costs. We consider a market with a single risky asset whose ask price process is higher than or equal to its ...
Christoph Kühn
wiley +1 more source
The Mathematical History Behind the Granger–Johansen Representation Theorem
Oxford Bulletin of Economics and Statistics, EarlyView.ABSTRACT When can a vector time series that is integrated once (i.e., becomes stationary after taking first differences) be described in error correction form? The answer to this is provided by the Granger–Johansen representation theorem. From a mathematical point of view, the theorem can be viewed as essentially a statement concerning the geometry of ...
Johannes M. Schumacher
wiley +1 more source
C∗-Algebras Associated with Hilbert C∗-Quad Modules of Finite Type
International Journal of Mathematics and Mathematical Sciences, 2014 A Hilbert C∗-quad module of finite type has a multistructure of Hilbert C∗-bimodules with two finite bases. We will construct a C∗-algebra from a Hilbert C∗-quad module of finite type and prove its universality subject to certain relations among ...
Kengo Matsumoto
doaj +1 more source
Spatial depth for data in metric spaces
Scandinavian Journal of Statistics, EarlyView.Abstract We propose a novel measure of statistical depth, the metric spatial depth, for data residing in an arbitrary metric space. The measure assigns high (low) values for points located near (far away from) the bulk of the data distribution, allowing quantifying their centrality/outlyingness.
Joni Virta
wiley +1 more source
How to Match Cognitive Model Predictions With EEG Data
Topics in Cognitive Science, EarlyView.Abstract Reliably identifying relevant brain areas implicated by the simulated activity from cognitive models is still an unsolved problem for cognitive modeling, particularly when matching model output with human electroencephalography (EEG) data. We propose a new method involving postprocessing of ACT‐R module activity and clustered EEG component ...
Kai Preuss +3 more
wiley +1 more source
A simple characterization of commutative H*-algebras
International Journal of Mathematics and Mathematical Sciences, 1999 Commutative H*-algebras are characterized without postulating the existence of Hilbert space structure.
Parfeny P. Saworotnow
doaj +1 more source
Hilbert C*-modules and related subjects - a guided reference overview I
, 1996 The overview contains 450 references of books, chapters of monographs, papers, preprints and Ph.~D.~thesises which are concerned with the theory and/or various applications of Hilbert C*-modules.
Frank, Michael
core
International Journal for Numerical Methods in Fluids, Volume 98, Issue 6, Page 709-730, June 2026.We introduce an efficient open‐source numerical framework for the automated search for the placements of injection and production wells in hot fracture‐controlled reservoirs that sustainably optimize geothermal energy production. We model the reservoirs as discrete fracture networks in 3D. The fluid flow and heat transport in the reservoirs are modeled
Ondřej Pártl, Ernesto Meneses Rioseco
wiley +1 more source
International Journal of Robust and Nonlinear Control, Volume 36, Issue 9, Page 4957-4970, June 2026.ABSTRACT Data‐based learning of system dynamics allows model‐based control approaches to be applied to systems with partially unknown dynamics. Gaussian process regression is a preferred approach that outputs not only the learned system model but also the variance of the model, which can be seen as a measure of uncertainty.
Daniel Landgraf +2 more
wiley +1 more source