Results 61 to 70 of about 56,469 (146)
Measure‐valued processes for energy markets
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
Robust Bernoulli Mixture Models for Credit Portfolio Risk
ABSTRACT This paper presents comparison results and establishes risk bounds for credit portfolios within classes of Bernoulli mixture models, assuming conditionally independent defaults that are stochastically increasing in a common risk factor. We provide simple and interpretable conditions on conditional default probabilities that imply a comparison ...
Jonathan Ansari, Eva Lütkebohmert
wiley +1 more source
Optimal Portfolio Choice With Cross‐Impact Propagators
ABSTRACT We consider a class of optimal portfolio choice problems in continuous time where the agent's transactions create both transient cross‐impact driven by a matrix‐valued Volterra propagator, as well as temporary price impact. We formulate this problem as the maximization of a revenue‐risk functional, where the agent also exploits available ...
Eduardo Abi Jaber +2 more
wiley +1 more source
ABSTRACT This study develops a novel multivariate stochastic framework for assessing systemic risks, such as climate and nature‐related shocks, within production or financial networks. By embedding a linear stochastic fluid network, interpretable as a generalized vector Ornstein–Uhlenbeck process, into the production network of interdependent ...
Giovanni Amici +3 more
wiley +1 more source
Likelihood Estimation for Stochastic Differential Equations with Mixed Effects
ABSTRACT Stochastic differential equations provide a powerful tool for modelling dynamic phenomena affected by random noise. When time series are observed for several experimental units, it is often the case that some of the parameters vary between the individual experimental units.
Fernando Baltazar‐Larios +2 more
wiley +1 more source
Lifts of continuous and Hölder alpha curves in the configuration space MN/SN$M^N/S_N$
Abstract In this paper, we study the quotient space X=MN/SN$X = M^N / S_N$ of equivalence classes of N$N$‐tuples in a metric space (M,dM)$(M, d_M)$, equipped with the metric induced by the minimal total pairing distance. Given a continuous path F:(0,1)→X$F: (0,1) \rightarrow X$, we prove that there exist continuous functions f1,⋯,fN:(0,1)→M$f_1, \dots,
Charles L. Fefferman +3 more
wiley +1 more source
Coupled Clustering in Hierarchical Matrices for the Oseen Problem
Fluid flow problems can be modelled by the Navier‐Stokes or, after linearization, by the Oseen equations. Their discretization results in linear systems in saddle point form which are typically very large and need to be solved iteratively. We propose a novel block structure for hierarchical matrices which is then used to build preconditioners for the ...
Jonas Grams, Sabine Le Borne
wiley +1 more source
On MAP Estimates and Source Conditions for Drift Identification in SDEs
ABSTRACT We consider the inverse problem of identifying the drift in an stochastic differential equation (SDE) from n$n$ observations of its solution at M+1$M+1$ distinct time points. We derive a corresponding maximum a posteriori (MAP) estimate, we prove differentiability properties as well as a so‐called tangential cone condition for the forward ...
Daniel Tenbrinck +3 more
wiley +1 more source
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
Spatial depth for data in metric spaces
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

