9,877 research outputs found
B-spline techniques for volatility modeling
This paper is devoted to the application of B-splines to volatility modeling,
specifically the calibration of the leverage function in stochastic local
volatility models and the parameterization of an arbitrage-free implied
volatility surface calibrated to sparse option data. We use an extension of
classical B-splines obtained by including basis functions with infinite
support. We first come back to the application of shape-constrained B-splines
to the estimation of conditional expectations, not merely from a scatter plot
but also from the given marginal distributions. An application is the Monte
Carlo calibration of stochastic local volatility models by Markov projection.
Then we present a new technique for the calibration of an implied volatility
surface to sparse option data. We use a B-spline parameterization of the
Radon-Nikodym derivative of the underlying's risk-neutral probability density
with respect to a roughly calibrated base model. We show that this method
provides smooth arbitrage-free implied volatility surfaces. Finally, we sketch
a Galerkin method with B-spline finite elements to the solution of the partial
differential equation satisfied by the Radon-Nikodym derivative.Comment: 25 page
Space-time least-squares isogeometric method and efficient solver for parabolic problems
In this paper, we propose a space-time least-squares isogeometric method to
solve parabolic evolution problems, well suited for high-degree smooth splines
in the space-time domain. We focus on the linear solver and its computational
efficiency: thanks to the proposed formulation and to the tensor-product
construction of space-time splines, we can design a preconditioner whose
application requires the solution of a Sylvester-like equation, which is
performed efficiently by the fast diagonalization method. The preconditioner is
robust w.r.t. spline degree and mesh size. The computational time required for
its application, for a serial execution, is almost proportional to the number
of degrees-of-freedom and independent of the polynomial degree. The proposed
approach is also well-suited for parallelization.Comment: 29 pages, 8 figure
Computational inference in systems biology
Parameter inference in mathematical models of biological pathways, expressed as coupled ordinary differential equations (ODEs), is a challenging problem. The computational costs associated with repeatedly solving the ODEs are often high. Aimed at reducing this cost, new concepts using gradient matching have been proposed. This paper combines current adaptive gradient matching approaches, using Gaussian processes, with a parallel tempering scheme, and conducts a comparative evaluation with current methods used for parameter inference in ODEs
Spline-based self-controlled case series method
The self-controlled case series (SCCS) method is an alternative to study designs such as cohort and case control methods and is used to investigate potential associations between the timing of vaccine or other drug exposures and adverse events. It requires information only on cases, individuals who have experienced the adverse event at least once, and automatically controls all fixed confounding variables that could modify the true association between exposure and adverse event. Time-varying confounders such as age, on the other hand, are not automatically controlled and must be allowed for explicitly. The original SCCS method used step functions to represent risk periods (windows of exposed time) and age effects. Hence, exposure risk periods and/or age groups have to be prespecified a priori, but a poor choice of group boundaries may lead to biased estimates. In this paper, we propose a nonparametric SCCS method in which both age and exposure effects are represented by spline functions at the same time. To avoid a numerical integration of the product of these two spline functions in the likelihood function of the SCCS method, we defined the first, second, and third integrals of I-splines based on the definition of integrals of M-splines. Simulation studies showed that the new method performs well. This new method is applied to data on pediatric vaccines
IGA-based Multi-Index Stochastic Collocation for random PDEs on arbitrary domains
This paper proposes an extension of the Multi-Index Stochastic Collocation
(MISC) method for forward uncertainty quantification (UQ) problems in
computational domains of shape other than a square or cube, by exploiting
isogeometric analysis (IGA) techniques. Introducing IGA solvers to the MISC
algorithm is very natural since they are tensor-based PDE solvers, which are
precisely what is required by the MISC machinery. Moreover, the
combination-technique formulation of MISC allows the straight-forward reuse of
existing implementations of IGA solvers. We present numerical results to
showcase the effectiveness of the proposed approach.Comment: version 3, version after revisio
Theoretical description of two ultracold atoms in finite 3D optical lattices using realistic interatomic interaction potentials
A theoretical approach is described for an exact numerical treatment of a
pair of ultracold atoms interacting via a central potential that are trapped in
a finite three-dimensional optical lattice. The coupling of center-of-mass and
relative-motion coordinates is treated using an exact diagonalization
(configuration-interaction) approach. The orthorhombic symmetry of an optical
lattice with three different but orthogonal lattice vectors is explicitly
considered as is the Fermionic or Bosonic symmetry in the case of
indistinguishable particles.Comment: 19 pages, 5 figure
- …