3 papers
stat.ME2021
Estimation When Both Covariance And Precision Matrices Are Sparse
Shev Macnamara, Erik Schlögl, Zdravko I. Botev
We offer a method to estimate a covariance matrix in the special case that \textit{both} the covariance matrix and the precision matrix are sparse --- a constraint we call double s…
q-fin.RM2018
Quantifying the Model Risk Inherent in the Calibration and Recalibration of Option Pricing Models
Yu Feng, Ralph Rudd, Christopher Baker +3
We focus on two particular aspects of model risk: the inability of a chosen model to fit observed market prices at a given point in time (calibration error) and the model risk due…
stat.ML2018
Parameter Learning and Change Detection Using a Particle Filter With Accelerated Adaptation
Karol Gellert, Erik Schlögl
This paper presents the construction of a particle filter, which incorporates elements inspired by genetic algorithms, in order to achieve accelerated adaptation of the estimated p…