28 citations · 94 across the 10 of their papers we have counts for
14 papers
Stochastic Monotonicity and Random Utility Models: The Good and The Ugly
Henk Keffert, Nikolaus Schweizer
When it comes to structural estimation of risk preferences from data on choices, random utility models have long been one of the standard research tools in economics. A recent lite…
Robust Decisions for Heterogeneous Agents via Certainty Equivalents
Anne G. Balter, Nikolaus Schweizer
We study the problem of a planner who resolves risk-return trade-offs - like financial investment decisions - on behalf of a collective of agents with heterogeneous risk preference…
Contingent Capital with Stock Price Triggers in Interbank Networks
Anne G. Balter, Nikolaus Schweizer, Juan C. Vera
This paper studies existence and uniqueness of equilibrium prices in a model of the banking sector in which banks trade contingent convertible bonds with stock price triggers among…
Robust Multiple Stopping -- A Pathwise Duality Approach
Roger J. A. Laeven, John G. M. Schoenmakers, Nikolaus F. F. Schweizer +1
We develop a method to solve, theoretically and numerically, general optimal stopping problems. Our general setting allows for multiple exercise rights, i.e., optimal multiple stop…
`Regression Anytime' with Brute-Force SVD Truncation
Christian Bender, Nikolaus Schweizer
We propose a new least-squares Monte Carlo algorithm for the approximation of conditional expectations in the presence of stochastic derivative weights. The algorithm can serve as…
Perturbation Bounds for Monte Carlo within Metropolis via Restricted Approximations
Felipe Medina-Aguayo, Daniel Rudolf, Nikolaus Schweizer
The Monte Carlo within Metropolis (MCwM) algorithm, interpreted as a perturbed Metropolis-Hastings (MH) algorithm, provides an approach for approximate sampling when the target dis…
Pathwise Iteration for Backward SDEs
Christian Bender, Christian Gaertner, Nikolaus Schweizer
We introduce a novel numerical approach for a class of stochastic dynamic programs which arise as discretizations of backward stochastic differential equations or semi-linear parti…
What's in a ball? Constructing and characterizing uncertainty sets
Thomas Kruse, Judith C. Schneider, Nikolaus Schweizer
In the presence of model risk, it is well-established to replace classical expected values by worst-case expectations over all models within a fixed radius from a given reference m…
Perturbation theory for Markov chains via Wasserstein distance
Daniel Rudolf, Nikolaus Schweizer
Perturbation theory for Markov chains addresses the question how small differences in the transitions of Markov chains are reflected in differences between their distributions. We…
Faster Comparison of Stopping Times by Nested Conditional Monte Carlo
Fabian Dickmann, Nikolaus Schweizer
We show that deliberately introducing a nested simulation stage can lead to significant variance reductions when comparing two stopping times by Monte Carlo. We derive the optimal…
Error bounds of MCMC for functions with unbounded stationary variance
Daniel Rudolf, Nikolaus Schweizer
We prove explicit error bounds for Markov chain Monte Carlo (MCMC) methods to compute expectations of functions with unbounded stationary variance. We assume that there is a $p\in(…
A primal-dual algorithm for BSDEs
Christian Bender, Nikolaus Schweizer, Jia Zhuo
We generalize the primal-dual methodology, which is popular in the pricing of early-exercise options, to a backward dynamic programming equation associated with time discretization…
Non-asymptotic Error Bounds for Sequential MCMC Methods in Multimodal Settings
Nikolaus Schweizer
We prove non-asymptotic error bounds for Sequential MCMC methods in the case of multimodal target distributions. Our bounds depend in an explicit way on upper bounds on relative de…
Non-asymptotic Error Bounds for Sequential MCMC and Stability of Feynman-Kac Propagators
Nikolaus Schweizer
We provide a generic way of deducing non-asymptotic error bounds for Sequential MCMC methods from suitable stability properties of Feynman-Kac propagators. We show how to derive th…