Zobrazeno 1 - 10
of 228
pro vyhledávání: '"Andersson, Kristoffer"'
Bygg- och fastighetssektorn står idag för 21% av Sveriges totala utsläpp av växthusgaser. Genom att bygga mer i trä kan utsläppen minskas i denna sektor och på så sätt bidra till att långsiktigt nå klimatmålen 2045. När det kommer till b
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-49476
In this paper, we propose a machine learning algorithm for time-inconsistent portfolio optimization. The proposed algorithm builds upon neural network based trading schemes, in which the asset allocation at each time point is determined by a a neural
Externí odkaz:
http://arxiv.org/abs/2308.10556
The aim of this work is to propose an extension of the deep solver by Han, Jentzen, E (2018) to the case of forward backward stochastic differential equations (FBSDEs) with jumps. As in the aforementioned solver, starting from a discretized version o
Externí odkaz:
http://arxiv.org/abs/2211.04349
Autor:
Ekstener, Albin, Andersson, Kristoffer
There are a variety of advantages to companies certifying their management system according to the ISO standards that exist. Examples of these advantages are that you get a tool that helps to conduct your business in a better way as you often get int
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-54037
In this paper, we propose a deep learning based numerical scheme for strongly coupled FBSDEs, stemming from stochastic control. It is a modification of the deep BSDE method in which the initial value to the backward equation is not a free parameter,
Externí odkaz:
http://arxiv.org/abs/2201.06854
A novel discretization is presented for forward-backward stochastic differential equations (FBSDE) with differentiable coefficients, simultaneously solving the BSDE and its Malliavin sensitivity problem. The control process is estimated by the corres
Externí odkaz:
http://arxiv.org/abs/2110.05421
Background: In the post-operative phase, the patient's ability to communicate is impaired. The postoperative patient is therefore in great need of that the information given is being individually adapted. Objectives: The aim of this study was to inve
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-381977
In this paper, we propose a neural network-based method for CVA computations of a portfolio of derivatives. In particular, we focus on portfolios consisting of a combination of derivatives, with and without true optionality, \textit{e.g.,} a portfoli
Externí odkaz:
http://arxiv.org/abs/2010.13843
A deep learning approach for computations of exposure profiles for high-dimensional Bermudan options
In this paper, we propose a neural network-based method for approximating expected exposures and potential future exposures of Bermudan options. In a first phase, the method relies on the Deep Optimal Stopping algorithm, which learns the optimal stop
Externí odkaz:
http://arxiv.org/abs/2003.01977
Autor:
Andersson, Kristoffer, Netzler, Tim
Denna undersökning tar hjälp av tidigare forskning, intervjuer, egna erfarenheter och diskussioner, för att komma till botten med om det finns en branschstandard gällande materialen som används i foleystudion samt arbetsprocessen. Stommen i unde
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:du-29536