Zobrazeno 1 - 10
of 619
pro vyhledávání: '"Bayer Christian"'
This work addresses stochastic optimal control problems where the unknown state evolves in continuous time while partial, noisy, and possibly controllable measurements are only available in discrete time. We develop a framework for controlling such s
Externí odkaz:
http://arxiv.org/abs/2407.18018
Autor:
Bayer, Christian, Hammouda, Chiheb Ben, Papapantoleon, Antonis, Samet, Michael, Tempone, Raúl
Efficiently pricing multi-asset options poses a significant challenge in quantitative finance. The Monte Carlo (MC) method remains the prevalent choice for pricing engines; however, its slow convergence rate impedes its practical application. Fourier
Externí odkaz:
http://arxiv.org/abs/2403.02832
Autor:
Bayer Christian, Seidel Robin
Publikováno v:
Current Directions in Biomedical Engineering, Vol 4, Iss 1, Pp 525-528 (2018)
Many machine learning algorithms depend on the choice of an appropriate similarity or distance measure. Comparing such measures in different domains and on diversely structured data is common, but often performed in regards of an algorithm to cluster
Externí odkaz:
https://doaj.org/article/844bca90ff1849b1be830b13308210de
We propose two signature-based methods to solve the optimal stopping problem - that is, to price American options - in non-Markovian frameworks. Both methods rely on a global approximation result for $L^p-$functionals on rough path-spaces, using line
Externí odkaz:
http://arxiv.org/abs/2312.03444
Autor:
Bayer, Christian, Breneis, Simon
We provide an efficient and accurate simulation scheme for the rough Heston model in the standard ($H>0$) as well as the hyper-rough regime ($H > -1/2$). The scheme is based on low-dimensional Markovian approximations of the rough Heston process deri
Externí odkaz:
http://arxiv.org/abs/2310.04146
Autor:
Bayer, Christian, Breneis, Simon
The rough Heston model is a very popular recent model in mathematical finance; however, the lack of Markov and semimartingale properties poses significant challenges in both theory and practice. A way to resolve this problem is to use Markovian appro
Externí odkaz:
http://arxiv.org/abs/2309.07023
We present an adaptive algorithm for effectively solving rough differential equations (RDEs) using the log-ODE method. The algorithm is based on an error representation formula that accurately describes the contribution of local errors to the global
Externí odkaz:
http://arxiv.org/abs/2307.12590
In this work, we introduce a novel pricing methodology in general, possibly non-Markovian local stochastic volatility (LSV) models. We observe that by conditioning the LSV dynamics on the Brownian motion that drives the volatility, one obtains a time
Externí odkaz:
http://arxiv.org/abs/2307.09216
Autor:
Samet, Michael, Bayer, Christian, Hammouda, Chiheb Ben, Papapantoleon, Antonis, Tempone, Raúl
Efficiently pricing multi-asset options is a challenging problem in quantitative finance. When the characteristic function is available, Fourier-based methods are competitive compared to alternative techniques because the integrand in the frequency s
Externí odkaz:
http://arxiv.org/abs/2203.08196
We study the weak convergence rate in the discretization of rough volatility models. After showing a lower bound $2H$ under a general model, where $H$ is the Hurst index of the volatility process, we give a sharper bound $H + 1/2$ under a linear mode
Externí odkaz:
http://arxiv.org/abs/2203.02943