Results 61 to 70 of about 136,850 (323)
G-Brownian Motion as Rough Paths and Differential Equations Driven by G-Brownian Motion
, 2013 The present paper is devoted to the study of sample paths of G-Brownian motion and stochastic differential equations (SDEs) driven by G-Brownian motion from the view of rough path theory.
B.M. Hambly +24 more
core +1 more source
Large Deviation Principle for Enhanced Gaussian Processes [PDF]
, 2006 We study large deviation principles for Gaussian processes lifted to the free nilpotent group of step N. We apply this to a large class of Gaussian processes lifted to geometric rough paths.
Friz, Peter, Victoir, Nicolas
core +3 more sources
Geometrical Brownian Motion Driven by Color Noise [PDF]
, 2007 The evolution of prices on ideal market is given by geometrical Brownian motion, where Gaussian white noise describes fluctuations. We study the effect of correlations introduced by a color noise.
Ryszard Zygadło
openalex +4 more sources
Infinite ergodicity for geometric Brownian motion
, 2022 Geometric Brownian motion is an exemplary stochastic processes obeying multiplicative noise, with widespread applications in several fields, e.g. in finance, in physics and biology. The definition of the process depends crucially on the interpretation of the stochastic integrals which involves the discretization parameter $α$ with $0 \leq α\leq 1 ...
Giordano, Stefano +2 more
openaire +2 more sources
Number of paths versus number of basis functions in American option pricing
, 2003 An American option grants the holder the right to select the time at which to exercise the option, so pricing an American option entails solving an optimal stopping problem.
Glasserman, Paul, Yu, Bin
core +6 more sources
Generalizing Geometric Brownian Motion
, 2018 To convert standard Brownian motion $Z$ into a positive process, Geometric Brownian motion (GBM) $e^{ Z_t}, >0$ is widely used. We generalize this positive process by introducing an asymmetry parameter $ \geq 0$ which describes the instantaneous volatility whenever the process reaches a new low.
Carr, Peter, Zhang, Zhibai
openaire +2 more sources
Advanced Functional Materials, EarlyView.An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone +11 more
wiley +1 more source
Advanced Healthcare Materials, EarlyView.Flexible, biocompatible PVDF nanofibers embedded with magnetite nanodiscs enable wireless magnetoelectric neuromodulation. Shape anisotropy of the nanodiscs facilitates magnetostrictive strain transfer under alternating magnetic fields, allowing activation of neurons in vitro and behavioral modulation in vivo, without requiring rigid implants or ...
Lorenzo Signorelli +11 more
wiley +1 more source
Mean-Variance Asset-Liability Management with State-Dependent Risk Aversion [PDF]
, 2013 In this paper, we consider the asset-liability management under the mean-variance criterion. The financial market consists of a risk-free bond and a stock whose price process is modeled by a geometric Brownian motion.
Wang, Rongming, Wei, Jiaqin, Zhao, Qian
core
A SHARP MAXIMAL INEQUALITY FOR A GEOMETRIC BROWNIAN MOTION [PDF]
Taiwanese Journal of Mathematics, 2015 Let $X=(X_t)_{t\geq0}$ be a geometric Brownian motion with drift $\mu$ and volatility $\sigma>0$, and let $Y=(Y_t)_{t\geq0}$ be the associated maximum process of $X$. Under certain conditions, we prove a sharp maximal inequality for the geometric Brownian motion.
openaire +2 more sources