Results 51 to 60 of about 129,146 (344)
, 2006 We propose two main applications of Gy\"{o}ngy (1986)'s construction of inhomogeneous Markovian stochastic differential equations that mimick the one-dimensional marginals of continuous It\^{o} processes.
Atlan, Marc
core +2 more sources
Advanced Functional Materials, EarlyView.This review highlights how machine learning (ML) algorithms are employed to enhance sensor performance, focusing on gas and physical sensors such as haptic and strain devices. By addressing current bottlenecks and enabling simultaneous improvement of multiple metrics, these approaches pave the way toward next‐generation, real‐world sensor applications.
Kichul Lee +17 more
wiley +1 more source
ESTIMASI VALUE AT RISK MENGGUNAKAN VOLATILITAS DISPLACED DIFFUSION
E-Jurnal Matematika, 2019 Value at Risk (VaR) is a measure of risk that is able to calculate the worst possible loss that can occurs to stock prices with a certain level of confidence and within a certain period of time. The purpose of this study was to determine the VaR estimate
MIRANDA NOVI MARA DEWI +2 more
doaj +1 more source
Stochastic Volatility: Origins and Overview [PDF]
, 2009 Stochastic volatility is the main way time-varying volatility is modelled in financial markets. The development of stochastic volatility is reviewed, placing it in a modeling and historical context. Some recent trends in the literature are highlighted.
Neil Shephard, Torben G. Andersen
openaire +6 more sources
Electron–Matter Interactions During Electron Beam Nanopatterning
Advanced Functional Materials, EarlyView.This article reviews the electron–matter interactions important to nanopatterning with electron beam lithography (EBL). Electron–matter interactions, including secondary electron generation routes, polymer radiolysis, and electron beam induced charging, are discussed.
Camila Faccini de Lima +2 more
wiley +1 more source
MathematicsThis paper develops an analytical pricing formula for vulnerable options with stochastic volatility under a two-factor stochastic interest rate model.
Junkee Jeon, Geonwoo Kim
doaj +1 more source
Multipower Variation and Stochastic Volatility [PDF]
, 2006 In this brief note we review some of our recent results on the use of high frequency financial data to estimate objects like integrated variance in stochastic volatility models. Interesting issues include multipower variation, jumps and market microstructure effects.
Barndorff-Nielsen, Ole Eiler +1 more
openaire +4 more sources
Advanced Functional Materials, EarlyView.This study establishes a materials‐driven framework for entropy generation within standard CMOS technology. By electrically rebalancing gate‐oxide traps and Si‐channel defects in foundry‐fabricated FDSOI transistors, the work realizes in‐materia control of temporal correlation – achieving task adaptive entropy optimization for reinforcement learning ...
Been Kwak +14 more
wiley +1 more source
PERHITUNGAN VALUE AT RISK DENGAN PENDUGA VOLATILITAS STOKASTIK HESTON
E-Jurnal Matematika, 2018 Value at risk is a method that measures financial risk of an security or portfolio. The aims of the research is to find out the value at risk of an exchange rate using the Heston stochastic volatility model. Heston model is a strochastic volatility model
DESAK PUTU DEVI DAMIYANTI +2 more
doaj +1 more source
Lithium Intercalation in the Anisotropic Van Der Waals Semiconductor CrSBr
Advanced Functional Materials, EarlyView.We report the lithium intercalation in the layered van der Waals crystal CrSBr, revealing strongly anisotropic ion‐migration dynamics. Optical and electrical characterization of exfoliated CrSBr shows lithium diffusion coefficients that differ by more than an order of magnitude along a‐ and b‐directions, consistent with molecular dynamics simulations ...
Kseniia Mosina +13 more
wiley +1 more source