Results 61 to 70 of about 1,263,567 (386)
Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models [PDF]
, 2016 We discuss efficient Bayesian estimation of dynamic covariance matrices in multivariate time series through a factor stochastic volatility model. In particular, we propose two interweaving strategies to substantially accelerate convergence and mixing of ...
G. Kastner +2 more
semanticscholar +1 more source
Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials
Advanced Engineering Materials, EarlyView.This article explores how machine learning (ML) revolutionizes the study and design of disordered materials by uncovering hidden patterns, predicting properties, and optimizing multiscale structures. It highlights key advancements, including generative models, graph neural networks, and hybrid ML‐physics methods, addressing challenges like data ...
Hamidreza Yazdani Sarvestani +4 more
wiley +1 more source
, 1995 This paper prepared for the Handbook of Statistics (Vol.14: Statistical Methods in Finance), surveys the subject of stochastic volatility. the following subjects are covered: volatility in financial markets (instantaneous volatility of asset returns, implied volatilities in option prices and related stylized facts), statistical modelling in discrete ...
Ghysels, E., Harvey, A., Renault, E.
openaire +1 more source
Structurally Colored Physically Unclonable Functions with Ultra‐Rich and Stable Encoding Capacity
Advanced Functional Materials, Volume 35, Issue 12, March 18, 2025.This study reports a design strategy for generating bright‐field resolvable physically unclonable functions with extremely rich encoding capacity coupled with outstanding thermal and chemical stability. The optical response emerges from thickness‐dependent structural color formation in ZnO features, which are fabricated by physical vapor deposition ...
Abidin Esidir +8 more
wiley +1 more source
, 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
An Investment and Consumption Problem with CIR Interest Rate and Stochastic Volatility
Abstract and Applied Analysis, 2013 We are concerned with an investment and consumption problem with stochastic interest rate and stochastic volatility, in which interest rate dynamic is described by the Cox-Ingersoll-Ross (CIR) model and the volatility of the stock is driven by Heston’s ...
Hao Chang, Xi-min Rong
doaj +1 more source
A New Memory Effect in Bulk Crystals of 1T‐TaS2
Advanced Functional Materials, EarlyView.A new memory effect is discovered in 1T‐TaS₂, appearing as a temperature shift in the metal to insulator transition, coinciding with the recently reported ramp reversal memory. These findings imply that ramp reversal memory is an emergent phenomenon, likely to appear in many different systems that share a few basic properties, which are discussed in ...
Avital Fried +4 more
wiley +1 more source
Pricing Arithmetic Asian Options under Hybrid Stochastic and Local Volatility
Journal of Applied Mathematics, 2014 Recently, hybrid stochastic and local volatility models have become an industry standard for the pricing of derivatives and other problems in finance. In this study, we use a multiscale stochastic volatility model incorporated by the constant elasticity ...
Min-Ku Lee +2 more
doaj +1 more source
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
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