Results 41 to 50 of about 768,080 (299)
Extremal behavior of stochastic volatility models [PDF]
, 2005 Empirical volatility changes in time and exhibits tails, which are heavier than normal. Moreover, empirical volatility has - sometimes quite substantial - upwards jumps and clusters on high levels.
Lindner, Alexander M. +7 more
core +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
Advanced Functional Materials, EarlyView.In MOCVD MoS2 memristors, a current compliance‐regulated Ag filament mechanism is revealed. The filament ruptures spontaneously during volatile switching, while subsequent growth proceeds vertically through the MoS2 layers and then laterally along the van der Waals gaps during nonvolatile switching.
Yuan Fa +19 more
wiley +1 more source
Model of Continuous Random Cascade Processes in Financial Markets
Frontiers in Physics, 2020 This article presents a continuous cascade model of volatility formulated as a stochastic differential equation. Two independent Brownian motions are introduced as random sources triggering the volatility cascade: one multiplicatively combines with ...
Jun-ichi Maskawa, Koji Kuroda
doaj +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
Deep Stochastic Volatility Model
CoRR, 2021 Volatility for financial assets returns can be used to gauge the risk for financial market. We propose a deep stochastic volatility model (DSVM) based on the framework of deep latent variable models. It uses flexible deep learning models to automatically detect the dependence of the future volatility on past returns, past volatilities and the ...
Xiuqin Xu, Ying Chen
openaire +3 more sources
Advanced Functional Materials, EarlyView.A scalable, solution‐processed WSe2/ZrO2‐x van der Waals heterostructure realizes a light‐induced field‐tunneling synapse (LIFTS) that activates exclusively under bright illumination, emulating the photopic adaptation of the human retina at the device level.
Kijeong Nam +10 more
wiley +1 more source
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
Advanced Functional Materials, EarlyView.The physical realization of artificial neurons is a critical challenge for energy‐efficient neuromorphic computing. This review presents a comprehensive analysis of the evolution of artificial neuron implementations from conventional CMOS to emerging post‐CMOS technologies.
Kannan Udaya Mohanan +4 more
wiley +1 more source
Forecasting the Crude Oil Prices Volatility With Stochastic Volatility Models
SAGE Open, 2021 In this article, the stochastic volatility model is introduced to forecast crude oil volatility by using data from the West Texas Intermediate (WTI) and Brent markets.
Dondukova Oyuna, Liu Yaobin
doaj +1 more source