Results 61 to 70 of about 1,246,337 (384)
A Neural Stochastic Volatility Model [PDF]
AAAI Conference on Artificial Intelligence, 2017 In this paper, we show that the recent integration of statistical models with deep recurrent neural networks provides a new way of formulating volatility (the degree of variation of time series) models that have been widely used in time series ...
Rui Luo +3 more
semanticscholar +1 more source
Multiple time scales and the empirical models for stochastic volatility [PDF]
, 2006 The most common stochastic volatility models such as the Ornstein-Uhlenbeck (OU), the Heston, the exponential OU (ExpOU) and Hull-White models define volatility as a Markovian process.
Brosa +32 more
core +2 more sources
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
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
AIMS Mathematics, 2023 In general, derivation of closed-form analytic formulas for the prices of path-dependent exotic options is a challenging task when the underlying asset price model is chosen to be a stochastic volatility model.
Min-Ku Lee, Jeong-Hoon Kim
doaj +1 more source
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
Advanced Engineering Materials, EarlyView.Developing process parameters for the laser‐based Powder Bed Fusion of metals can be a tedious task. Based on melt pool depth, the process parameters are transferable to different laser scan speeds. For this, understanding the melt pool scaling behavior is essential, particularly for materials with high thermal diffusivity, as a change in scaling ...
Markus Döring +2 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
Large Deviation Principle for Volterra Type Fractional Stochastic Volatility Models [PDF]
SIAM Journal on Financial Mathematics, 2017 We study fractional stochastic volatility models for the asset price, in which the volatility process is a positive continuous function $\sigma$ of a continuous fractional stochastic process $\widehat{B}$. The main result obtained in the present paper is
Archil Gulisashvili
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