Results 41 to 50 of about 1,203,453 (336)

Functional traits mediate individualistic species‐environment distributions at broad spatial scales while fine‐scale species associations remain unpredictable

open access: yesAmerican Journal of Botany, Volume 109, Issue 12, Page 1991-2005, December 2022., 2022
Abstract Premise Numerous processes influence plant distributions and co‐occurrence patterns, including ecological sorting, limiting similarity, and stochastic effects. To discriminate among these processes and determine the spatial scales at which they operate, we investigated how functional traits and phylogenetic relatedness influence the ...
Jared J. Beck   +7 more
wiley   +1 more source

A Neural Stochastic Volatility Model [PDF]

open access: yesAAAI 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

Model of Continuous Random Cascade Processes in Financial Markets

open access: yesFrontiers 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

open access: yesSAGE 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

Closed-form approximate solutions for stop-loss and Russian options with multiscale stochastic volatility

open access: yesAIMS 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

The Jacobi stochastic volatility model [PDF]

open access: yesFinance and Stochastics, 2016
We introduce a novel stochastic volatility model where the squared volatility of the asset return follows a Jacobi process. It contains the Heston model as a limit case.
Damien Ackerer   +2 more
semanticscholar   +1 more source

Predictability of European winter 2020/2021: Influence of a mid‐winter sudden stratospheric warming

open access: yesAtmospheric Science Letters, Volume 23, Issue 12, December 2022., 2022
Boreal winter 2020/2021 was characterised by a negative North Atlantic Oscillation (NAO) pressure pattern, yet the signals from the ensemble mean of many seasonal forecast systems were for a positive NAO. In this letter we focus on the GloSea5 seasonal forecast system and investigate if there is any evidence for forecast error, or whether the ...
Julia F. Lockwood   +18 more
wiley   +1 more source

Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models [PDF]

open access: yes, 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

An Investment and Consumption Problem with CIR Interest Rate and Stochastic Volatility

open access: yesAbstract 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

Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials

open access: yesAdvanced 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

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