Results 141 to 150 of about 1,263,567 (386)

BVARs and stochastic volatility

Bayesian vector autoregressions (BVARs) are the workhorse in macroeconomic forecasting. Research in the last decade has established the importance of allowing time-varying volatility to capture both secular and cyclical variations in macroeconomic uncertainty.
openaire   +2 more sources

Big Data and AI‐Powered Modeling: A Pathway to Sustainable Precision Animal Nutrition

Advanced Science, EarlyView.
This review summarizes the current landscape of big data and AI‐powered modeling in animal nutrition, covering techniques including intelligent data acquisition, data augmentation, explainable machine learning, heuristic algorithms, and life cycle assessment‐based sustainability evaluation.
Shuai Zhang, Changhua Lai, Jinbiao Zhao, Junjun Wang   +3 more
wiley   +1 more source

Vanilla Option Pricing on Stochastic Volatility market models [PDF]

We want to discuss the option pricing on stochastic volatility market models, in which we are going to consider a generic function β (νt ) for the drift of volatility process.
Dell'Era, Mario
core   +1 more source

Pricing vanilla, barrier, and lookback options under two-scale stochastic volatility driven by two approximate fractional Brownian motions

AIMS Mathematics
In this paper, we proposed a stochastic volatility model in which the volatility was given by stochastic processes representing two characteristic time scales of variation driven by approximate fractional Brownian motions with two Hurst exponents.
Min-Ku Lee, Jeong-Hoon Kim
doaj   +1 more source

Aging as a Loss of Goal‐Directedness: An Evolutionary Simulation and Analysis Unifying Regeneration with Anatomical Rejuvenation

Advanced Science, EarlyView.
The paper proposes that the root cause of aging is the loss of anatomical goal‐directedness after development. Using evolutionary neural cellular automata simulations, the authors show that after the organism has reached its developmental homeostatic setpoint (the adult morphology), the absence of target state to pursue leads to a drifting anatomical ...
Léo Pio‐Lopez, Benedikt Hartl, Michael Levin   +2 more
wiley   +1 more source

Pricing American Options under Stochastic Volatility: A New Method Using Chebyshev Polynomials to Approximate the Early Exercise Boundary [PDF]

This paper presents a new numerical method for pricing American call options when the volatility of the price of the underlying stock is stochastic.
Elias Tzavalis, Shijun Wang
core  

Modelling fluctuations of financial time series: from cascade process to stochastic volatility model [PDF]

, 2000
Jean–François Muzy, J. Delour, Emmanuel Bacry   +2 more
openalex   +1 more source

A Stochastic Volatility Lattice

IFAC Proceedings Volumes, 1998
Abstract Stochastic volatility models model asset dynamics by a bivariate diffusion process. For practical calculation of prices of financial derivatives lattice models are necessary. In this paper we present a procedure to construct discrete process approximations converging to such ...
openaire   +2 more sources

Learning to Navigate in Chemical Fields Without A Map at Low Reynolds Numbers

Advanced Science, EarlyView.
Deep reinforcement learning enables a reconfigurable artificial microswimmer to exhibit run‐and‐tumble strategy to navigate toward a chemical source. This mapless microswimmer can robustly navigate in diverse chemical fields without relying on a pre‐existing map.
Yangzhe Liu, On Shun Pak, Alan C. H. Tsang   +2 more
wiley   +1 more source

Systematics of Advanced Capital Market Models based on Empirical Research [PDF]

The complex blue prints of ODE and PDE based capital market models remain closed to systematic review. Particularly, when some authors of mathematical models can not or may not offer explicit solutions.
Gerhard Schroeder
core