Results 181 to 190 of about 71,229 (282)

Dynamic forecasting and mechanisms of volatility synchronization in complex financial systems. [PDF]

PLoS One
Li JC, Guo J, Ma R, Zhong G.
europepmc   +1 more source

Between Cryptocurrencies' Risk and Crypto Environmental Attention: The Crypto Environment Attention Index and Volatility in the Cryptocurrencies Market Nexus

Business Ethics, the Environment &Responsibility, EarlyView.
ABSTRACT This study investigates the impact of environmental attention on cryptocurrency market volatility by introducing the Crypto Environmental Attention Index (CEAI), a new metric inspired by Wang et al. (2022) and constructed using daily web search data.
Ines Ghazouani, Zaineb Hlioui, Marwa Zouawi   +2 more
wiley   +1 more source

Application of seasonal-adjusted hybrid models for forecasting Discomfort Index in a heat-prone region of Bangladesh. [PDF]

PLoS One
Binte Ahmed A, Uddin Qureshi MM, Hussain Khan MM, Dulmini A, Haque Mollah MA, Rois R.   +5 more
europepmc   +1 more source

Closed‐Form Optimal Investment Under Generalized GARCH Models

European Financial Management, EarlyView.
ABSTRACT This paper introduces a new class of stochastic volatility models for asset prices, the generalized Heston Nandi GARCH (GHN‐GARCH), with the primary objective of optimal dynamic asset allocation under expected utility theory for constant relative risk aversion investors. We study some of its theoretical properties, and demonstrate that the GHN‐
Marcos Escobar‐Anel, Tobias Lausser, Rudi Zagst   +2 more
wiley   +1 more source

Value at Risk long memory volatility models with heavy-tailed distributions for cryptocurrencies. [PDF]

Front Appl Math Stat
Subramoney SD, Chinhamu K, Chifurira R.
europepmc   +1 more source

Specification Tests for Jump‐Diffusion Models Based on the Characteristic Function

International Statistical Review, EarlyView.
Summary Goodness‐of‐fit tests are suggested for several popular jump‐diffusion processes. The suggested test statistics utilise the marginal characteristic function of the model and its L2‐type discrepancy from an empirical counterpart. Model parameters are estimated either by minimising the aforementioned L2‐type discrepancy or by maximum likelihood ...
Gerrit Lodewicus Grobler, Denis Belomestny, Simos George Meintanis, Emanuele Taufer   +3 more
wiley   +1 more source

AI-Carbon-Energy: Spillover effects and drivers in interconnected markets. [PDF]

iScience
Zhang M, Pan Y, Su B, Zhou D.
europepmc   +1 more source

Simple Python‐based methods for analysis and drift‐correction of STM images

Journal of Microscopy, EarlyView.
Abstract A successful scanning tunnelling microscopy (STM) experiment relies on both delicate sample preparation and measurement, and careful image filtering and analysis to provide clear and solid results. Processing and analysis of STM images may result in a tricky task, due to the complexity and specificity of the probed systems.
Francesco Cazzadori, Alessandro Facchin, Silvio Reginato, Christian Durante   +3 more
wiley   +1 more source

Investigating the impact of investor attention on AI-based stocks: A comprehensive analysis using quantile regression, GARCH, and ARIMA models. [PDF]

PLoS One
Ravichandran S, Afjal M.
europepmc   +1 more source

Distributional and Tail‐Dependent Perspectives in Economic Relationships: A Review of Quantile Regression Application

Journal of Economic Surveys, EarlyView.
ABSTRACT There is an increased proportion of studies using quantile‐based regression methodology (QR) in economics. They offer a robust alternative to classical mean regressions, which can estimate non‐normal variables with distributional heterogeneity in the dependent variable.
Shajara Ul‐Durar, Konstantinos A. Dimitriadis, Noman Arshed, Marco De Sisto, Hamidreza Harati   +4 more
wiley   +1 more source