Results 211 to 220 of about 71,229 (282)

AI companies' strategies with traditional vs. digital assets amid geopolitical and banking crises. [PDF]

Heliyon
Dammak W, Fakhfekh M, Alnafisah H, Jeribi A.   +3 more
europepmc   +1 more source

Empirical‐Process Limit Theory and Filter Approximation Bounds for Score‐Driven Time Series Models

Journal of Time Series Analysis, EarlyView.
ABSTRACT This article examines the filtering and approximation‐theoretic properties of score‐driven time series models. Under specific Lipschitz‐type and tail conditions, new results are derived, leading to maximal and deviation inequalities for the filtering approximation error using empirical process theory.
Enzo D'Innocenzo
wiley   +1 more source

Weighted portmanteau statistics for testing for zero autocorrelation in dependent data. [PDF]

J Appl Stat
Muriel N.
europepmc   +1 more source

Systemic risk in the insurance sector: A semi‐parametric approach based on Spearman's rho

Risk Management and Insurance Review, EarlyView.
Abstract We propose a new method to measure systemic risk in the global insurance sector by analyzing interconnectedness among firms under different market conditions. Using a semi‐parametric approach that relies on the Spearman correlation and copula‐based partial dependence, we assess relationships in relatively stable, extremely bullish, and ...
Leonardo Iania, Arturo Leccadito, Alessandro Staino   +2 more
wiley   +1 more source

The dependency structure of international commodity and stock markets after the Russia-Ukraine war. [PDF]

PLoS One
Zhang C, Liu S, Qin M, Gao B.
europepmc   +1 more source

Unveiling Stock Market Trends by Deep Learning Insights With Correction Factor and Recurrent Neural Networks

Expert Systems, Volume 43, Issue 5, May 2026.
ABSTRACT Understanding financial behaviour, particularly in the stock market, has attracted significant interest in recent years due to advancements in artificial intelligence and its impact on the global economy. The field of stock market prediction, which explores the interaction between finance and computer science to create predictive models, aims ...
Jair O. González, Rafael A. Berri, Giancarlo Lucca, Bruno L. Dalmazo, Eduardo N. Borges   +4 more
wiley   +1 more source

Predicting the volatility of Chinese stock indices based on realized recurrent conditional heteroskedasticity. [PDF]

PLoS One
Zhang G, Zhao H, Fan R.
europepmc   +1 more source

A Fuzzy Framework for Realized Volatility Prediction: Empirical Evidence From Equity Markets

Journal of Forecasting, Volume 45, Issue 3, Page 1261-1291, April 2026.
ABSTRACT This study introduces a realized volatility fuzzy time series (RV‐FTS) model that applies a fuzzy c‐means clustering algorithm to estimate time‐varying c latent volatility states and their corresponding membership degrees. These memberships are used to construct a fuzzified volatility estimate as a weighted average of cluster centroids.
Shafqat Iqbal, Štefan Lyócsa
wiley   +1 more source

Mitigating the choice of the duration in DDMS models through a parametric link. [PDF]

J Appl Stat
Mendes FHPES, Turatti DE, Pumi G.
europepmc   +1 more source

DysRegNet: Patient‐specific and confounder‐aware dysregulated network inference towards precision therapeutics

British Journal of Pharmacology, Volume 183, Issue 8, Page 1709-1724, April 2026.
Abstract Background and Purpose Gene regulation is frequently altered in diseases in unique and patient‐specific ways. Hence, personalised strategies have been proposed to infer patient‐specific gene‐regulatory networks. However, existing methods do not scale well because they often require recomputing the entire network per sample.
Johannes Kersting, Olga Lazareva, Zakaria Louadi, Jan Baumbach, David B. Blumenthal, Markus List   +5 more
wiley   +1 more source