Results 211 to 220 of about 2,971 (265)

Extensions to Extended Tight‐Binding Methods for Transition‐Metal Containing Systems

Journal of Computational Chemistry, Volume 47, Issue 7, March 15, 2026.
We present a new GFN2‐xTB implementation with a geometric direct minimization scheme and a Hubbard‐U correction. We demonstrate that the Hubbard correction improves linearity of the elctronic energy, stabilizes SCF convergence, and enables more accurate spin‐gap predictions in narrow application domains such as specific iron‐containing complexes ...
Siyavash Moradi, Rebecca Tomann, Martin Head‐Gordon, Christopher J. Stein   +3 more
wiley   +1 more source

Climate variability, population growth, and globalization impacting food security in Pakistan. [PDF]

Sci Rep
Abbas S, Haider A, Kousar S, Lu H, Lu S, Liu F, Li H, Miao C, Feng W, Ahamad MI, Mehmood MS, Zulqarnain RM.   +11 more
europepmc   +1 more source

Two forecasting model selection methods based on time series image feature augmentation. [PDF]

Sci Rep
Jiang W, Wang Q, Li H.
europepmc   +1 more source

Heteroscedasticity effects as component to future stock market predictions using RNN-based models. [PDF]

PLoS One
Sadon AN, Ismail S, Khamis A, Tariq MU.
europepmc   +1 more source

Hybrid Fourier asymmetric-garch estimation of value at risk and expected shortfall: Empirical evidence from crude oil prices. [PDF]

Heliyon
Doabil L, Nasiru S, Iddrisu MM.
europepmc   +1 more source

Multiscale neural dynamics in sleep transition volatility across age scales: a multimodal EEG-EMG-EOG analysis of temazepam effects. [PDF]

Geroscience
Sirpal P, Sikora WA, Refai HH.
europepmc   +1 more source

Evaluating GARCH models [PDF]

Journal of Econometrics, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Stefan Lundbergh, Timo Teräsvirta
openaire   +2 more sources

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ON MIXTURE MEMORY GARCH MODELS

Journal of Time Series Analysis, 2013
We propose a new volatility model, which is called the mixture memory generalized autoregressive conditional heteroskedasticity (MM‐GARCH) model. The MM‐GARCH model has two mixture components, of which one is a short‐memory GARCH and the other is the long‐memory fractionally integrated GARCH.
Li, M, Li, WK, Li, G
openaire   +4 more sources

Varying Coefficient GARCH Models

2009
This paper offers a new method for estimation and forecasting of the volatility of financial time series when the stationarity assumption is violated. We consider varying–coefficient parametric models, such as ARCH and GARCH, whose coefficients may arbitrarily vary with time.
Cizek, P., Spokoiny, V.
openaire   +2 more sources