Results 161 to 170 of about 11,660 (201)

Improving Financial Volatility Modeling Using neutrosophic Logic and Applying the GJR-GARCH Model

passer journal of basic and applied sciences
openaire   +1 more source

mRNA vaccines engage unconventional pathways in CD8⁺ T cell priming. [PDF]

Nature
Jo S, Li L, Thakur C, Telfer KA, Sultan H, Ohara RA, He M, Nam G, Chen J, Ou F, Draghi M, Valiante NM, Schreiber RD, Randolph GJ, Saligrama N, Murphy TL, Gillanders WE, Murphy KM.   +17 more
europepmc   +1 more source

Unveiling in situ oxygen, carbon and nutrient cycling of a sponge-driven biological hotspot in the arctic. [PDF]

Sci Rep
Hanz U, Mueller B, Bart MC, Busch K, Reichart GJ, Rapp HT, de Goeij JM, Mienis F.   +7 more
europepmc   +1 more source

Flexible Target Prediction for Quantitative Trading in the American Stock Market: A Hybrid Framework Integrating Ensemble Models, Fusion Models and Transfer Learning. [PDF]

Entropy (Basel)
Yan K, Yue Z, Wu CC, He Q, Zhou J, Hao Z, Li Y.   +6 more
europepmc   +1 more source

Coproducing and evaluating public health communication of invasive group A streptococcal infection (iGAS) for people experiencing homelessness and people who inject drugs

Martin AF, Denford S, Eastwood L, Yardley L, Rubin GJ, Lamagni T, Amlôt R, Wilsenham N, Roberts DJ.   +8 more
europepmc   +1 more source

Analytic moments for GJR-GARCH (1, 1) processes

International Journal of Forecasting, 2021
For a GJR-GARCH(1,1) specification with a generic innovation distribution we derive analytic expressions for the first four conditional moments of the forward and aggregated returns and variances. Moments for the most commonly used GARCH models are stated as special cases.
Carol Alexander, Emese Lazar
exaly   +2 more sources

GJR-GARCH Volatility Modeling under NIG and ANN for Predicting Top Cryptocurrencies [PDF]

Journal of Risk and Financial Management, 2021
Cryptocurrencies are currently traded worldwide, with hundreds of different currencies in existence and even more on the way. This study implements some statistical and machine learning approaches for cryptocurrency investments. First, we implement GJR-GARCH over the GARCH model to estimate the volatility of ten popular cryptocurrencies based on market
Fahad Mostafa, Mohammad Rafiqul Islam, Nguyet   +2 more
exaly   +3 more sources

Some of the next articles are maybe not open access.

Related searches:

A new GJR‐GARCH model for ℤ‐valued time series

Journal of Time Series Analysis, 2021
The Glosten–Jagannathan–Runkle GARCH (GJR‐GARCH) model is popular in accounting for asymmetric responses in the volatility in the analysis of continuous‐valued financial time series, but asymmetric responses in the volatility are also observed in time series of counts or ‐valued time series, such as the daily number of stock transactions or the daily ...
Yue Xu, Fukang Zhu
openaire   +1 more source

Robust M-estimate of GJR model with high frequency data

Acta Mathematicae Applicatae Sinica, 2015
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Huang, Jin-shan, Wu, Wu-qing, Chen, Zhao, Zhou, Jian-jun   +3 more
exaly   +2 more sources

Volatility Forecasting Using a Hybrid GJR-GARCH Neural Network Model

Procedia Computer Science, 2014
AbstractVolatility forecasting in the financial markets, along with the development of financial models, is important in the areas of risk management and asset pricing, among others. Previous testing has shown that asymmetric GARCH models outperform other GARCH family models with regard to volatility prediction.
David Enke
exaly   +2 more sources