Testing and modelling autoregressive conditional heteroskedasticity of streamflow processes [PDF]
Nonlinear Processes in Geophysics, 2005 Conventional streamflow models operate under the assumption of constant variance or season-dependent variances (e.g. ARMA (AutoRegressive Moving Average) models for deseasonalized streamflow series and PARMA (Periodic AutoRegressive Moving Average ...
W. Wang +4 more
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A new frontier for studying within-person variability: Bayesian multivariate generalized autoregressive conditional heteroskedasticity models. [PDF]
Psychol Methods, 2022 Rast P, Martin SR, Liu S, Williams DR.
europepmc +2 more sources
Chaos, Fractionality, Nonlinear Contagion, and Causality Dynamics of the Metaverse, Energy Consumption, and Environmental Pollution: Markov-Switching Generalized Autoregressive Conditional Heteroskedasticity Copula and Causality Methods [PDF]
Fractal and FractionalMetaverse (MV) technology introduces new tools for users each day. MV companies have a significant share in the total stock markets today, and their size is increasing.
Melike Bildirici +2 more
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Investigating the Impact of International Sanctions on Performance Indicators of Tehran Stock Exchange with Industries Divided From 2010 to 2020 [PDF]
مدلسازی اقتصادسنجی, 2023 In this research, the impact of the impact of the international sanctions index on the performance indices of the Tehran Stock Exchange by industries, including mass production indices, banks, insurance, automobiles, investments, basic metals, rubber ...
Hamid Reza Vaezian +3 more
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The Volatility Assessment of CO2 Emissions in Uzbekistan: ARCH/GARCH Models
International Journal of Energy Economics and Policy, 2023 The study is pioneer to investigate the volatility of CO2 emissions in Uzbekistan. To this end, ARCH (Autoregressive Conditional Heteroskedasticity) and GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models are used spanning the ...
Bekhzod Kuziboev +4 more
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Bayesian time‐varying autoregressive models of COVID‐19 epidemics
Biometrical Journal, Volume 65, Issue 1, January 2023., 2023 Abstract The COVID‐19 pandemic has highlighted the importance of reliable statistical models which, based on the available data, can provide accurate forecasts and impact analysis of alternative policy measures. Here we propose Bayesian time‐dependent Poisson autoregressive models that include time‐varying coefficients to estimate the effect of policy ...
Paolo Giudici +2 more
wiley +1 more source
Quantification of the stock market value at risk by using FIAPARCH, HYGARCH and FIGARCH models
Data Science in Finance and Economics, 2023 The South African financial market is developing with periods of high and low volatility. Employing an adequate volatility model is essential to manage market risk.
Moses Khumalo +2 more
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A Study on Cryptocurrency Log-Return Price Prediction Using Multivariate Time-Series Model
Axioms, 2022 Cryptocurrencies are highly volatile investment assets and are difficult to predict. In this study, various cryptocurrency data are used as features to predict the log-return price of major cryptocurrencies. The original contribution of this study is the
Sang-Ha Sung +3 more
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Short-term user load forecasting based on GARCH-M family model with different distributions
电力工程技术, 2022 Power load forecasting is one of the basic tasks power system research,and time series analysis is currently the most widely used forecasting method. Aiming at the fluctuation and the characteristics of peak and thick tail of user daily load time series ...
WANG Chen, YE Jiangming, HE Jiahong
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Probabilistic Forecasting of Wind Power Generation Using Online LASSO VAR and EGARCH Model
Shanghai Jiaotong Daxue xuebao, 2023 Wind power generation has uncertainty due to the high fluctuation of wind speed. In traditional wind power prediction models, the uncertainty is measured by normal distribution with zero mean and constant variance.
WANG Peng, LI Yanting, ZHANG Yu
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