-- ARIMA (p,d,q): y(t) = c + α1.y(t-1) + … + αp.y(t-p) + β1.ε(t-1) + … + βq.ε(t-q) + εt (univariate) -- ARIMAX: Having a exogenous variables (x) into the ...
This project uses Python and the GARCH(1,1) model to forecast the volatility of the CSI 1000 Index based on historical data. It includes data acquisition, preprocessing, model fitting, volatility ...
Will Kenton is an expert on the economy and investing laws and regulations. He previously held senior editorial roles at Investopedia and Kapitall Wire and holds a MA in Economics from The New School ...
Abstract: In this paper, we introduce a two-dimensional Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model for clutter modeling and anomaly detection. The one-dimensional GARCH ...
The class of generalized autoregressive conditional heteroscedastic (GARCH) models has proved particularly valuable in modelling time series with time varying volatility. These include financial data, ...
1 School of Economics and Statistics, Guangzhou University, Guangzhou, China. 2 Department of Statistics, George Washington University, Washington, USA. This paper aims to study the GARCH-X model ...
Using E-GARCH to Analyze the Impact of Investor Sentiment on Stock Returns Near Stock Market Crashes
1 China-ASEAN International College, Dhurakij Pundit University, Bangkok, Thailand 2 International Master Program in Asia-Pacific Affairs, College of Social Sciences, National Sun Yat-sen University, ...
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