A statistical model is autoregressive if it predicts future values based on past values (i.e., predicting future stock prices based on past performance).

Based on an idea of Granger (1986. "Oxford Bulletin of Economics and Statistics" 48, 213-228), we analyze a new vector autoregressive model defined from the fractional lag operator $1- (1-L)^ {d}$. We ...

For more information on this research see: A Hybrid Model for Forecasting Realized Volatility Based on Heterogeneous Autoregressive Model and Support Vector Regression. Risks, 2024,12 (1).

This paper gives necessary and sufficient conditions for stationarity and existence of second moments in mixtures of linear vector autoregressive models with autoregressive conditional ...

The generalized autoregressive conditional heteroskedasticity (GARCH) process is an econometric term used to describe an approach to estimate volatility in financial markets.

Using a Global Vector Autoregressive model with quarterly data from 1987 to 2022, we find that external factors such as the imported inflation from main trading partners, mainly driven by China, and ...