The vector autoregressive model has long been used for portfolio analysis, while a recent extension (VARX) incorporates exogenous factors. Despite its increased forecasting precision, the ...

Since the vector autoregressive models are estimates from the Yule-Walker equations, not by maximum likelihood, the exact likelihood values are not available for computing the AIC. However, for the ...

Impulse response functions from time series models are standard tools for analyzing the relationship between economic variables. The asymptotic distribution of orthogonalized impulse responses is ...

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

If there are no exogenous components to the vector autoregression model, including no intercepts, then assign zero to each of the variables. There must be an assignment to each of the variables before ...

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

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

Vector autoregression with exogenous variables (VARX) has been used as a powerful model for portfolio returns. We provide a regularization method, based on network analysis, that reduces the ...