Autoregressive conditional heteroskedasticity

In econometrics, an autoregressive conditional heteroskedasticity (ARCH) model considers the variance of the current error term to be a function of the variances of the previous time period's error terms.

If an autoregressive moving average model is assumed for the error variance, the model is a generalized autoregressive conditional heteroskedasticity (GARCH) model.

Generally, when testing for heteroskedasticity in econometric models, the best test is the White test . However, when dealing with time series data, the best test is Engle's ARCH test.

References

• Tim Bollerslev. "Generalized Autorregressive Conditional Heteroskedasticity", Journal of Econometrics, 31:307-327, 1986.

• Robert F. Engle. "Autoregressive Conditional Heteroscedasticity with Estimates of Variance of United Kingdom Inflation", Econometrica 50:987-1008, 1982. (the paper which sparked the general interest in ARCH models)

• Robert F. Engle. "GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics", Journal of Economic Perspectives 15(4):157-168, 2001. (a short, readable introduction)

External links

• ARCH and GARCH models for forecasting volatility, quantnotes.com