Conditional heteroskedasticity in asset returns: A new approach. (English) Zbl 0722.62069

Nelson, Daniel B.

Econometrica 59, No. 2, 347-370 (1991).

Summary: GARCH models have been applied in modelling the relation between conditional variance and asset risk premia. These models, however, have at least three major drawbacks in asset pricing applications:
(i) Researchers have found a negative correlation between current returns and future returns volatility. GARCH models rule this out by assumption. (ii) GARCH models impose parameter restrictions that are often violated by estimated coefficients and that may unduly restrict the dynamics of the conditional variance process. (iii) Interpreting whether shocks to conditional variance “persist” or not is difficult in GARCH models, because the usual norms measuring persistence often to do not agree.
A new form of ARCH is proposed that meets these objections. The method is used to estimate a model of the risk premium on the CRSP Value-Weighted Market Index from 1962 to 1987.

MSC:

62P20	Applications of statistics to economics
91B84	Economic time series analysis
62M10	Time series, auto-correlation, regression, etc. in statistics (GARCH)

Keywords:

autoregressive conditional heteroskedasticity; generalized autoregressive conditional heteroskedasticity; exponential ARCH; market volatility; nonlinear time series; GARCH models; conditional variance; asset risk premia; asset pricing applications