Asymptotic Filtering Theory for Multivariate ARCH Models / Daniel B. Nelson.

Format:

Book

Author/Creator:

Nelson, Daniel B.

Contributor:

National Bureau of Economic Research.

Series:

Technical Working Paper Series (National Bureau of Economic Research) no. t0162.

NBER technical working paper series no. t0162

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 1994.

Summary:

ARCH models are widely used to estimate conditional variances and covariances in financial time series models. How successfully can ARCH models carry out this estimation when they are misspecified? How can ARCH models be optimally constructed? Nelson and Foster (1994) employed continuous record asymptotics to answer these questions in the univariate case. This paper considers the general multivariate case. Our results allow us, for example, to construct an asymptotically optimal ARCH model for estimating the conditional variance or conditional beta of a stock return given lagged returns on the stock, volume, market returns, implicit volatility from options contracts, and other relevant data. We also allow for time-varying shapes of conditional densities (e.g., `heteroskewticity` and `heterokurticity'). Examples are provided.

Notes:

Print version record

August 1994.