 Asymptotic Filtering Theory for Multivariate ARCH ModelsDaniel B. Nelson No 162, NBER Technical Working Papers from National Bureau of Economic Research, Inc Abstract: 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. JEL-codes: C32 (search for similar items in EconPapers) Published as Nelson, Daniel B. "Asymptotic Filtering Theory For Multivariate ARCH Models," Journal of Econometrics, 1996, v71(1&2,Mar/Apr), 1-47. Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:nbr:nberte:0162 Ordering information: This working paper can be ordered from Access Statistics for this paper More papers in NBER Technical Working Papers from National Bureau of Economic Research, Inc National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.. Contact information at EDIRC. |
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