Estimation of the Bivariate Stable Spectral Representation by theProjection Method

Authors:
J. Huston McCulloch
Affiliations:
Economics Department, Ohio State University, 1945 N. High St., Columbus, OH 43210, U.S.A. mcculloch.2@osu.edu
Venue:
Computational Economics - Special issue on computational studies at Cambridge
Year:
2000

Citing 3
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Abstract

A method of estimating the spectral representation of a generalized bivariatestable distribution is presented, based on a series of maximum likelihood (ML)estimates of the stable parameters of univariate projections of the data. Thecorresponding stable spectral density is obtained by solving a quadraticprogram. The proposed method avoids the often arduous task of computing themultivariate stable density, relying instead on the standard univariate stabledensity. The paper applies this projection procedure, under the simplifyingassumption of symmetry, to simulated data as well as to foreign exchangereturn data, with favorable results. Kanter projection coefficients governingconditional expectations are computed from the estimated spectral density. For the simulated data these compare well to their known true values.