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Abstract:
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There has been a flurry of activity in the last two decades in reparametrizing Cholesky factors of correlation matrices using hyperspherical coordinates. The ensuing angles are hard to interpret statistically, nevertheless we demonstrate that they are quite flexible and effective for guaranteeing the positive-definiteness and parsimonious modeling of large structured correlation matrices commonly encountered in finance. Asymptotic normality of the maximum likelihood estimates of the angles is established. Real examples will be used to demonstrate the flexibility and applicability of the methodology. (Joint work with Ruey Tsay, Booth School of Business, U of Chicago)
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