Estimating the Wishart Affine Stochastic Correlation Model using the empirical characteristic function

José Da Fonseca, Grasselli Martino and Florian Ielpo
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Grasselli Martino: Università degli Studi di Padova, Dipartimento di Matematica, Via Trieste 63, Padova, Italy; ESILV, Ecole Supérieure d’Ingénieurs Léonard de Vinci, Département Mathématiques et Ingénierie Financière, Paris La Défense, France; and QUANTA FINANZA S.R.L., Via Cappuccina 40, Mestre (Venezia), Italy

Studies in Nonlinear Dynamics & Econometrics, 2014, vol. 18, issue 3, 253-289

Abstract: This paper provides the first estimation strategy for the Wishart Affine Stochastic Correlation (WASC) model. We provide elements showing that the use of empirical characteristic function-based estimates is advisable as this function is exponential affine in the WASC case. We use a GMM estimation strategy with a continuum of moment conditions based on the characteristic function. We present the estimation results obtained using a dataset of equity indexes. The WASC model captures most of the known stylized facts associated with financial markets, including leverage and asymmetric correlation effects.

Keywords: Wishart Process; Empirical Characteristic Function; Stochastic Correlation; Generalized Method of Moments (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (20)

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DOI: 10.1515/snde-2012-0009

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