The Most General Methodology to Create a Valid Correlation Matrix for Risk Management and Option Pricing Purposes

By R. Rebonato, P. Jaeckel. 2000

294 citations recorded by the source corpus.

Subfields: Finance

View Paper: https://www.semanticscholar.org/paper/315e6ceb6ec6b57cee9749dc3a339f18c376d999

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Citations in the corpus

A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations (2011, unknown): “In the copula framework, the correlation matrix is the key parameter via Sklar’s theorem. The mean and variance parts of yt can be considered separately as marginal models; see Sklar (1959). To develop our results in the current multivariate setting, we adopt the following matrix notation.”
The Composite Marginal Likelihood (CML) Inference Approach with Applications to Discrete and Mixed Dependent Variable Models (2014, background): “Studies of two correlated ordered-response outcomes include Scotti (2006), Mitchell and Weale (2007), Scott and Axhausen (2006), and LaMondia and Bhat Kanaroglou (2011).”
A feasible method for optimization with orthogonality constraints (2012, methodology): “The same decomposition X = V >V are employed in the geometric optimization methods [26], majorization method [41] and trigonometric parametrization methods [44].”
Parameterizing correlations: a geometric interpretation (2007, methodology): “In this paper we will revise the “standard angles parameterization” (SAP) procedure described for example in Pinheiro and Bates [5], recently applied to financial modeling by Rebonato and Jäckel [8], and discuss a geometric interpretation to it.”
A new two-stage multivariate quantile mapping method for bias correcting climate model outputs (2019, methodology): “To solve this problem, a spectral decomposition proposed by Rebonato and Jäckel (2000) is used.”
Using Copulas for Modeling Stochastic Dependence in Power System Uncertainty Analysis (2009, methodology): “However, as mentioned in [18], empirical studies show that the results obtained using this second approach are extremely close, albeit not identical, to the ones obtained using the first technique.”
Robust Markowitz mean‐variance portfolio selection under ambiguous covariance matrix (2016, unknown): “The extension to multivariate assets for d ≥ 2 can also be done within our framework with a parametric form for the correlation matrix using for instance d(d − 1)/2 angular coordinates as in [31].”
Stochastic generation of multi-site daily precipitation focusing on extreme events (2018, background): “If ̂X is not positive-definite, the closest positive-definite matrix is considered (Rousseeuw and Molenberghs, 1993; Rebonato and Jaeckel, 2011).”