The most general methodology for creating a valid correlation matrix for risk management and option pricing purposes
By R. Rebonato, Peter J̈ackel. 2000
112 citations recorded by the source corpus.
Subfields: Computational Mechanics, Environmental Engineering, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/53d9043100a4b42f93a402dacf9d21e842c1768f
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In Computational Mechanics
In Environmental Engineering
In Statistical and Nonlinear Physics
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.”
- Managing computational complexity using surrogate models: a critical review (2020, methodology): “…used to evaluate the outputs of a statistical study and generalize the results to other sets of data (Qian 2009; Qian et al. 2008, 2009a, b; Higdon et al. 2004; IT 2002; Jaeckel and Rebonato 1999; Reese et al. 2004; Shankar Bhattacharjee et al. 2016; Trucano et al. 2006; Kennedy and O’Hagan 2000).”
- 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).”