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

Author(s):  
Riccardo Rebonato ◽  
Peter Jaeckel
2000 ◽  
Vol 03 (03) ◽  
pp. 391-397 ◽  
Author(s):  
LAURENT LALOUX ◽  
PIERRE CIZEAU ◽  
MARC POTTERS ◽  
JEAN-PHILIPPE BOUCHAUD

We show that results from the theory of random matrices are potentially of great interest when trying to understand the statistical structure of the empirical correlation matrices appearing in the study of multivariate financial time series. We find a remarkable agreement between the theoretical prediction (based on the assumption that the correlation matrix is random) and empirical data concerning the density of eigenvalues associated to the time series of the different stocks of the S&P500 (or other major markets). Finally, we give a specific example to show how this idea can be sucessfully implemented for improving risk management.


2002 ◽  
Vol 26 (2-3) ◽  
pp. 323-345 ◽  
Author(s):  
Alfred Lehar ◽  
Martin Scheicher ◽  
Christian Schittenkopf

2020 ◽  
Vol 54 (2) ◽  
pp. 361-371
Author(s):  
Long Teng ◽  
Xueran Wu ◽  
Michael Günther ◽  
Matthias Ehrhardt

In many areas of finance and of risk management it is interesting to know how to specify time-dependent correlation matrices. In this work we propose a new methodology to create valid time-dependent instantaneous correlation matrices, which we called correlation flows. In our methodology one needs only an initial correlation matrix to create these correlation flows based on isospectral flows. The tendency of the time-dependent matrices can be controlled by requirements. An application example is presented to illustrate our methodology.


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