How To Fix RY325 - Covariance matrix is not positively definite

RY325 - Overview

• Message type: E = Error

• Message class: RY - Messages for RM Basis

• Message number: 325

• Message text: Covariance matrix is not positively definite

What causes this issue?

The covariance matrix is not positively definite. Consequently, no
Choleskyanalysis of the covariance matrix can be performed. The
calculated VaRs are only applied conditionally.

System Response

The system issues an error message and will not allow you to continue with this transaction until the error is resolved.

How to fix this error?

Align variances and covariances in such a way so that the covariance
matrix is positively definite. Alternatively,
use an external Monte Carlo calculator (user exit level 1) to split up
the covariance matrix with the single value analysis. (The algorithm for
the single value analysis of a matrix cannot be applied in the case of
non-positively defined matrices.)

Error message extract from SAP system. Copyright SAP SE.

Smart SAP Assistant

• What is the cause and solution for SAP error message RY325 - Covariance matrix is not positively definite ?

  The SAP error message RY325, which states "Covariance matrix is not positively definite," typically occurs in the context of statistical analysis or optimization problems within SAP systems, particularly in the SAP BW (Business Warehouse) or SAP Analytics Cloud environments. This error indicates that the covariance matrix used in a calculation (such as regression analysis or other statistical modeling) is not suitable for the intended mathematical operations.
  
  Cause:
  
  
  Data Issues: The data used to compute the covariance matrix may have issues such as:
  
  Multicollinearity: High correlation between independent variables.
  Insufficient variability: Some variables may have little to no variation.
  Missing values: Incomplete data can lead to an invalid covariance matrix.
  
  
  
  Model Specification: The model being used may not be correctly specified, leading to an invalid covariance structure.
  
  
  Sample Size: A small sample size relative to the number of variables can lead to an unstable covariance matrix.
  
  
  Numerical Precision: In some cases, numerical precision issues can lead to a covariance