How To Fix TE253 - Cholesky decomposition cannot be processed due to negative square root

TE253 - Overview

• Message type: E = Error

• Message class: TE - Market Risk Management

• Message number: 253

• Message text: Cholesky decomposition cannot be processed due to negative square root

What causes this issue?

During calculation of Cholesky decomposition, the argument below the
square root becomes negative. In detail, the matrix is not positive
definite.

System Response

The negative square root has no solution in the expected range.
Therefore, the Cholesky decomposition cannot be calculated.

How to fix this error?

Check the market data, especially the data for risk factor &V1& and
above.
Check the market data for same validation date. If this is not
fulfilled, it could be the reason for inconsistencies in the values for
the correlation coefficient.

Procedure for System Administrators

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

Error message extract from SAP system. Copyright SAP SE.

Smart SAP Assistant

• What is the cause and solution for SAP error message TE253 - Cholesky decomposition cannot be processed due to negative square root ?

  The SAP error message TE253, which states "Cholesky decomposition cannot be processed due to negative square root," typically occurs in the context of financial or statistical calculations, particularly when dealing with covariance matrices or similar mathematical constructs. This error indicates that the matrix being processed is not positive definite, which is a requirement for Cholesky decomposition.
  
  Cause:
  
  Negative Eigenvalues: The matrix may have one or more negative eigenvalues, which means it is not positive definite.
  Data Issues: The underlying data used to create the matrix may contain errors, such as outliers or incorrect values.
  Model Specification: The model or assumptions used to generate the covariance matrix may be incorrect or inappropriate for the data.
  Numerical Instability: In some cases, numerical precision issues can lead to this error, especially with large datasets or poorly scaled data.
  
  
  Solution:
  
  Check Data Quality: Review the input data for any anomalies, outliers, or incorrect values that could affect the covariance matrix.
  Adjust Model: