Real World Examples of Singular Matrices in Finance

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Singular matrices, which are not invertible, are rare in real-world finance applications, but ill-conditioned matrices, with determinants close to zero, are more common. These ill-conditioned matrices can lead to significant variations in results due to small changes in parameters. The discussion highlights the challenge of finding specific financial examples where these types of matrices arise. The condition number of a matrix indicates its sensitivity to fluctuations, and any financial application can potentially yield such matrices depending on the data used. Understanding these concepts is crucial for accurate financial modeling and analysis.
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Homework Statement



Hi, no variables, eqns or any of that here:) I was hoping some of you brilliant people could help me in finding some real world examples of where singular matrices occur. I do know that most real world problems yield matrices that are singular and therefore not invertible. We can use a generalized inverse on these problems, but I am finding it difficult to find some real world examples of this, specifically in the field of finance.

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The Attempt at a Solution

 
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In real world applications singular matrices occur very seldom, since the elements are real numbers.
What is common is badly conditioned matrices: matrices whose determinant is not zero, but is very close to. In this case, small fluctuations in any parameter yields enormous variations in the result.
 
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hey thanks so much.

I have been looking at ill-conditioned matrices. I was hoping to find some specific financial application that yields ill-conditioned matrices. (i.e. there condition number is very large)
 
calli said:
hey thanks so much.

I have been looking at ill-conditioned matrices. I was hoping to find some specific financial application that yields ill-conditioned matrices. (i.e. there condition number is very large)

What makes a matrix singular or ill-conditioned is the value of the elements. Any application can have those types of matrices, depending on the data.
 
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