Finding low-rank solutions in financial factor models

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Terézia Fulová


In financial factor models based on the structure of a correlation matrix, the rank of the correlation matrix should be equal to the number of factors. However, it is not so rare to obtain a high-rank correlation matrix from the given data in practical applications. Therefore, it is necessary to find the nearest low-rank correlation matrix to the computed one. If we take the Frobenius norm to measure the "nearness" of two matrices, we will show that this problem can be formulated in the form of a~rank-constrained semidefinite program. Although this kind of problem is considered to be NP-hard, there are some rank reduction techniques to deal with this non-convex rank constraint.

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Fulová, T. (2020). Finding low-rank solutions in financial factor models. Proceedings Of The Conference Algoritmy, , 161 - 170. Retrieved from


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