Journal of Investment Strategies

Risk.net

Statistical risk models

Zura Kakushadze and Willie Yu

  • Complete algorithms and source code for constructing statistical risk models.
  • Methods for fixing the number of risk factors, including based on effective rank.
  • A complete non-iterative algorithm and source code for computing eigenpairs.
  • The presentation is intended to be essentially self-contained and pedagogical.

In this paper, we give complete algorithms and source code for constructing statistical risk models, including methods for fixing the number of risk factors. One such method is based on effective rank (eRank) and yields results similar to (and further validates) the prior method of Kakushadze. We also give a complete algorithm and source code for computing eigenvectors and eigenvalues of a sample covariance matrix, (i) which requires no costly iterations and (ii) for which the number of operations is linearly proportional to the number of returns. This paper is intended to be pedagogical and oriented toward practical applications.

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