Designing Simulations to Compare Minimum Variance Portfolio Methods

Summary

The document concerns how to design a simulation study comparing hierarchical risk parity with the analytical global minimum variance portfolio formula for stocks. The question highlights that results may depend on assumptions about the covariance structure and the size of shocks, and asks how to vary such inputs systematically when manipulating covariance and mean matrices.

No answer, method, or supporting evidence is included. The material identifies important simulation design choices but does not provide an accepted parameterization, references, or guidance for evaluating the comparison. Its practical contribution is therefore limited to framing the problem and the factors that may influence conclusions.

Key ideas

• The proposed comparison is between hierarchical risk parity and analytical global minimum variance weighting.
• Covariance magnitudes and stock shocks are identified as factors that may affect simulated results.
• The question asks how to vary means and covariances systematically in a simulation study.
• No solution, evidence, or references are provided.

Tags

# Controlling for factors that influence minimum variance optimization


# Controlling for factors that influence minimum variance optimization












I am trying to compare the performance of two minimum variance optimization (mvpo) methods applied on stocks Hierarchical risk parity (HRP) vs the analytical global minimum variance formula.

I feel like using naively chosen empirical data simulated data will affect the results in an unpredictable way. One of those factors is the magnitude of the covariance between assets, another is shocks to the stocks.

My question is if there is an accepted way of varying parameters like this in a simulation study where the covariance and mean matrix are manipulated. I am new to this field, so any specific research or part in a book would be appreciated.

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