Data-Driven Distributionally Robust Risk Parity Portfolio Optimization

@article{Costa2020DataDrivenDR,
  title={Data-Driven Distributionally Robust Risk Parity Portfolio Optimization},
  author={Giorgio Costa and Roy H. Kwon},
  journal={ERN: Statistical Decision Theory; Operations Research (Topic)},
  year={2020}
}
  • Giorgio Costa, R. Kwon
  • Published 12 September 2020
  • Mathematics, Economics, Computer Science
  • ERN: Statistical Decision Theory; Operations Research (Topic)
We propose a distributionally robust formulation of the traditional risk parity portfolio optimization problem. Distributional robustness is introduced by targeting the discrete probabilities attached to each observation used during parameter estimation. Instead of assuming that all observations are equally likely, we consider an ambiguity set that provides us with the flexibility to find the most adversarial probability distribution based on the investor’s confidence level. This allows us to… 

References

SHOWING 1-10 OF 53 REFERENCES
Generalized Risk Parity Portfolio Optimization: An ADMM Approach
The risk parity solution to the asset allocation problem yields portfolios where the risk contribution from each asset is made equal. We consider a generalized approach to this problem. First, we set
A robust framework for risk parity portfolios
TLDR
A novel robust risk parity model is presented that introduces robustness around both the overall portfolio risk and the assets’ marginal risk contributions and yields a higher risk-adjusted rate of return than the nominal model while maintaining a sufficiently risk-diverse portfolio.
Distributionally Robust Optimization Under Moment Uncertainty with Application to Data-Driven Problems
TLDR
This paper proposes a model that describes uncertainty in both the distribution form (discrete, Gaussian, exponential, etc.) and moments (mean and covariance matrix) and demonstrates that for a wide range of cost functions the associated distributionally robust stochastic program can be solved efficiently.
Ambiguous Risk Measures and Optimal Robust Portfolios
  • G. Calafiore
  • Mathematics, Computer Science
    SIAM J. Optim.
  • 2007
TLDR
An efficient method is proposed for determining optimal robust portfolios of risky financial instruments in the presence of ambiguity (uncertainty) on the probabilistic model of the returns to determine portfolios that minimize the maximum with respect to all the allowable distributions of a weighted risk-mean objective.
Robust Portfolio Optimization
TLDR
This work shows that the risk of the estimated portfolio converges to the oracle optimal risk with parametric rate under weakly dependent asset returns, thus allowing for heavy-tailed asset returns.
Least-Squares Approach to Risk Parity in Portfolio Selection
The risk parity portfolio selection problem aims to find such portfolios for which the contributions of risk from all assets are equally weighted. Portfolios constructed using the risk parity
Investigating the effectiveness of robust portfolio optimization techniques
Data uncertainty is a common feature in most of the real-life optimization problems. Despite that, the usual approach in mathematical optimization is to assume that all the input data are known
Computing equal risk contribution portfolios
TLDR
This paper reviews the nonlinear optimization models that underlie the ERC approach and compares the performance of several nonlinear programming algorithms when constructing ERC portfolios and suggests that performance worsens with a poor choice of algorithm or a bad problem formulation.
The worst-case risk of a portfolio
We show how to compute in a numerically efficient way the maximum risk of a portfolio, given uncertainty in the means and covariances of asset returns. This is a semidefinite programming problem, and
The Price of Robustness
TLDR
An approach is proposed that flexibly adjust the level of conservatism of the robust solutions in terms of probabilistic bounds of constraint violations, and an attractive aspect of this method is that the new robust formulation is also a linear optimization problem, so it naturally extend to discrete optimization problems in a tractable way.
...
1
2
3
4
5
...