PT  - JOURNAL ARTICLE
AU  - Denis Chaves
AU  - Jason Hsu
AU  - Feifei Li
AU  - Omid Shakernia
TI  - Efficient Algorithms for Computing Risk<br/>Parity Portfolio Weights
AID  - 10.3905/joi.2012.21.3.150
DP  - 2012 Aug 31
TA  - The Journal of Investing
PG  - 150--163
VI  - 21
IP  - 3
4099  - https://pm-research.com/content/21/3/150.short
4100  - https://pm-research.com/content/21/3/150.full
AB  - This article presents two simple algorithms to calculate the portfolio weights for a risk parity strategy, where asset class covariance information is appropriately taken into consideration to achieve “true” equal risk contribution. Previous implementations of risk parity either used a naïve 1/vol solution, which ignores asset class correlations, or computed “true” risk parity weights using relatively complicated optimizations to solve a quadratic minimization program with nonlinear constraints.The two iterative algorithms presented require only simple computations and quickly converge to the optimal solution. In addition to the technical contribution, the authors compute the parity in portfolio “risk allocation” using the Gini coefficient, and confirm that portfolio strategies with parity in “asset class allocation” can actually have high concentration in its “risk allocation.”TOPICS: Portfolio theory, statistical methods, portfolio theory