一种计算高维风险平价组合的快速算法

Computing Technologies eJournal Pub Date : 2013-09-01 DOI:10.2139/ssrn.2325255

T. Griveau-Billion, J. Richard, T. Roncalli

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引用次数: 29

摘要

本文提出了一种求解高维风险奇偶问题的循环坐标下降(CCD)算法。我们证明了该算法即使在大协方差矩阵(n > 500)下也是收敛的，并且速度非常快。与现有算法的比较也表明它是最有效的算法之一。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A Fast Algorithm for Computing High-Dimensional Risk Parity Portfolios

In this paper we propose a cyclical coordinate descent (CCD) algorithm for solving high dimensional risk parity problems. We show that this algorithm converges and is very fast even with large covariance matrices (n > 500). Comparison with existing algorithms also shows that it is one of the most efficient algorithms.

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Computing Technologies eJournal

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