Economy – Quantitative Finance – Portfolio Management
Scientific paper
2010-05-27
Economy
Quantitative Finance
Portfolio Management
27 pages, 11 figures
Scientific paper
In this short report, we discuss how coordinate-wise descent algorithms can be used to solve minimum variance portfolio (MVP) problems in which the portfolio weights are constrained by $l_{q}$ norms, where $1\leq q \leq 2$. A portfolio which weights are regularised by such norms is called a sparse portfolio (Brodie et al.), since these constraints facilitate sparsity (zero components) of the weight vector. We first consider a case when the portfolio weights are regularised by a weighted $l_{1}$ and squared $l_{2}$ norm. Then two benchmark data sets (Fama and French 48 industries and 100 size and BM ratio portfolios) are used to examine performances of the sparse portfolios. When the sample size is not relatively large to the number of assets, sparse portfolios tend to have lower out-of-sample portfolio variances, turnover rates, active assets, short-sale positions, but higher Sharpe ratios than the unregularised MVP. We then show some possible extensions; particularly we derive an efficient algorithm for solving an MVP problem in which assets are allowed to be chosen grouply.
No associations
LandOfFree
A Note on Sparse Minimum Variance Portfolios and Coordinate-Wise Descent Algorithms does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with A Note on Sparse Minimum Variance Portfolios and Coordinate-Wise Descent Algorithms, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and A Note on Sparse Minimum Variance Portfolios and Coordinate-Wise Descent Algorithms will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-192567