Name
Abstract | ||
|---|---|---|
•We conducted new simulations on S&P 500. From this new simulation, we can see that the portfolio behaves as we expected as it achieves a good tradeoff among different metrics of interest. However, since the initial set of assets changes and we have no preporcessing on the large scale data, the performance will deteriorate to some degree as we expect.•We explained some technical points in terms of the proposed algorithm, especially in terms of the efficiency.•We revised some illustrations of the focus of this paper to make our point more clear. This paper proposed a sparse risk parity portfolio and the corresponding fast solving numerical algorithm. The algorithm can design the portfolio weights well with the support of read data simulation. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.sigpro.2019.107433 | Signal Processing |
Keywords | Field | DocType |
Portfolio selection,Risk diversification,Sparsity,Risk parity,Successive convex optimization | Mathematical optimization,Transaction cost,Risk parity,Financial crisis,Portfolio,Risk management,Diversification (marketing strategy),Sequential algorithm,Convex optimization,Mathematics | Journal |
Volume | ISSN | Citations |
170 | 0165-1684 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Linlong Wu | 1 | 31 | 3.88 |
| Yiyong Feng | 2 | 11 | 3.55 |
| Daniel P. Palomar | 3 | 7 | 0.77 |