| Item Type |
Monograph
(Working Paper)
|
| Abstract |
We propose an averaging rule that combines established minimum-variance strategies to minimize the expected out-of-sample variance. Our rule overcomes the problem of selecting the “best” strategy ex-ante and diversifies remaining estimation errors of the single strategies included in the averaging. Extensive simulations show that the contributions of estimation errors to the out-of-sample variances are uncorrelated between the considered strategies. This implies that averaging over multiple strategies o˙ers sizable diversification benefits. Our rule leverages these benefits and compares favorably to eleven strategies in terms of out-of-sample variance on both simulated and empirical data sets. The Sharpe ratio is across all data sets at least 25% higher than for the 1/N portfolio. |
| Authors |
Füss, Roland; Koeppel, Christian & Miebs, Felix |
| Language |
English |
| Keywords |
Averaging; diversification; estimation error; portfolio optimization; shrinkage |
| Subjects |
economics
finance |
| HSG Classification |
contribution to scientific community |
| HSG Profile Area |
SOF - System-wide Risk in the Financial System |
| Date |
8 February 2021 |
| Publisher |
SoF-HSG |
| Place of Publication |
St.Gallen |
| Series Name |
School of Finance Working Paper Series |
| Volume |
2021/05 |
| Number |
05 |
| Page Range |
1-69 |
| Number of Pages |
69 |
| Contact Email Address |
Roland.Fuess@unisg.ch |
| Depositing User |
Carolin Hitz
|
| Date Deposited |
08 Feb 2021 14:32 |
| Last Modified |
08 Feb 2021 14:32 |
| URI: |
https://www.alexandria.unisg.ch/publications/262294 |
Diversifying estimation errors:
An efficient averaging rule for portfolio optimization
![[img]](http://www.alexandria.unisg.ch/style/images/fileicons/oa_orange.png)
