| bibtype |
V -
Research Report
|
| ARLID |
0468834 |
| utime |
20240103213339.0 |
| mtime |
20170111235959.9 |
| title
(primary) (eng) |
Sparse robust portfolio optimization via NLP regularizations |
| publisher |
| place |
Praha |
| name |
ÚTIA AV ČR v. v. i. |
| pub_time |
2016 |
|
| specification |
| page_count |
19 s. |
| media_type |
P |
|
| edition |
| name |
Research Report |
| volume_id |
2358 |
|
| keyword |
Conditional Value-at-Risk |
| keyword |
Value-at-Risk |
| keyword |
risk measure |
| author
(primary) |
| ARLID |
cav_un_auth*0280972 |
| name1 |
Branda |
| name2 |
Martin |
| full_dept (cz) |
Ekonometrie |
| full_dept (eng) |
Department of Econometrics |
| department (cz) |
E |
| department (eng) |
E |
| institution |
UTIA-B |
| full_dept |
Department of Decision Making Theory |
| country |
CZ |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0220207 |
| name1 |
Červinka |
| name2 |
Michal |
| full_dept (cz) |
Matematická teorie rozhodování |
| full_dept |
Department of Decision Making Theory |
| department (cz) |
MTR |
| department |
MTR |
| institution |
UTIA-B |
| full_dept |
Department of Decision Making Theory |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0332700 |
| name1 |
Schwartz |
| name2 |
A. |
| country |
DE |
|
| source |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0294967 |
| project_id |
GA13-01930S |
| agency |
GA ČR |
| country |
CZ |
|
| project |
| ARLID |
cav_un_auth*0321507 |
| project_id |
GA15-00735S |
| agency |
GA ČR |
|
| abstract
(eng) |
We deal with investment problems where we minimize a risk measure under a condition on the sparsity of the portfolio. Various risk measures are considered including Value-at-Risk and Conditional Value-at-Risk under normal distribution of returns and their robust counterparts are derived under moment conditions, all leading to nonconvex objective functions. We propose four solution approaches: a mixed-integer formulation, a relaxation of an alternative mixed-integer reformulation and two NLP regularizations. In a numerical study, we compare their computational performance on a large number of simulated instances taken from the literature. |
| abstract
(eng) |
We deal with investment problems where we minimize a risk measure\nunder a condition on the sparsity of the portfolio. Various risk measures\nare considered including Value-at-Risk and Conditional Value-at-Risk\nunder normal distribution of returns and their robust counterparts are\nderived under moment conditions, all leading to nonconvex objective\nfunctions. We propose four solution approaches: a mixed-integer formulation,\na relaxation of an alternative mixed-integer reformulation and\ntwo NLP regularizations. In a numerical study, we compare their computational\nperformance on a large number of simulated instances taken\nfrom the literature. |
| RIV |
BB |
| reportyear |
2017 |
| num_of_auth |
3 |
| mrcbC52 |
4 O 4o 20231122142157.0 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0266849 |
| cooperation |
| ARLID |
cav_un_auth*0340903 |
| name |
Matematicko-fyzikalni fakulta UK |
| institution |
MFF UK |
|
| cooperation |
| ARLID |
cav_un_auth*0340904 |
| name |
Fakulta socialnich ved UK |
| institution |
FSV UK |
|
| cooperation |
| ARLID |
cav_un_auth*0340905 |
| name |
Technische Universitaet Darmstadt |
| country |
DE |
|
| confidential |
S |
| arlyear |
2016 |
| mrcbTft |
\nSoubory v repozitáři: 0468834.pdf |
| mrcbU10 |
2016 |
| mrcbU10 |
Praha ÚTIA AV ČR v. v. i. |
|