A Statistical Learning Analysis to Norm-Constrained Portfolio Optimization
| Project/Area Number |
20710120
|
|---|
| Research Category |
Grant-in-Aid for Young Scientists (B)
|
| Allocation Type | Single-year Grants |
|---|
| Research Field |
Social systems engineering/Safety system
|
|---|
| Research Institution | Chuo University |
|---|
Principal Investigator |
GOTO Junya Chuo University, 理工学部, 准教授 (40334031)
|
|---|
| Project Period (FY) |
2008 – 2010
|
| Project Status |
Completed (Fiscal Year 2010)
|
| Budget Amount *help |
¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
Fiscal Year 2010: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2009: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2008: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
|
|---|
| Keywords | ポートフォリオ最適化 / VaR(バリュー・アット・リスク) / CVaR(条件付きバリュー・アット・リスク) / 汎化誤差 / ノルム制約 / ポートフォリオ選択 / 正則化 / バリューアットリスク(VaR) / 条件付きVaR(CVaR) / support vector machine(SVM) |
|---|
| Research Abstract |
In this project, we investigate a portfolio optimization approach on the basis of the minimizations of VaR and CVaR, which have obtained a growing popularity both in practice and theory. Employing a theoretical underpinning known as the generalization theory for the ν-SVM, a statistical learning method, we empirically show that the norm-constrained versions of VaR and CVaR minimizations achieve better out-of-sample performance than the unconstrained versions.
|
Report
(4 results)
Research Products
(28 results)
