Penalized Optimization Approaches to Control Lower-partial Risk Using Factor Model
Project/Area Number |
23710176
|
Research Category |
Grant-in-Aid for Young Scientists (B)
|
Allocation Type | Multi-year Fund |
Research Field |
Social systems engineering/Safety system
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Research Institution | Chuo University |
Principal Investigator |
GOTOH Jun-ya 中央大学, 理工学部, 准教授 (40334031)
|
Project Period (FY) |
2011-04-28 – 2015-03-31
|
Project Status |
Completed (Fiscal Year 2014)
|
Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2014: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2013: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2012: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2011: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
|
Keywords | ポートフォリオ選択 / CVaR / コヒレントリスク尺度 / 正則化 / SVM / 凸リスク尺度 / ロバスト最適化 / リスク最小化 / 実数空間上のノルム / 凸最適化 |
Outline of Final Research Achievements |
In this project, we consider a portfolio selection problems which minimizes the conditional value-at-risk or its generalized versions. It is known that solutions obtained so as to optimize over historical data do not necessarily optimal to future realization. In order to improve on such an out-of-sample performance, we employed several regularized approaches and examined their effectiveness.
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Report
(5 results)
Research Products
(32 results)