Development of new multivariate prediction theory and dynamic dependency analysis in finance
Project/Area Number |
26400139
|
Research Category |
Grant-in-Aid for Scientific Research (C)
|
Allocation Type | Multi-year Fund |
Section | 一般 |
Research Field |
Basic analysis
|
Research Institution | Hiroshima University |
Principal Investigator |
Inoue Akihiko 広島大学, 理学研究科, 教授 (50168431)
|
Co-Investigator(Kenkyū-buntansha) |
笠原 雪夫 北海道大学, 理学(系)研究科(研究院), 研究員 (10399793)
|
Project Period (FY) |
2014-04-01 – 2018-03-31
|
Project Status |
Completed (Fiscal Year 2017)
|
Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2016: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2015: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2014: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
|
Keywords | 予測理論 / 非マルコフ / 確率解析 / ARMA 過程 / 偏相関関数 / 有限予測誤差 / ARMA過程 / 直交多項式 / Verblunsky 係数 / Baxterの不等式 / 短期金利モデル / 非マルコフ・モデル |
Outline of Final Research Achievements |
(1) In joint work with M. Pourahmadi, Inoue and Kasahara completed the work to extend the new method in prediction theory to multivariate processes. (2) In joint work with Shingo Moriuchi and Yusuke Nakamura, A. Inoue introduced a new short rate model which has several good properties. This is an application of a framework introduced by A. Inoue and others, which admits the use of stochastic analysis in non-Markovian set up. (3) The ARMA processes are one of the most important models in discrete-time stationary processes. In joint work, A. Inoue and Y. Kasahara derived closed-form Matrix expressions for partial autocorrelation functions and finite prediction errors of univariate ARMA processes.
|
Report
(5 results)
Research Products
(27 results)