Statistical inference for extended models in financial time series
| Project/Area Number |
23330075
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Economic statistics
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| Research Institution | Hiroshima University of Economics |
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Principal Investigator |
MAEKAWA Koichi 広島経済大学, 経済学研究科(研究院), 教授 (20033748)
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| Co-Investigator(Kenkyū-buntansha) |
TOKUTSU Yasuyoshi 広島経済大学, 経済学部, 准教授 (30412282)
KAWAI Ken-ichi 別府大学, 国際経済学部, 准教授 (50425831)
MORIMOTO Tkayuki 関西学院大学, 理工学部, 准教授 (80402543)
KATAYAMA Naoya 関西大学, 経済学部, 准教授 (80452720)
NAGATA Shuichi 関西学院大学, 商学部, 助教 (50546893)
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| Project Period (FY) |
2011-04-01 – 2014-03-31
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| Project Status |
Completed (Fiscal Year 2013)
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| Budget Amount *help |
¥9,880,000 (Direct Cost: ¥7,600,000、Indirect Cost: ¥2,280,000)
Fiscal Year 2013: ¥2,730,000 (Direct Cost: ¥2,100,000、Indirect Cost: ¥630,000)
Fiscal Year 2012: ¥3,250,000 (Direct Cost: ¥2,500,000、Indirect Cost: ¥750,000)
Fiscal Year 2011: ¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
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| Keywords | Nonstandard time series / GARCH error / Error correction model / High frequency data / Structural change / Bootstrap method / Realized volatility / Long memory / 経済時系列分析 / 誤差修正モデル / 構造変化の検定 / 非定常時系列 / ブートストラップ法 / 高頻度データ / GARCHモデル / 長期記憶系列 / ジャンプ過程 / 誤差修正モデル「国際研究者交流」 / 一般化最小２乗法 / 証券市場のバブル 「国際研究者交流」 / 時系列の構造変化 / 多変量GARCH Model / Error Correction Model / Jump過程 / ファイナンス時系列 / 一般化最小2乗法 / 最尤推定法 |
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| Research Abstract |
We studied problems in statistical analysis of financial time series. Since those problems cannot be dealt with classical statistical theory and methods a new research field in econometrics has emerged. As the results the appropriate theory and methods have been developed for problems concerning to the keyword listed below. But even now we think there remain many unsolved problems for compound problems related to plural keywords, such as estimation problems related to vector error correction model with GARCH error, long memory in GARCH process, structural change in high frequency data, modeling of realized volatility by using high frequency time series and so on. We challenged to such problems and attained some significant results including a proposal of some suitable method to such new problems. In addition we evaluated our theoretical and methodological results by computer simulation and applied them to real data.
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Report
(4 results)
Research Products
(41 results)
