Financial Risk Analysis using High Dimensional and/or High Frequency Data

2021 Fiscal Year Final Research Report

• Project/Area Number: 19K01594
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 07030:Economic statistics-related
• Research Institution: Soka University
• Principal Investigator: Asai Manabu 創価大学, 経済学部, 教授 (90319484)
• Project Period (FY): 2019-04-01 – 2022-03-31
• Keywords: 高次元データ / 高頻度データ / 金融資産リスク

Outline of Final Research Achievements

In the analysis of risks of financial assets, the risk is measure by the return volatility (standard deviation or variance). In recent years, high-dimensional and/or high-frequency data are available, and thus researchers can estimate time-varying risks more accurately. In this research project, I focused on three topics; (i) network volatility models, (ii) high-dimensional covariance model for high-frequency data, and (iii) Asymptotic property of QML estimator for the transformed BEKK models. The research results are summarized in eleven articles.

Free Research Field

計量ファイナンス

Academic Significance and Societal Importance of the Research Achievements

金融資産のリスク分析において、高次元・高頻度データが使えるようになってきたとはいえ、その研究は緒に就いたばかりである。実用的なモデルを考案し、リスクの予測力の向上に役立てていくことは、実務上非常に重要である。