Financial Network and Financial Public Good : DCC-GARCH, Network Theory, Aggregate Game

研究成果の概要 (和文) : 本研究の成果は(i)動学的相関GARCHモデルを使ってネットネットワーク理論のノード(各国金融市場)とエッジ(市場間の相関係数)を推定し、アジア金融市場のネットワーク構造を現状認識した。(ii)すでに推定されているアジア金融市場のネットワーク構造を使って、コストがより小さくなるように金融公共財をノードやエッジに配置する理論と金融政策の構築を試みた。研究成果の概要 (英文) : This project has theoretically and empirically examined Asian financial network during 2000 and 2017 by using DCCGARCH model. Moreover, we explored the economic policy to achieve network efficiency

プロトン付加メタノール・水混合クラスターの赤外分光研究

Tohoku University藤井朱鳥課

Identification of Network Structures in Dynamical Network Systems Using Koopman Operators

東京都立大学Tokyo Metropolitan University博士（工学）doctoral thesi

投資SNSのネットワーク構造

In this article, we will explore the network structure of SNS specialized in equity investment. Weuse network data from over 40,000 individual investors. This is part of a total number of over 450,000investors in this SNS.Unlike other popular social networks such as Facebook and LinkedIn, this network doesn’t seem tobe human relationship. However, it is important that investors interact to share investment ideas. This isa valuable feature of the investment community SNS.First, we analyzed the entire network. As expected, the entire network consists of a small numberof page owners with a very large number of links（hereinafter referred to as investors）and a largenumber of page owners with a small number of links. Except for the scale-free nature of this network, nonetwork-wide features were found.Next, we adopted the Modularity Q method and divided the entire network into several subnetworks.Subnetwork statistics are completely different when compared to network-wide statistics. Mostsubnetworks have several hubs or authorities. However, the reason for such a network formation is anopen question and need to be explored in the next work

ネットワーク構造の統計的な推定手法について

要旨あり多様性と進化の統計解析研究詳

相対角度情報を用いた分散制御器によるフォーメーションの大域定型性に関する研究

電気通信大学201