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  1. 117 経済学研究科・経済学部
  2. Working Papers on Central Bank Communication
  1. 0 資料タイプ別
  2. 60 レポート類
  3. 063 ワーキングペーパー

Term Structure Models During the Global Financial Crisis : A Parsimonious Text Mining Approach

http://hdl.handle.net/2261/00076346
http://hdl.handle.net/2261/00076346
8c81be54-09bb-43d0-8e69-891ef9156394
名前 / ファイル ライセンス アクション
cb-wp003.pdf cb-wp003.pdf (1.1 MB)
Item type テクニカルレポート / Technical Report(1)
公開日 2018-11-06
タイトル
タイトル Term Structure Models During the Global Financial Crisis : A Parsimonious Text Mining Approach
言語
言語 eng
資源タイプ
資源 http://purl.org/coar/resource_type/c_18gh
タイプ technical report
著者 Nishimura, Kiyohiko G.

× Nishimura, Kiyohiko G.

WEKO 152585

Nishimura, Kiyohiko G.

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Sato, Seisho

× Sato, Seisho

WEKO 152586

Sato, Seisho

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Takahashi, Akihiko

× Takahashi, Akihiko

WEKO 152587

Takahashi, Akihiko

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著者所属
著者所属 National Graduate Institute for Policy Studies (GRIPS) and CARF, University of Tokyo
著者所属
著者所属 Graduate School of Economics and CARF, University of Tokyo
抄録
内容記述タイプ Abstract
内容記述 This work develops and estimates a three-factor term structure model with explicit sentiment factors in a period including the global financial crisis, where market confidence was said to erode considerably. It utilizes a large text data of real time, relatively high-frequency market news and takes account of the difficulties in incorporating market sentiment into the models. To the best of our knowledge, this is the first attempt to use this category of data in term-structure models.
Although market sentiment or market confidence is often regarded as an important driver of asset markets, it is not explicitly incorporated in traditional empirical factor models for daily yield curve data because they are unobservable. To overcome this problem, we use a text mining approach to generate observable variables which are driven by otherwise unobservable sentiment factors. Then, applying the Monte Carlo filter as a filtering method in a state space Bayesian filtering approach, we estimate the dynamic stochastic structure of these latent factors from observable variables driven by these latent variables.
As a result, the three-factor model with text mining is able to distinguish (1) a spread-steepening factor which is driven by pessimists' view and explaining the spreads related to ultra-long term yields from (2) a spread-flattening factor which is driven by optimists' view and in uencing the long and medium term spreads. Also, the three-factor model with text mining has better fitting to the observed yields than the model without text mining.
Moreover, we collect market participants' views about specific spreads in the term structure and find that the movement of the identified sentiment factors are consistent with the market participants' views, and thus market sentiment.
内容記述
内容記述タイプ Other
内容記述 Publisher's another name: JSPS Grants-in-Aid for Scientific Research (S) Central Bank Communication Design
書誌情報 Working Papers on Central Bank Communication

巻 003, 発行日 2018-11
著者版フラグ
値 publisher
出版者
出版者 Research Project on Central Bank Communication
関係URI
識別子タイプ URI
関連識別子 http://www.centralbank.e.u-tokyo.ac.jp/en/category/research-data/
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