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Term Structure Models During the Global Financial Crisis : A Parsimonious Text Mining Approach
http://hdl.handle.net/2261/00076346
http://hdl.handle.net/2261/000763468c81be54-09bb-43d0-8e69-891ef9156394
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
cb-wp003.pdf (1.1 MB)
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| Item type | テクニカルレポート / Technical Report(1) | |||||
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| 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 | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_18gh | |||||
| 資源タイプ | technical report | |||||
| 著者 |
Nishimura, Kiyohiko G.
× Nishimura, Kiyohiko G.× Sato, Seisho× Takahashi, Akihiko |
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| 著者所属 | ||||||
| 値 | National Graduate Institute for Policy Studies (GRIPS) and CARF, University of Tokyo | |||||
| 著者所属 | ||||||
| 値 | 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. |
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| 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 |
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| 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/ | |||||
