Term Structure Models During the Global Financial Crisis: A Parsimonious Text Mining Approach(Forthcoming in "Asia-Pacific Financial Markets". )
Kiyohiko G. Nishimura,
Seisho Sato and
Additional contact information
Kiyohiko G. Nishimura: National Graduate Institute for Policy Studies (GRIPS) and CARF, University of Tokyo
Seisho Sato: Gradtuate School of Economics and CARF, University of Tokyo
Akihiko Takahashi: Gradtuate School of Economics and CARF, University of Tokyo
No CARF-F-446, CARF F-Series from Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo
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 influencing 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.
New Economics Papers: this item is included in nep-big and nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:cfi:fseres:cf446
Access Statistics for this paper
More papers in CARF F-Series from Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo Contact information at EDIRC.
Bibliographic data for series maintained by ().