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

  • Kiyohiko G. Nishimura
  • Seisho Sato
  • Akihiko TakahashiEmail author
Article
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Abstract

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.

Keywords

Term structure model Market sentiment Text mining Monte Carlo filter Factor model Quadratic Gaussian model 

Notes

References

  1. Adrian, T. (2017). The term structure of interest rates and macrofinancial dynamics. In Speech at Bank of Canada conference on advances in fixed income and macro-finance research, August 17, 2017.Google Scholar
  2. Bain, A., & Crisan, D. (2008). Fundamentals of stochastic filtering. Berlin: Springer.Google Scholar
  3. Bauer, M. D. (2015). Nominal interest rates and the news. Journal of Money, Credit and Banking, 47(2–3), 295–332.CrossRefGoogle Scholar
  4. Cox, J. C., Ingersoll, J. E., & Ross, S. A. (1985). A theory of the term structure of interest rates. Econometrica, 53, 385–407.CrossRefGoogle Scholar
  5. Fukui, T., Sato, S., & Takahashi, A. (2017). Style analysis with particle filtering and generalized simulated annealing. International Journal of Financial Engineering, 4(02n03), 1750037.CrossRefGoogle Scholar
  6. Gotthelf, N., & Uhl, M. W. (2018). News sentiment: A new yield curve factor. Journal of Behavioral Finance.  https://doi.org/10.1080/15427560.2018.1432620.Google Scholar
  7. Hull, J., & White, A. (1990). Pricing interest-rate-derivative securities. The Review of Financial Studies, 3(4), 573–592.CrossRefGoogle Scholar
  8. Karatzas, I., & Shreve, S. E. (1991). Brownian motion and stochastic calculus. Berlin: Springer.Google Scholar
  9. Karatzas, I., & Shreve, S. E. (1998). Methods of mathematical finance. Berlin: Springer.CrossRefGoogle Scholar
  10. Kitagawa, G. (1996). Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. Journal of Computational and Graphical Statistics, 5(1), 1–25.Google Scholar
  11. Kumar, B. S., & Vadlamani Ravi, V. (2016). A survey of the applications of text mining in financial domain. Knowledge-Based Systems, 114, 128–147.CrossRefGoogle Scholar
  12. Kunitomo, N., Sato, S., & Kurisu, D. (2018). Separating information maximum likelihood method for high-frequency financial data. Berlin: Springer.CrossRefGoogle Scholar
  13. MeCab. (2006). Yet another part-of-speech and morphological analyzer. http://taku910.github.io/mecab/.
  14. Nakano, M., Takahashi, A., Takahashi, S., & Tokioka, T. (2018). On the effect of Bank of Japan’s outright purchase on the JGB yield curve. Asia-Pacific Financial Markets, 25(1), 47–70.CrossRefGoogle Scholar
  15. Nassirtoussi, A. K., Aghabozorgi, S., Waha, T. Y., & Ngo, D. C. L. (2014). Text mining for market prediction: A systematic review. Expert Systems with Applications, 41, 7653–7670.CrossRefGoogle Scholar
  16. Nishimura, K. G. (2008). Recent economic and financial developments and the conduct of monetary policy, speech at the foreign correspondents’ club of Japan, September 29, 2008, Bank of Japan.Google Scholar
  17. Nishimura, K. G., & Ozaki, H. (2017). Economics of pessimism and optimism: Theory of Knightian uncertainty and its applications. Berlin: Springer.CrossRefGoogle Scholar
  18. Rudebusch, G. D., & Wu, T. (2008). A macro-finance model of the term structure, no monetary policy and the economy. Economic Journal, 118(530), 906–926.CrossRefGoogle Scholar
  19. Shirakawa, H. (2002). Squared Bessel processes and their applications to the squared root interest rate model. Asia-Pacific Financial Markets, 9, 169–190.CrossRefGoogle Scholar
  20. Takahashi, A., & Sato, S. (2001). Monte Carlo filtering approach for estimating the term structure of interest rates. Annals of The Institute of Statistical Mathematics, 53(1), 50–62.CrossRefGoogle Scholar

Copyright information

© Springer Japan KK, part of Springer Nature 2019

Authors and Affiliations

  • Kiyohiko G. Nishimura
    • 1
  • Seisho Sato
    • 2
  • Akihiko Takahashi
    • 2
    Email author
  1. 1.National Graduate Institute for Policy Studies (GRIPS) and CARFUniversity of TokyoTokyoJapan
  2. 2.Graduate School of Economics and CARFUniversity of TokyoTokyoJapan

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