Skip to main content

Application of the CIR Model for Spot Short Interest Rates Modelling on the Polish Market

  • Conference paper
  • First Online:
Cyclostationarity: Theory and Methods – IV (CSTA 2017)

Part of the book series: Applied Condition Monitoring ((ACM,volume 16))

Included in the following conference series:

  • 275 Accesses

Abstract

The paper examines the estimation of the instantaneous Polish short term interest rate using one of the most popular stochastic differential models for studying the short interest rates, i.e. the Cox, Ingersoll, Ross model (1985) (henceforth CIR). We propose a new approach to estimating an instantaneous short interest rate: our attention is shifted from the whole term structure of the interest rate to the artificial notation of the short rate. In particular, the method focusing on determining a relationship between an observed instantaneous short interest rate and a certain (abstract) unobserved instantaneous rate which is defined as an interest rate demanded over an infinitesimally short period under the risk-neutral measure. To estimate the CIR model, we use a state space model in which estimates of the latent variables and model parameters are obtained by applying an Expectation-Maximisation algorithm combined with particle filters (PF). In practice, the instantaneous rate is identified with an overnight rate, therefore during the research we have adopted daily domestic interbank lending rates which are represented by interest rates on overnight deposits (WIBOR ON). To facilitate the discussion, simulated data are also employed. The obtained results prove the correctness and attractiveness of the method under consideration.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    It is worth noting that Bessel function approaches the plus infinity rapidly, which hinders the optimization routines. However the issue will not be addressed in this paper.

  2. 2.

    We use the Euler discretization with correction, known as “the full truncation scheme” studied in (Higham et al. 2002).

References

Journal article

  • Aït-Sahalia Y (1996) Nonparametric pricing of interest rate derivative securities. Econometrica 64:527–560

    Article  Google Scholar 

  • Briers M, Doucet A, Maskell S (2010) Smoothing algorithms for state-space models. Ann Inst Stat Math 62:61–89

    Article  MathSciNet  Google Scholar 

  • Brzozowska-Rup K, Dawidowicz A (2009) Metoda filtru cząsteczkowego. Matematyka Stosowana 10:69–107

    Google Scholar 

  • Cappé O, Godsill SJ, Moulines E (2007) An overview of existing methods and recent advances in sequential Monte Carlo. Proc IEEE 95(5):899–924

    Article  Google Scholar 

  • Cappé O (2011) Expectation-Maximisation. In: Mengersen K, Titterington M, Robert CP (eds) Mixtures: estimation and applications. Wiley, pp 1–53

    Google Scholar 

  • Cox JC, Ingersoll JE, Ross S (1985) A theory of term structure of interest rates. Econometrica 53:385–407

    Article  MathSciNet  Google Scholar 

  • Chatterjee S (2005) Application of the Kalman filter for estimating Continuous time term structure models: the case of UK and Germany. Working paper, University of Glasgow

    Google Scholar 

  • Chen SY (2012) Kalman filter for robot vision: a survey. IEEE Trans Ind Electron 59(11):4409–4420

    Article  Google Scholar 

  • Christoffersen P, Dorion Ch, Jacobs K, Karoui L (2014) Nonlinear Kalman filtering in affine term structure models. CIRPEE, working paper 14-04

    Google Scholar 

  • Chopin N (2004) Central limit theorem for sequential Monte Carlo methods and its application to Bayesian inference. Ann Stat 32(6):2385–2411

    Article  MathSciNet  Google Scholar 

  • Dempster A, Larid N, Rubin D (1977) Maximum likelihood from incomplete data via the EM algorithm. J Roy Stat Soc B 39(1):1–38

    MathSciNet  MATH  Google Scholar 

  • Djurić PM, Kotecha JH, Zhang J, Huang Y, Ghirmai T, Bugallo MF, Míguez J (2003) Particle filtering. IEEE Signal Process. Mag 20(5):19–38

    Article  Google Scholar 

  • Douc R, Garivier A, Moulines E, Olsson J (2011) Sequential Monte Carlo smoothing for general state space hidden Markov models. Ann Appl Probab 21(6):2109–2145

    Article  MathSciNet  Google Scholar 

  • Douc R, Moulines E (2008) Limit theorems for weighted samples with applications to sequential Monte Carlo methods. Ann Stat 36(5):2344–2376

    Article  MathSciNet  Google Scholar 

  • De Rossi G (2010) Maximum likelihood estimation of the Cox-Ingersoll-Ross model using particle filters. Comput Econ 36(1):1–16

    Article  MathSciNet  Google Scholar 

  • Doucet A, Johansen AM (2008) A tutorial on particle filtering and smoothing: fifteen years later. Technical report, Department of Statistics, University of British Columbia

    Google Scholar 

  • Ghirmai T (2016) Distributed particle filter for target tracking: With reduced sensor communications. Sensors 16(9):1454

    Article  Google Scholar 

  • Gordon N, Salmond D, Smith AF (1993) Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proc F Radar Signal Process 140:107–113

    Article  Google Scholar 

  • Gustafsson F, Gunnarsson F, Bergman N, Forssell U, Jansson J, Karlsson R, Nordlund PJ (2002) Particle filters for positioning, navigation and tracking. IEEE Trans Signal Process 50(2):425–437

    Article  Google Scholar 

  • Higham DJ, Mao X, Stuart AM (2002) Strong convergence of Euler-type methods for nonlinear stochastic differential equations. SIAM J 40:1041–1063

    MathSciNet  MATH  Google Scholar 

  • Kantas N, Doucet A, Singh SS, Maciejowski J, Chopin N (2014) On particle methods for parameter estimation in state-space models. Stat Sci 30(3):328–351

    Article  MathSciNet  Google Scholar 

  • Kladìvko K (2007) Maximum likelihood estimation of Cox-Ingersoll-Ross process: the Matlab implementation. Tech Comput Prague. http://www2.humusoft.cz/www/papers/tcp07/kladivko.pdf

  • Langrock R (2011) Some applications of nonlinear and non-Gaussian state-space modelling by means of hidden Markov models. J Appl Stat 38(12):2955–2970

    Article  MathSciNet  Google Scholar 

  • Nordlund PJ, Gustafsson F (2008) Research on an improved terrain aided positioning model. Technical report LiTH-ISY-R2870, Linköpings Universitet

    Google Scholar 

  • Olsson J, Douc R, Cappé O, Moulines E (2008) Sequential Monte Carlo smoothing with application to parameter estimation in nonlinear state-space models. Bernouli 14(1):155–179

    Article  MathSciNet  Google Scholar 

  • Schön TB, Wills A, Ninness B (2011) System identification of nonlinear state-space models. Automatica 47(1):39–49

    Article  MathSciNet  Google Scholar 

  • Vo LH (2014) Application of Kalman Filter on Modelling Interest Rates. J Manag Sci 1:1–15

    Article  Google Scholar 

  • Zhou HS (2013) Modified backward sampling smoothing with EM algorithm - application to economics and finance. Pre-print, Unpublished report

    Google Scholar 

Book

  • Brigo D, Mercurio F (2001) Interest rate models. Theory and practice. Springer, Heidelberg

    Book  Google Scholar 

  • Cairns AJG (2004) Interest rate models: an introduction. Princeton University Press, Princeton

    Book  Google Scholar 

  • Del Moral P (2004) Feynman-Kac formulae. Genealogical and interacting particle systems with applications. Springer, Heidelberg

    Book  Google Scholar 

  • Del Moral P (2013) Mean field simulation for Monte Carlo integration. Chapman and Hall, Boca Raton

    Book  Google Scholar 

  • Dębski W (2010) Rynek finansowy i jego mechanizmy. Podstawy teorii i praktyki. Wydawnictwo Naukowe PWN, Warszawa

    Google Scholar 

  • Doucet A, de Freitas N, Gordon N (2001) Sequential Monte Carlo methods in practice. Springer, Heidelberg

    Book  Google Scholar 

  • Gibson R, Lhabitant FS, Talay D (2001) Modeling the term structure of interest rates: a review of the literature. http://www.risklab.ch/ftp/papers/TermStructureSurvey.pdf

  • Hol JD, Schon TB, Gustafsson F (2006) On resampling algorithms for particle filters. In: Proceedings IEEE Nonlinear Statistical Signal Processing Workshop, pp 79–82

    Google Scholar 

  • Hull JC (2015) Risk management and financial institutions, 4th edn. Wiley, New York

    Google Scholar 

  • Weron A, Weron R (1999) Inżynieria finansowa. Wycena instrumentów pochodnych. Symulacje komputerowe. Statystyka rynku. Wydawnictwa Naukowo-Techniczne, Warszawa

    Google Scholar 

  • Fabozzi FJ (2002) Interest rate, therm structure and valuation modeling. Wiley, New Jersey

    Google Scholar 

Download references

Acknowledgements

This work is supported by the National Centre of Science granted on the basis of decision number DEC-2013/11/D/HS4/04014.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Katarzyna Brzozowska-Rup .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Brzozowska-Rup, K. (2020). Application of the CIR Model for Spot Short Interest Rates Modelling on the Polish Market. In: Chaari, F., Leskow, J., Zimroz, R., Wyłomańska, A., Dudek, A. (eds) Cyclostationarity: Theory and Methods – IV. CSTA 2017. Applied Condition Monitoring, vol 16. Springer, Cham. https://doi.org/10.1007/978-3-030-22529-2_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-22529-2_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-22528-5

  • Online ISBN: 978-3-030-22529-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics