IDEAS home Printed from https://ideas.repec.org/a/gam/jecnmx/v6y2018i3p34-d158660.html
   My bibliography  Save this article

Financial Big Data Solutions for State Space Panel Regression in Interest Rate Dynamics

Author

Listed:
  • Dorota Toczydlowska

    (Department of Statistical Science, University College London, 1-19 Torrington Place, London WC1E 7HB, UK)

  • Gareth W. Peters

    (Department of Statistical Science, University College London, 1-19 Torrington Place, London WC1E 7HB, UK
    Department of Actuarial Mathematics and Statistics, Heriot-Watt University, Colin Maclaurin Building, Heriot-Watt University, Edinburgh EH14 4AS, UK
    Man Institute of Quantitative Finance, University of Oxford, Oxford OX1 3BD, UK
    Systemic Risk Center, The London School of Economics and Political Science, Houghton Street, London WC2A 2AE, UK)

Abstract

A novel class of dimension reduction methods is combined with a stochastic multi-factor panel regression-based state-space model in order to model the dynamics of yield curves whilst incorporating regression factors. This is achieved via Probabilistic Principal Component Analysis (PPCA) in which new statistically-robust variants are derived also treating missing data. We embed the rank reduced feature extractions into a stochastic representation for state-space models for yield curve dynamics and compare the results to classical multi-factor dynamic Nelson–Siegel state-space models. This leads to important new representations of yield curve models that can be practically important for addressing questions of financial stress testing and monetary policy interventions, which can incorporate efficiently financial big data. We illustrate our results on various financial and macroeconomic datasets from the Euro Zone and international market.

Suggested Citation

  • Dorota Toczydlowska & Gareth W. Peters, 2018. "Financial Big Data Solutions for State Space Panel Regression in Interest Rate Dynamics," Econometrics, MDPI, vol. 6(3), pages 1-45, July.
    • Handle: RePEc:gam:jecnmx:v:6:y:2018:i:3:p:34-:d:158660
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2225-1146/6/3/34/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2225-1146/6/3/34/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Scott Joslin & Marcel Priebsch & Kenneth J. Singleton, 2014. "Risk Premiums in Dynamic Term Structure Models with Unspanned Macro Risks," Journal of Finance, American Finance Association, vol. 69(3), pages 1197-1233, June.
    2. Michael E. Tipping & Christopher M. Bishop, 1999. "Probabilistic Principal Component Analysis," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(3), pages 611-622.
    3. Harvey,Andrew C., 1991. "Forecasting, Structural Time Series Models and the Kalman Filter," Cambridge Books, Cambridge University Press, number 9780521405737.
    4. Christa Cuchiero & Claudio Fontana & Alessandro Gnoatto, 2016. "A general HJM framework for multiple yield curve modelling," Finance and Stochastics, Springer, vol. 20(2), pages 267-320, April.
    5. Christa Cuchiero & Claudio Fontana & Alessandro Gnoatto, 2019. "Affine multiple yield curve models," Mathematical Finance, Wiley Blackwell, vol. 29(2), pages 568-611, April.
    6. Laura Coroneo & Domenico Giannone & Michele Modugno, 2016. "Unspanned Macroeconomic Factors in the Yield Curve," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(3), pages 472-485, July.
    7. Bianchi, Francesco & Mumtaz, Haroon & Surico, Paolo, 2009. "Dynamics of the term structure of UK interest rates," Bank of England working papers 363, Bank of England.
    8. Ang, Andrew & Piazzesi, Monika, 2003. "A no-arbitrage vector autoregression of term structure dynamics with macroeconomic and latent variables," Journal of Monetary Economics, Elsevier, vol. 50(4), pages 745-787, May.
    9. Mirko Abbritti & Salvatore Dell’Erba & Antonio Moreno & Sergio Sola, 2018. "Global Factors in the Term Structure of Interest Rates," International Journal of Central Banking, International Journal of Central Banking, vol. 14(2), pages 301-340, March.
    10. Diebold, Francis X. & Li, Canlin, 2006. "Forecasting the term structure of government bond yields," Journal of Econometrics, Elsevier, vol. 130(2), pages 337-364, February.
    11. Teichmann, Josef & Wüthrich, Mario V., 2016. "Consistent Yield Curve Prediction," ASTIN Bulletin, Cambridge University Press, vol. 46(2), pages 191-224, May.
    12. Justin Sirignano, 2016. "Deep Learning for Limit Order Books," Papers 1601.01987, arXiv.org, revised Jul 2016.
    13. Nelson, Charles R & Siegel, Andrew F, 1987. "Parsimonious Modeling of Yield Curves," The Journal of Business, University of Chicago Press, vol. 60(4), pages 473-489, October.
    14. P. Robert & Y. Escoufier, 1976. "A Unifying Tool for Linear Multivariate Statistical Methods: The RV‐Coefficient," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 25(3), pages 257-265, November.
    15. Jonathan H. Wright, 2011. "Term Premia and Inflation Uncertainty: Empirical Evidence from an International Panel Dataset," American Economic Review, American Economic Association, vol. 101(4), pages 1514-1534, June.
    16. Cairns, A.J.G. & Pritchard, D.J., 2001. "Stability of Models for the Term Structure of Interest Rates with Application to German Market Data," British Actuarial Journal, Cambridge University Press, vol. 7(3), pages 467-507, August.
    17. Karimalis, Emmanouil & Kosmidis, Ioannis & Peters, Gareth, 2017. "Multi yield curve stress-testing framework incorporating temporal and cross tenor structural dependencies," Bank of England working papers 655, Bank of England.
    18. Chib, Siddhartha & Ergashev, Bakhodir, 2009. "Analysis of Multifactor Affine Yield Curve Models," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1324-1337.
    19. Alec N. Kercheval & Yuan Zhang, 2015. "Modelling high-frequency limit order book dynamics with support vector machines," Quantitative Finance, Taylor & Francis Journals, vol. 15(8), pages 1315-1329, August.
    20. Filipović, Damir & Trolle, Anders B., 2013. "The term structure of interbank risk," Journal of Financial Economics, Elsevier, vol. 109(3), pages 707-733.
    21. Chen, Tao & Martin, Elaine & Montague, Gary, 2009. "Robust probabilistic PCA with missing data and contribution analysis for outlier detection," Computational Statistics & Data Analysis, Elsevier, vol. 53(10), pages 3706-3716, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. P. Byrne, Joseph & Cao, Shuo & Korobilis, Dimitris, 2015. "Term Structure Dynamics, Macro-Finance Factors and Model Uncertainty," SIRE Discussion Papers 2015-71, Scottish Institute for Research in Economics (SIRE).
    2. Michael D. Bauer & Glenn D. Rudebusch, 2020. "Interest Rates under Falling Stars," American Economic Review, American Economic Association, vol. 110(5), pages 1316-1354, May.
    3. P. Byrne, Joseph & Cao, Shuo & Korobilis, Dimitris, 2015. "Term Structure Dynamics, Macro-Finance Factors and Model Uncertainty," 2007 Annual Meeting, July 29-August 1, 2007, Portland, Oregon TN 2015-71, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    4. Byrne, Joseph P. & Cao, Shuo & Korobilis, Dimitris, 2017. "Forecasting the term structure of government bond yields in unstable environments," Journal of Empirical Finance, Elsevier, vol. 44(C), pages 209-225.
    5. Hong, Zhiwu & Niu, Linlin & Zhang, Chen, 2022. "Affine arbitrage-free yield net models with application to the euro debt crisis," Journal of Econometrics, Elsevier, vol. 230(1), pages 201-220.
    6. Byrne, Joseph P. & Cao, Shuo & Korobilis, Dimitris, 2019. "Decomposing global yield curve co-movement," Journal of Banking & Finance, Elsevier, vol. 106(C), pages 500-513.
    7. Rui Liu, 2019. "Forecasting Bond Risk Premia with Unspanned Macroeconomic Information," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 9(01), pages 1-62, March.
    8. Jiazi Chen & Zhiwu Hong & Linlin Niu, 2022. "Forecasting Interest Rates with Shifting Endpoints: The Role of the Demographic Age Structure," Working Papers 2022-06-25, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    9. Luis Ceballos & Alberto Naudon & Damián Romero, 2016. "Nominal term structure and term premia: evidence from Chile," Applied Economics, Taylor & Francis Journals, vol. 48(29), pages 2721-2735, June.
    10. Kučera, Adam, 2020. "Identification of triggers of U.S. yield curve movements," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    11. Fernandes, Marcelo & Vieira, Fausto, 2019. "A dynamic Nelson–Siegel model with forward-looking macroeconomic factors for the yield curve in the US," Journal of Economic Dynamics and Control, Elsevier, vol. 106(C), pages 1-1.
    12. Stona, Filipe & Caldeira, João F., 2019. "Do U.S. factors impact the Brazilian yield curve? Evidence from a dynamic factor model," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 76-89.
    13. Zhu, Xiaoneng & Rahman, Shahidur, 2015. "A regime-switching Nelson–Siegel term structure model of the macroeconomy," Journal of Macroeconomics, Elsevier, vol. 44(C), pages 1-17.
    14. Stolyarov, Dmitriy & Tesar, Linda L., 2021. "Interest rate trends in a global context," Economic Modelling, Elsevier, vol. 101(C).
    15. Dmitriy Stolyarov & Linda L. Tesar, 2019. "Interest Rate Trends in a Global Context," Working Papers wp402, University of Michigan, Michigan Retirement Research Center.
    16. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2021. "No‐arbitrage priors, drifting volatilities, and the term structure of interest rates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(5), pages 495-516, August.
    17. Gerhart, Christoph & Lütkebohmert, Eva, 2020. "Empirical analysis and forecasting of multiple yield curves," Insurance: Mathematics and Economics, Elsevier, vol. 95(C), pages 59-78.
    18. Karahan, Cenk C. & Soykök, Emre, 2022. "Term premium dynamics in an emerging market: Risk, liquidity, and behavioral factors," International Review of Financial Analysis, Elsevier, vol. 84(C).
    19. Candelon, Bertrand & Moura, Rubens, 2023. "Sovereign yield curves and the COVID-19 in emerging markets," Economic Modelling, Elsevier, vol. 127(C).
    20. Christos Ioannidis & Kook Ka, 2021. "Economic Policy Uncertainty and Bond Risk Premia," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(6), pages 1479-1522, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jecnmx:v:6:y:2018:i:3:p:34-:d:158660. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.