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Stochastic Threshold Models on Interest Rate

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Abstract
Threshold models have been found useful in modeling nonlinearities in many financial time series. In this framework, the financial variable of interest evolves according to different dynamics, which is solely determined by the threshold regimes that the observed indicator variable falls into. This paper generalizes the threshold models to a class of stochastic threshold models, which allow for stochastic dependence of the current economic state on the threshold regimes. In a stochastic threshold model, different economic states are possible to occur within a certain threshold regime and each state occurs with some probability depend on the threshold regime and other recently observed information. Model identification and maximum likelihood estimation are developed. An study of short-term interest rate is conducted. We find that the short-term interest rate behaves asymmetrically in a rising versus a declining market. Declining market has significantly negative duration (in "return clock") dependence and rising market has insignificantly positive duration dependence. In the comparison of generalized autoregressive conditional heteroskedastisity models, threshold autoregressive models, generalized regime-switching models and stochastic threshold models, we find that our stochastic threshold model fits the data best in terms of alternative model selection criteria and in-sample forecasting. It also provides the best out-of-sample forecasting.

Suggested Citation

  • Huirong Li & Jian Yang, 1999. "Stochastic Threshold Models on Interest Rate," University of Western Ontario, Departmental Research Report Series 9913, University of Western Ontario, Department of Economics.
    • Handle: RePEc:uwo:uwowop:9913
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    File URL: https://ir.lib.uwo.ca/cgi/viewcontent.cgi?article=1340&context=economicsresrpt
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    Citations

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    Cited by:

    1. Peter Fredriksson & Bertil Holmlund, 2006. "Optimal unemployment insurance design: Time limits, monitoring, or workfare?," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 13(5), pages 565-585, September.
    2. Abbring, Jaap H., 2003. "Dynamic Econometric Program Evaluation," IZA Discussion Papers 804, Institute of Labor Economics (IZA).
    3. Rafael Lalive & Jan C. van Ours & Josef Zweimüller, 2005. "The Effect Of Benefit Sanctions On The Duration Of Unemployment," Journal of the European Economic Association, MIT Press, vol. 3(6), pages 1386-1417, December.
    4. Robert Breunig & Deborah A. Cobb‐Clark & Yvonne Dunlop & Marion Terrill, 2003. "Assisting the Long‐Term Unemployed: Results from a Randomised Trial," The Economic Record, The Economic Society of Australia, vol. 79(244), pages 84-102, March.
    5. Jan Boone & Peter Fredriksson & Bertil Holmlund & Jan C. van Ours, 2007. "Optimal Unemployment Insurance with Monitoring and Sanctions," Economic Journal, Royal Economic Society, vol. 117(518), pages 399-421, March.
    6. Bukowski, Maciej & Lewandowski, Piotr & Koloch, Grzegorz & Baranowska, Anna & Magda, Iga & Szydlowski, Arkadiusz & Bober, Magda & Bieliński, Jacek & Zawistowski, Julian & Sarzalska, Malgorzata, 2008. "Employment in Poland 2007: Security on flexible labour market," MPRA Paper 14284, University Library of Munich, Germany.
    7. Rafael Lalive & Jan C. van Ours & Josef Zweimueller, "undated". "The Impact of Active Labor Market Programs on the Duration of Unemployment," IEW - Working Papers 041, Institute for Empirical Research in Economics - University of Zurich.
    8. Peter Fredriksson & Bertil Holmlund, 2006. "Improving Incentives in Unemployment Insurance: A Review of Recent Research," Journal of Economic Surveys, Wiley Blackwell, vol. 20(3), pages 357-386, July.
    9. Dan A. Black & Jeffrey A. Smith & Mark C. Berger & Brett J. Noel, 2002. "Is the Threat of Reemployment Services More Effective than the Services Themselves? Experimental Evidence from the UI System," NBER Working Papers 8825, National Bureau of Economic Research, Inc.
    10. Jan C. van Ours, 2007. "Compulsion in active labour market programmes," National Institute Economic Review, National Institute of Economic and Social Research, vol. 202(1), pages 67-78, October.
    11. Dehejia, Rajeev H., 2005. "Program evaluation as a decision problem," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 141-173.
    12. Glismann, Hans H. & Schrader, Klaus, 2001. "Alternative Systeme der Arbeitslosenversicherung: das Beispiel der Vereinigten Staaten und des Vereinigten Königreichs," Kiel Working Papers 1032, Kiel Institute for the World Economy (IfW Kiel).
    13. Martin, John P. & Grubb, David, 2001. "What works and for whom: a review of OECD countries' experiences with active labour market policies," Working Paper Series 2001:14, IFAU - Institute for Evaluation of Labour Market and Education Policy.

    More about this item

    Keywords

    Stochastic Threshold; Short-term interest rate; Conditional volatility; Maximum likelihood;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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