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Testing for Parameter Instability across Different Modeling Frameworks

Author

Listed:
  • Francesco Calvori
  • Drew Creal
  • Siem Jan Koopman
  • André Lucas

Abstract

We develop a new parameter instability test that generalizes the seminal ARCH-Lagrange Multiplier test of Engle (1982) for a constant variance against the alternative of autoregressive conditional heteroskedasticity to settings with nonlinear time-varying parameters and non-Gaussian distributions. We investigate the performance of the new test relative to both classic and recently proposed parameter instability tests, including tests against structural breaks and parameter-driven dynamics. We find that the recent test of Müller and Petalas (2010) performs best across a wide range of alternatives, particularly if parameter instability is slow. For time-varying parameters that exhibit more mean reversion, our new test has higher power. We provide an application to a heavily unbalanced panel of losses given default for US corporations from 1982 to 2010 and provide evidence of significant parameter instability in the parameters of a static beta distributed model.

Suggested Citation

  • Francesco Calvori & Drew Creal & Siem Jan Koopman & André Lucas, 2017. "Testing for Parameter Instability across Different Modeling Frameworks," Journal of Financial Econometrics, Oxford University Press, vol. 15(2), pages 223-246.
    • Handle: RePEc:oup:jfinec:v:15:y:2017:i:2:p:223-246.
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbw008
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    References listed on IDEAS

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    1. Harvey,Andrew C., 2013. "Dynamic Models for Volatility and Heavy Tails," Cambridge Books, Cambridge University Press, number 9781107630024, November.
    2. Andrei Shleifer & Robert Vishny, 2011. "Fire Sales in Finance and Macroeconomics," Journal of Economic Perspectives, American Economic Association, vol. 25(1), pages 29-48, Winter.
    3. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    4. Shephard, Neil (ed.), 2005. "Stochastic Volatility: Selected Readings," OUP Catalogue, Oxford University Press, number 9780199257201.
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    Citations

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    Cited by:

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    2. Davide Delle Monache & Andrea De Polis & Ivan Petrella, 2024. "Modeling and Forecasting Macroeconomic Downside Risk," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(3), pages 1010-1025, July.
    3. Carlo Campajola & Domenico Di Gangi & Fabrizio Lillo & Daniele Tantari, 2020. "Modelling time-varying interactions in complex systems: the Score Driven Kinetic Ising Model," Papers 2007.15545, arXiv.org, revised Aug 2021.
    4. Harvey, Andrew & Thiele, Stephen, 2016. "Testing against changing correlation," Journal of Empirical Finance, Elsevier, vol. 38(PB), pages 575-589.
    5. repec:wrk:wrkemf:34 is not listed on IDEAS
    6. Palumbo, D., 2021. "Testing and Modelling Time Series with Time Varying Tails," Cambridge Working Papers in Economics 2111, Faculty of Economics, University of Cambridge.
    7. Fuentes, Fernanda & Herrera, Rodrigo & Clements, Adam, 2023. "Forecasting extreme financial risk: A score-driven approach," International Journal of Forecasting, Elsevier, vol. 39(2), pages 720-735.
    8. Francisco Blasques & Noah Stegehuis, 2024. "A Score-Driven Filter for Causal Regression Models with Time- Varying Parameters and Endogenous Regressors," Tinbergen Institute Discussion Papers 24-016/III, Tinbergen Institute.
    9. F. Campigli & G. Bormetti & F. Lillo, 2022. "Measuring price impact and information content of trades in a time-varying setting," Papers 2212.12687, arXiv.org, revised Dec 2023.

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    More about this item

    Keywords

    credit risk; generalized autoregressive score model; observation-driven and parameter-driven models; regime switching; structural breaks; time-varying parameters;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • 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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