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Regime-Switching Stochastic Volatility and Short-Term Interest Rates

Author

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  • Raúl Susmel
  • Madhu Kalimipalli

Abstract

In this paper, we introduce regime-switching in a two-factor stochastic volatility model to explain the behavior of short-term interest rates. The regime-switching stochastic volatility (RSV) process for interest rates is able to capture all possible exogenous shocks that could be either discrete, as occurring from possible changes in the underlying regime, or continuous in the form of `market-news' events. We estimate the model using a Gibbs Sampling based Markov Chain Monte Carlo algorithm that is robust to complex nonlinearities in the likelihood function. We compare the performance of our RSV model with the performance of other GARCH and stochastic volatility two-factor models. We evaluate all models with several in-sample and out-of-sample measures. Overall, our results show a superior performance of the RSV two-factor model.

Suggested Citation

  • Raúl Susmel & Madhu Kalimipalli, 2001. "Regime-Switching Stochastic Volatility and Short-Term Interest Rates," CEMA Working Papers: Serie Documentos de Trabajo. 197, Universidad del CEMA.
    • Handle: RePEc:cem:doctra:197
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    References listed on IDEAS

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    1. Robert R. Bliss, 1997. "Movements in the term structure of interest rates," Economic Review, Federal Reserve Bank of Atlanta, vol. 82(Q 4), pages 16-33.
    2. Albert, James H & Chib, Siddhartha, 1993. "Bayes Inference via Gibbs Sampling of Autoregressive Time Series Subject to Markov Mean and Variance Shifts," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(1), pages 1-15, January.
    3. Cai, Jun, 1994. "A Markov Model of Switching-Regime ARCH," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 309-316, July.
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    Citations

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    Cited by:

    1. Soosung Hwang & Steve E. Satchell & Pedro L. Valls Pereira, 2004. "How Persistent is Volatility? An Answer with Stochastic Volatility Models with Markov Regime Switching State Equations," Econometric Society 2004 Latin American Meetings 198, Econometric Society.
    2. Pedro L. Valls Pereira, 2004. "How Persistent is Volatility? An Answer with Stochastic Volatility Models with Markov Regime Switching State Equations," Finance Lab Working Papers flwp_59, Finance Lab, Insper Instituto de Ensino e Pesquisa.
    3. Łukasz Kwiatkowski, 2010. "Markov Switching In-Mean Effect. Bayesian Analysis in Stochastic Volatility Framework," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 2(1), pages 59-94, January.
    4. B. Craven & Sardar Islam, 2008. "A model for stock market returns: non-Gaussian fluctuations and financial factors," Review of Quantitative Finance and Accounting, Springer, vol. 30(4), pages 355-370, May.
    5. Łukasz Kwiatkowski, 2011. "Bayesian Analysis of a Regime Switching In-Mean Effect for the Polish Stock Market," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 3(4), pages 187-219, December.
    6. Gorynin, Ivan & Derrode, Stéphane & Monfrini, Emmanuel & Pieczynski, Wojciech, 2017. "Fast smoothing in switching approximations of non-linear and non-Gaussian models," Computational Statistics & Data Analysis, Elsevier, vol. 114(C), pages 38-46.
    7. Alizadeh, Amir H. & Gabrielsen, Alexandros, 2013. "Dynamics of credit spread moments of European corporate bond indexes," Journal of Banking & Finance, Elsevier, vol. 37(8), pages 3125-3144.

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    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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