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Forecasting sovereign CDS volatility: A comparison of univariate GARCH-class models

Author

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  • Saker Sabkha

    (LEGO - Laboratoire d'Economie et de Gestion de l'Ouest - UBS - Université de Bretagne Sud - UBO - Université de Brest - IMT - Institut Mines-Télécom [Paris] - IBSHS - Institut Brestois des Sciences de l'Homme et de la Société - UBO - Université de Brest - UBL - Université Bretagne Loire - IMT Atlantique - IMT Atlantique - IMT - Institut Mines-Télécom [Paris])

Abstract

Initially overlooked by investors, the sovereign credit risk has been reassessed upwards since the 2000's which has contributed to awaken the interest of speculators in sovereign CDS. The growing need of accurate forecasting models has led us to fill the gap in the literature by studying the predictability of sovereign CDS volatility, using both linear and non-linear GARCH-class models. This paper uses data from 38 worldwide countries, ranging from January 2006 to March 2017. Results show that the CDS markets are subject to periods of volatility clustering, nonlinearity, asym-metric leverage effects and long-memory behavior. Using 7 heteroskedastic and no heteroskedastic-robust statistic criteria, results show that the fractionally-integrated models outperform the basic GARCH-class models in terms of forecasting ability and that allowing flexibility regarding the persistence degree of variance shocks significantly improves the model's suitability to data. Despite the divergence in the economic status and geographical positions of the countries composing our sample, the FIGARCH and FIEGARCH models are mainly found to be the most accurate models in predicting credit market volatility. JEL Classification: G15, G17, C58.

Suggested Citation

  • Saker Sabkha, 2021. "Forecasting sovereign CDS volatility: A comparison of univariate GARCH-class models," Post-Print hal-01769390, HAL.
    • Handle: RePEc:hal:journl:hal-01769390
    DOI: 10.3917/vse.209.0027
    Note: View the original document on HAL open archive server: https://hal.science/hal-01769390
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    References listed on IDEAS

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    1. Agnolucci, Paolo, 2009. "Volatility in crude oil futures: A comparison of the predictive ability of GARCH and implied volatility models," Energy Economics, Elsevier, vol. 31(2), pages 316-321, March.
    2. Fontana, Alessandro & Scheicher, Martin, 2016. "An analysis of euro area sovereign CDS and their relation with government bonds," Journal of Banking & Finance, Elsevier, vol. 62(C), pages 126-140.
    3. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    4. Andersen, Torben G. & Bollerslev, Tim & Lange, Steve, 1999. "Forecasting financial market volatility: Sample frequency vis-a-vis forecast horizon," Journal of Empirical Finance, Elsevier, vol. 6(5), pages 457-477, December.
    5. Krishnan, C.N.V. & Ritchken, Peter H. & Thomson, James B., 2010. "Predicting credit spreads," Journal of Financial Intermediation, Elsevier, vol. 19(4), pages 529-563, October.
    6. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    7. Bollerslev, Tim & Ole Mikkelsen, Hans, 1996. "Modeling and pricing long memory in stock market volatility," Journal of Econometrics, Elsevier, vol. 73(1), pages 151-184, July.
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    More about this item

    Keywords

    CDS volatility; Predictability; Forecasting models; Loss functions criteria 1;
    All these keywords.

    JEL classification:

    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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