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Stylized Facts of Return Series, Robust Estimates, and Three Popular Models of Volatility

Author

Listed:
  • Teräsvirta, Timo

    (Dept. of Economic Statistics, Stockholm School of Economics)

  • Zhao, Zhenfang

    (Dept. of Economic Statistics, Stockholm School of Economics)

Abstract
Financial return series of sufficiently high frequency display stylized facts such as volatility clustering, high kurtosis, low starting and slow-decaying autocorrelation function of squared returns and the so-called Taylor effect. In order to evaluate the capacity of volatility models to reproduce these facts, we apply both standard and robust measures of kurtosis and autocorrelation of squares to first-order GARCH, EGARCH and ARSV models. Robust measures provide a fresh view of stylized facts which is useful because many financial time series can be viewed as being contaminated with outliers.

Suggested Citation

  • Teräsvirta, Timo & Zhao, Zhenfang, 2007. "Stylized Facts of Return Series, Robust Estimates, and Three Popular Models of Volatility," SSE/EFI Working Paper Series in Economics and Finance 662, Stockholm School of Economics, revised 01 Aug 2007.
    • Handle: RePEc:hhs:hastef:0662
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    as
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    Cited by:

    1. Leovardo Mata Mata & José Antonio Núñez Mora & Ramona Serrano Bautista, 2021. "Multivariate Distribution in the Stock Markets of Brazil, Russia, India, and China," SAGE Open, , vol. 11(2), pages 21582440211, April.
    2. Hanck, Christoph & Demetrescu, Matei & Kruse, Robinson, 2015. "Fixed-b Asymptotics for t-Statistics in the Presence of Time-Varying Volatility," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112916, Verein für Socialpolitik / German Economic Association.
    3. Amado, Cristina & Teräsvirta, Timo, 2013. "Modelling volatility by variance decomposition," Journal of Econometrics, Elsevier, vol. 175(2), pages 142-153.
    4. Demetrescu, Matei & Kruse, Robinson, 2015. "Testing heteroskedastic time series for normality," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113221, Verein für Socialpolitik / German Economic Association.
    5. Pascual, Lorenzo & Fresoli, Diego Eduardo, 2011. "Bootstrap forecast of multivariate VAR models without using the backward representation," DES - Working Papers. Statistics and Econometrics. WS ws113426, Universidad Carlos III de Madrid. Departamento de Estadística.
    6. Chorro, Christophe & Guégan, Dominique & Ielpo, Florian & Lalaharison, Hanjarivo, 2018. "Testing for leverage effects in the returns of US equities," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 290-306.
    7. M. Angeles Carnero & Ana Pérez & Esther Ruiz, 2016. "Identification of asymmetric conditional heteroscedasticity in the presence of outliers," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(1), pages 179-201, March.
    8. Cristina Amado & Timo Teräsvirta, 2008. "Modelling Conditional and Unconditional Heteroskedasticity with Smoothly Time-Varying Structure," NIPE Working Papers 03/2008, NIPE - Universidade do Minho.
    9. Matei Demetrescu & Christoph Hanck & Robinson Kruse, 2016. "Fixed-b Inference in the Presence of Time-Varying Volatility," CREATES Research Papers 2016-01, Department of Economics and Business Economics, Aarhus University.
    10. Dalla, Violetta, 2015. "Power transformations of absolute returns and long memory estimation," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 1-18.
    11. Haas, Markus, 2009. "Persistence in volatility, conditional kurtosis, and the Taylor property in absolute value GARCH processes," Statistics & Probability Letters, Elsevier, vol. 79(15), pages 1674-1683, August.
    12. Carnero M. Angeles & Pérez Ana, 2021. "Outliers and misleading leverage effect in asymmetric GARCH-type models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 25(1), pages 1-19, February.
    13. Christophe Chorro & Dominique Guegan & Florian Ielpo & Hanjarivo Lalaharison, 2017. "Testing for Leverage Effects in the Returns of US Equities," Post-Print halshs-00973922, HAL.
    14. Li, Qi & Lian, Heng & Zhu, Fukang, 2016. "Robust closed-form estimators for the integer-valued GARCH (1,1) model," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 209-225.
    15. Eunhee Lee & Doo Bong Han & Rodolfo M. Nayga, 2017. "A common factor of stochastic volatilities between oil and commodity prices," Applied Economics, Taylor & Francis Journals, vol. 49(22), pages 2203-2215, May.
    16. Dave Cliff, 2021. "BBE: Simulating the Microstructural Dynamics of an In-Play Betting Exchange via Agent-Based Modelling," Papers 2105.08310, arXiv.org.
    17. García-Centeno, María del Carmen & Fernández-Avilés, Gema & Montero, José María, 2010. "Asymmetries in the Volatility of Precious Metals Returns: The TA-ARSV Modelling Strategy," The Journal of Economic Asymmetries, Elsevier, vol. 7(1), pages 23-41.
    18. Demetrescu, Matei & Rodrigues, Paulo M.M., 2022. "Residual-augmented IVX predictive regression," Journal of Econometrics, Elsevier, vol. 227(2), pages 429-460.
    19. Antonio Díaz & Gonzalo García-Donato & Andrés Mora-Valencia, 2017. "Risk quantification in turmoil markets," Risk Management, Palgrave Macmillan, vol. 19(3), pages 202-224, August.
    20. Laurie Davies & Walter Kramer, 2016. "Stylized Facts and Simulating Long Range Financial Data," Papers 1612.05229, arXiv.org.
    21. Carnero, M. Angeles & Pérez, Ana, 2019. "Leverage effect in energy futures revisited," Energy Economics, Elsevier, vol. 82(C), pages 237-252.
    22. Namid R. Stillman & Rory Baggott & Justin Lyon & Jianfei Zhang & Dingqiu Zhu & Tao Chen & Perukrishnen Vytelingum, 2023. "Deep Calibration of Market Simulations using Neural Density Estimators and Embedding Networks," Papers 2311.11913, arXiv.org, revised Nov 2023.
    23. Hachmi Ben Ameur & Zied Ftiti & Fredj Jawadi & Wael Louhichi, 2022. "Measuring extreme risk dependence between the oil and gas markets," Annals of Operations Research, Springer, vol. 313(2), pages 755-772, June.
    24. Christophe Chorro & Dominique Guegan & Florian Ielpo & Hanjarivo Lalaharison, 2014. "Testing for Leverage Effect in Financial Returns," Documents de travail du Centre d'Economie de la Sorbonne 14022, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    25. José‐María Montero & Gema Fernández‐Avilés & María‐Carmen García, 2010. "Estimation of Asymmetric Stochastic Volatility Models: Application to Daily Average Prices of Energy Products," International Statistical Review, International Statistical Institute, vol. 78(3), pages 330-347, December.

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    More about this item

    Keywords

    GARCH; EGARCH; ARSV; extreme observations; autocorrelation function; kurtosis; robust measure; confidence region.;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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