IDEAS home Printed from https://ideas.repec.org/p/tin/wpaper/20000049.html
   My bibliography  Save this paper

Asymmetries in Conditional Mean and Variance: Modelling Stock Returns by asMA-asQGARCH

Author

Listed:
  • Kurt Brännäs

    (Umeå University, Sweden)

  • Jan G. de Gooijer

    (University of Amsterdam)

Abstract

The asymmetric moving average model (asMA) is extended to allow forasymmetric quadratic conditional heteroskedasticity (asQGARCH). Theasymmetric parametrization of the conditional variance encompassesthe quadratic GARCH model of Sentana (1995). We introduce a framework fortesting asymmetries in theconditional mean and the conditional variance, separately or jointly.Some of the new model's moment properties are also derived. Empiricalresults are given for the daily returns of the compositeindex of the New York Stock Exchange. There is strong evidence ofasymmetry in both the conditional mean and conditional variancefunctions. In a genuine out-of-sample forecasting experiment theperformance of the best fitted asMA-asQGARCH model is compared topure asMA and no-change forecasts. This is done both in terms ofconditional mean forecasting as well as in terms of risk forecasting.

Suggested Citation

  • Kurt Brännäs & Jan G. de Gooijer, 2000. "Asymmetries in Conditional Mean and Variance: Modelling Stock Returns by asMA-asQGARCH," Tinbergen Institute Discussion Papers 00-049/4, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20000049
    as

    Download full text from publisher

    File URL: https://papers.tinbergen.nl/00049.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. LeBaron, Blake, 1992. "Some Relations between Volatility and Serial Correlations in Stock Market Returns," The Journal of Business, University of Chicago Press, vol. 65(2), pages 199-219, April.
    2. Harvey, Campbell R. & Siddique, Akhtar, 1999. "Autoregressive Conditional Skewness," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 34(4), pages 465-487, December.
    3. repec:bla:jfinan:v:44:y:1989:i:5:p:1115-53 is not listed on IDEAS
    4. Li, C W & Li, W K, 1996. "On a Double-Threshold Autoregressive Heteroscedastic Time Series Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(3), pages 253-274, May-June.
    5. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    6. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    7. Pagan, Adrian, 1996. "The econometrics of financial markets," Journal of Empirical Finance, Elsevier, vol. 3(1), pages 15-102, May.
    8. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    9. Lundbergh, Stefan & Teräsvirta, Timo, 1998. "Modelling economic high-frequency time series with STAR-STGARCH models," SSE/EFI Working Paper Series in Economics and Finance 291, Stockholm School of Economics.
    10. Bekaert, Geert & Wu, Guojun, 2000. "Asymmetric Volatility and Risk in Equity Markets," The Review of Financial Studies, Society for Financial Studies, vol. 13(1), pages 1-42.
    11. Enrique Sentana, 1995. "Quadratic ARCH Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 62(4), pages 639-661.
    12. Kurt Brännäs & Henry Ohlsson, 1999. "Asymmetric Time Series and Temporal Aggregation," The Review of Economics and Statistics, MIT Press, vol. 81(2), pages 341-344, May.
    13. Gregory Koutmos, 1999. "Asymmetric index stock returns: evidence from the G-7," Applied Economics Letters, Taylor & Francis Journals, vol. 6(12), pages 817-820.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Taştan, Hüseyin, 2011. "Simulation based estimation of threshold moving average models with contemporaneous shock asymmetry," MPRA Paper 34302, University Library of Munich, Germany.
    2. Hua, Zhongsheng & Zhang, Bin, 2008. "Improving density forecast by modeling asymmetric features: An application to S&P500 returns," European Journal of Operational Research, Elsevier, vol. 185(2), pages 716-725, March.
    3. Brännäs Kurt & De Gooijer Jan G. & Lönnbark Carl & Soultanaeva Albina, 2012. "Simultaneity and Asymmetry of Returns and Volatilities: The Emerging Baltic States' Stock Exchanges," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(1), pages 1-24, January.
    4. Kurt Brannas & Albina Soultanaeva, 2011. "Influence of news from Moscow and New York on returns and risks of Baltic States’ stock markets," Baltic Journal of Economics, Baltic International Centre for Economic Policy Studies, vol. 11(1), pages 109-124, July.
    5. Brännäs, Kurt & Nordman, Niklas, 2001. "An Alternative Conditional Asymmetry Specification for Stock Returns," Umeå Economic Studies 556, Umeå University, Department of Economics.
    6. Kurt Brannas & Niklas Nordman, 2003. "Conditional skewness modelling for stock returns," Applied Economics Letters, Taylor & Francis Journals, vol. 10(11), pages 725-728.
    7. Brännäs, Kurt & Soultanaeva, Albina, 2006. "Influence of News in Moscow and New York on Returns and Risks on Baltic State Stock Indices," Umeå Economic Studies 696, Umeå University, Department of Economics.
    8. María José Rodríguez & Esther Ruiz, 2012. "Revisiting Several Popular GARCH Models with Leverage Effect: Differences and Similarities," Journal of Financial Econometrics, Oxford University Press, vol. 10(4), pages 637-668, September.
    9. Rodríguez, Mª José, 2009. "GARCH models with leverage effect : differences and similarities," DES - Working Papers. Statistics and Econometrics. WS ws090302, Universidad Carlos III de Madrid. Departamento de Estadística.
    10. Srikanta Kundu & Nityananda Sarkar, 2016. "Is the Effect of Risk on Stock Returns Different in Up and Down Markets? A Multi-Country Study," International Econometric Review (IER), Econometric Research Association, vol. 8(2), pages 53-71, September.
    11. Brännäs, Kurt, 2003. "Temporal Aggregation of the Returns of a Stock Index Series," Umeå Economic Studies 614, Umeå University, Department of Economics.
    12. Jan G. Gooijer, 2021. "Asymmetric vector moving average models: estimation and testing," Computational Statistics, Springer, vol. 36(2), pages 1437-1460, June.
    13. Kulp-Tåg, Sofie, 2007. "Short-Horizon Asymmetric Mean-Reversion and Overreactions: Evidence from the Nordic Stock Markets," Working Papers 524, Hanken School of Economics.
    14. Malmsten, Hans & Teräsvirta, Timo, 2004. "Stylized Facts of Financial Time Series and Three Popular Models of Volatility," SSE/EFI Working Paper Series in Economics and Finance 563, Stockholm School of Economics, revised 03 Sep 2004.
    15. Alessandra Amendola & Giuseppe Storti, 2002. "A non-linear time series approach to modelling asymmetry in stock market indexes," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 11(2), pages 201-216, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tim Bollerslev, 2008. "Glossary to ARCH (GARCH)," CREATES Research Papers 2008-49, Department of Economics and Business Economics, Aarhus University.
    2. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654, September.
    3. Markus Haas, 2004. "Mixed Normal Conditional Heteroskedasticity," Journal of Financial Econometrics, Oxford University Press, vol. 2(2), pages 211-250.
    4. Carol Alexander & Emese Lazar, 2009. "Modelling Regime‐Specific Stock Price Volatility," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(6), pages 761-797, December.
    5. LeBaron, Blake, 2003. "Non-Linear Time Series Models in Empirical Finance,: Philip Hans Franses and Dick van Dijk, Cambridge University Press, Cambridge, 2000, 296 pp., Paperback, ISBN 0-521-77965-0, $33, [UK pound]22.95, [," International Journal of Forecasting, Elsevier, vol. 19(4), pages 751-752.
    6. Thomas C. Chiang & Cathy W.S. Chen & Mike K.P. So, 2007. "Asymmetric Return and Volatility Responses to Composite News from Stock Markets," Multinational Finance Journal, Multinational Finance Journal, vol. 11(3-4), pages 179-210, September.
    7. Degiannakis, Stavros & Xekalaki, Evdokia, 2004. "Autoregressive Conditional Heteroskedasticity (ARCH) Models: A Review," MPRA Paper 80487, University Library of Munich, Germany.
    8. Bali, Turan G. & Mo, Hengyong & Tang, Yi, 2008. "The role of autoregressive conditional skewness and kurtosis in the estimation of conditional VaR," Journal of Banking & Finance, Elsevier, vol. 32(2), pages 269-282, February.
    9. Ender Su & John Bilson, 2011. "Trading asymmetric trend and volatility by leverage trend GARCH in Taiwan stock index," Applied Economics, Taylor & Francis Journals, vol. 43(26), pages 3891-3905.
    10. Luc Bauwens & Sébastien Laurent & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109, January.
    11. Francesco Audrino & Fabio Trojani, 2006. "Estimating and predicting multivariate volatility thresholds in global stock markets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(3), pages 345-369, April.
    12. Wu, Guojun & Xiao, Zhijie, 2002. "A generalized partially linear model of asymmetric volatility," Journal of Empirical Finance, Elsevier, vol. 9(3), pages 287-319, August.
    13. Alessandra Amendola & Giuseppe Storti, 2002. "A non-linear time series approach to modelling asymmetry in stock market indexes," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 11(2), pages 201-216, June.
    14. Lundblad, Christian, 2007. "The risk return tradeoff in the long run: 1836-2003," Journal of Financial Economics, Elsevier, vol. 85(1), pages 123-150, July.
    15. Giraitis, Liudas & Leipus, Remigijus & Robinson, Peter M. & Surgailis, Donatas, 2004. "LARCH, leverage, and long memory," LSE Research Online Documents on Economics 294, London School of Economics and Political Science, LSE Library.
    16. Liudas Giraitis, 2004. "LARCH, Leverage, and Long Memory," Journal of Financial Econometrics, Oxford University Press, vol. 2(2), pages 177-210.
    17. Jondeau, Eric & Rockinger, Michael, 2003. "Conditional volatility, skewness, and kurtosis: existence, persistence, and comovements," Journal of Economic Dynamics and Control, Elsevier, vol. 27(10), pages 1699-1737, August.
    18. Nam, Kiseok & Pyun, Chong Soo & Avard, Stephen L., 2001. "Asymmetric reverting behavior of short-horizon stock returns: An evidence of stock market overreaction," Journal of Banking & Finance, Elsevier, vol. 25(4), pages 807-824, April.
    19. Amendola, Alessandra & Christian, Francq, 2009. "Concepts and tools for nonlinear time series modelling," MPRA Paper 15140, University Library of Munich, Germany.
    20. Jondeau, E. & Rockinger, M., 2000. "Conditional Volatility, Skewness, and Kurtosis: Existence and Persistence," Working papers 77, Banque de France.

    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:tin:wpaper:20000049. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Tinbergen Office +31 (0)10-4088900 (email available below). General contact details of provider: https://edirc.repec.org/data/tinbenl.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.