IDEAS home Printed from https://ideas.repec.org/a/ere/journl/vxxviiy2008i2p29-48.html
   My bibliography  Save this article

Varianza condicional de medias móviles no-lineales

Author

Listed:
  • Daniel Ventosa-Santaulària

    (Escuela de Economía Universidad de Guanajuato.)

  • Alfonso Mendoza Velázquez

    (Departamento de Economía y Centro de Investigación e Inteligencia Económica (CIIE), Universidad Popular Autónoma del Estado de Puebla.)

  • Manuel Gómez-Zaldívar

    (Escuela de Economía Universidad de Guanajuato.)

Abstract

We present a new heteroskedastic conditional variance model using NonLinear Moving Average as the basis for this specification [NLMACH(q)]. The typical problem of this class of models-i.e., noninvertibility—is solved by means of an intuitive parametric restriction; this allows us to use Maximum Likelihood as the estimation procedure. The statistical properties of the new model are both simple and attractive for empirical purposes in finance: a natural fat-tailed distribution stands out. The Autocorrelation Function of the squared process allows us for identification of the number of lags to be included in the new specification. In addition, we present several Monte Carlo experiments where the properties of the model using finite samples are exhibited. Finally, an empirical application using exchange rates and capital market bonds is shown.

Suggested Citation

  • Daniel Ventosa-Santaulària & Alfonso Mendoza Velázquez & Manuel Gómez-Zaldívar, 2008. "Varianza condicional de medias móviles no-lineales," Ensayos Revista de Economia, Universidad Autonoma de Nuevo Leon, Facultad de Economia, vol. 0(2), pages 29-48, November.
  • Handle: RePEc:ere:journl:v:xxvii:y:2008:i:2:p:29-48
    as

    Download full text from publisher

    File URL: http://www.economia.uanl.mx/revistaensayos/xxvii/2/Varianza-condicional.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    2. Robinson, P. M., 1977. "The estimation of a nonlinear moving average model," Stochastic Processes and their Applications, Elsevier, vol. 5(1), pages 81-90, February.
    3. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521770415.
    4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    5. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September.
    6. Engle, Robert F & Lilien, David M & Robins, Russell P, 1987. "Estimating Time Varying Risk Premia in the Term Structure: The Arch-M Model," Econometrica, Econometric Society, vol. 55(2), pages 391-407, March.
    Full references (including those not matched with items on IDEAS)

    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. Ventosa-Santaulària, Daniel & Mendoza V., Alfonso, 2005. "Non Linear Moving-Average Conditional Heteroskedasticity," MPRA Paper 58769, University Library of Munich, Germany.
    2. Tim Bollerslev, 2008. "Glossary to ARCH (GARCH)," CREATES Research Papers 2008-49, Department of Economics and Business Economics, Aarhus University.
    3. L. Grossi & G. Morelli, 2006. "Robust volatility forecasts and model selection in financial time series," Economics Department Working Papers 2006-SE02, Department of Economics, Parma University (Italy).
    4. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005. "Volatility Forecasting," PIER Working Paper Archive 05-011, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    5. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.
    6. Christensen, Bent Jesper & Nielsen, Morten Ørregaard & Zhu, Jie, 2015. "The impact of financial crises on the risk–return tradeoff and the leverage effect," Economic Modelling, Elsevier, vol. 49(C), pages 407-418.
    7. Christensen, Bent Jesper & Nielsen, Morten Ørregaard & Zhu, Jie, 2010. "Long memory in stock market volatility and the volatility-in-mean effect: The FIEGARCH-M Model," Journal of Empirical Finance, Elsevier, vol. 17(3), pages 460-470, June.
    8. Mehmet Sahiner, 2022. "Forecasting volatility in Asian financial markets: evidence from recursive and rolling window methods," SN Business & Economics, Springer, vol. 2(10), pages 1-74, October.
    9. Lütkepohl,Helmut & Krätzig,Markus (ed.), 2004. "Applied Time Series Econometrics," Cambridge Books, Cambridge University Press, number 9780521547871.
    10. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2013. "Financial Risk Measurement for Financial Risk Management," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, volume 2, chapter 0, pages 1127-1220, Elsevier.
    11. Subrata Roy, 2020. "Stock Market Asymmetry and Investors’ Sensation on Prime Minister: Indian Evidence," Jindal Journal of Business Research, , vol. 9(2), pages 148-161, December.
    12. Barry A. Goss & S. Gulay Avsar, 2013. "Simultaneity, Forecasting and Profits in London Copper Futures," Australian Economic Papers, Wiley Blackwell, vol. 52(2), pages 79-96, June.
    13. Mohammadi, Hassan & Su, Lixian, 2010. "International evidence on crude oil price dynamics: Applications of ARIMA-GARCH models," Energy Economics, Elsevier, vol. 32(5), pages 1001-1008, September.
    14. Benoit Mandelbrot & Adlai Fisher & Laurent Calvet, 1997. "A Multifractal Model of Asset Returns," Cowles Foundation Discussion Papers 1164, Cowles Foundation for Research in Economics, Yale University.
    15. Cheong, Chin Wen, 2009. "Modeling and forecasting crude oil markets using ARCH-type models," Energy Policy, Elsevier, vol. 37(6), pages 2346-2355, June.
    16. Daniel Ventosa-Santaularia & Alfonso Mendoza, 2005. "Non Linear Moving-Average Conditional Heteroskedasticity," Department of Economics and Finance Working Papers EM200502, Universidad de Guanajuato, Department of Economics and Finance.
    17. Wang, Yuanfang & Roberts, Matthew C., 2005. "Realized Volatility in the Agricultural Futures Market," 2005 Annual meeting, July 24-27, Providence, RI 19211, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    18. Faisal Khan & Saif-Ur-Rehman Khan & Hashim Khan, 2016. "Pricing of Risk, Various Volatility Dynamics and Macroeconomic Exposure of Firm Returns: New Evidence on Age Effect," International Journal of Economics and Financial Issues, Econjournals, vol. 6(2), pages 551-561.
    19. Lin, Boqiang & Wesseh, Presley K., 2013. "What causes price volatility and regime shifts in the natural gas market," Energy, Elsevier, vol. 55(C), pages 553-563.
    20. Pagan, Adrian, 1996. "The econometrics of financial markets," Journal of Empirical Finance, Elsevier, vol. 3(1), pages 15-102, May.

    More about this item

    Keywords

    Conditionally Heteroskedastic Models; NLMACH(q); Volatility; Fat-tailed Distributions;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

    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:ere:journl:v:xxvii:y:2008:i:2:p:29-48. 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: Dora María Vega Facio (email available below). General contact details of provider: https://edirc.repec.org/data/feualmx.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.