IDEAS home Printed from https://ideas.repec.org/p/bdm/wpaper/2006-04.html
   My bibliography  Save this paper

Volatility Forecasts for the Mexican Peso - U.S. Dollar Exchange Rate: An Empirical Analysis of Garch, Option Implied and Composite Forecast Models

Author

Listed:
  • Benavides Guillermo

Abstract

The volatility accuracy of several volatility forecast models is examined for the case of daily spot returns for the Mexican peso - US Dollar exchange rate. The models applied are univariate GARCH, a multi-variate GARCH (the BEKK model), option implied volatilities, and a composite forecast model. The composite specification includes time-series (ARCH-type) and option implied volatility forecasts. Different to most of the literature, this paper includes a statistical evaluation of the forecast accuracy of a composite model and models that are not combined. The results show that the composite volatility forecasts are superior to the other models in terms of mean squared errors (MSE). In forecast evaluations of the MSE it was found that estimates were statistically significantly different between composite forecast estimates and its counterparts. According to these results conclusions are as follows: the composite model is superior and both type of data -historical and implied in option prices- must be used when available.

Suggested Citation

  • Benavides Guillermo, 2006. "Volatility Forecasts for the Mexican Peso - U.S. Dollar Exchange Rate: An Empirical Analysis of Garch, Option Implied and Composite Forecast Models," Working Papers 2006-04, Banco de México.
  • Handle: RePEc:bdm:wpaper:2006-04
    as

    Download full text from publisher

    File URL: https://www.banxico.org.mx/publications-and-press/banco-de-mexico-working-papers/%7B88CED925-09D7-C2C1-E378-F41BF3FBE212%7D.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bollerslev, Tim & Engle, Robert F & Wooldridge, Jeffrey M, 1988. "A Capital Asset Pricing Model with Time-Varying Covariances," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 116-131, February.
    2. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(1), pages 122-150, February.
    3. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    4. Barone-Adesi, Giovanni & Whaley, Robert E, 1987. "Efficient Analytic Approximation of American Option Values," Journal of Finance, American Finance Association, vol. 42(2), pages 301-320, June.
    5. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    6. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    7. Fang, Yue, 2003. "Forecasting combination and encompassing tests," International Journal of Forecasting, Elsevier, vol. 19(1), pages 87-94.
    8. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    9. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521634809, January.
    10. Blair, Bevan J. & Poon, Ser-Huang & Taylor, Stephen J., 2001. "Forecasting S&P 100 volatility: the incremental information content of implied volatilities and high-frequency index returns," Journal of Econometrics, Elsevier, vol. 105(1), pages 5-26, November.
    11. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
    12. Akgiray, Vedat, 1989. "Conditional Heteroscedasticity in Time Series of Stock Returns: Evidence and Forecasts," The Journal of Business, University of Chicago Press, vol. 62(1), pages 55-80, January.
    13. Ernst R. Berndt & Bronwyn H. Hall & Robert E. Hall & Jerry A. Hausman, 1974. "Estimation and Inference in Nonlinear Structural Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 3, number 4, pages 653-665, National Bureau of Economic Research, Inc.
    14. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    15. Engle, Robert F, 2000. "Dynamic Conditional Correlation - A Simple Class of Multivariate GARCH Models," University of California at San Diego, Economics Working Paper Series qt56j4143f, Department of Economics, UC San Diego.
    16. Brooks, C. & Henry, O.T. & Persand, G., 1999. "Optimal Hedging and the Value of News," Department of Economics - Working Papers Series 717, The University of Melbourne.
    17. Christensen, B. J. & Prabhala, N. R., 1998. "The relation between implied and realized volatility," Journal of Financial Economics, Elsevier, vol. 50(2), pages 125-150, November.
    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. Benavides Guillermo, 2010. "Forecasting Short-Run Inflation Volatility using Futures Prices: An Empirical Analysis from a Value at Risk Perspective," Working Papers 2010-12, Banco de México.
    2. Guillermo Benavides, 2010. "Forecasting Short-Run Inflation Volatility using Futures Prices: An Empirical Analysis from a Value at Risk Perspective," Revista de Administración, Finanzas y Economía (Journal of Management, Finance and Economics), Tecnológico de Monterrey, Campus Ciudad de México, vol. 4(2), pages 1-27.
    3. Benavides, Guillermo & Capistrán, Carlos, 2012. "Forecasting exchange rate volatility: The superior performance of conditional combinations of time series and option implied forecasts," Journal of Empirical Finance, Elsevier, vol. 19(5), pages 627-639.
    4. Emilian DOBRESCU, 2017. "Modelling an Emergent Economy and Parameter Instability Problem," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 5-28, June.
    5. Benavides Guillermo, 2020. "Asymmetric Volatility Effects in Risk Management: An Empirical Analysis using a Stock Index Futures," Working Papers 2020-10, Banco de México.
    6. Guillermo Benavides, 2021. "Asymmetric Volatility Relevance in Risk Management: An Empirical Analysis using Stock Index Futures," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 16(TNEA), pages 1-18, Septiembr.

    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. Guillermo Benavides Perales, 2009. "Price volatility forecasts for agricultural commodities: an application of volatility models, option implieds and composite approaches forfutures prices of corn and wheat," Revista de Administración, Finanzas y Economía (Journal of Management, Finance and Economics), Tecnológico de Monterrey, Campus Ciudad de México, vol. 3(2), pages 40-59.
    2. Benavides, Guillermo, 2009. "Predictive Accuracy of Futures Options Implied Volatility: the Case of the Exchange Rate Futures Mexican Peso-Us Dollar," Panorama Económico, Escuela Superior de Economía, Instituto Politécnico Nacional, vol. 0(09), pages 55-95, segundo s.
    3. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654, January.
    4. Bali, Turan G. & Weinbaum, David, 2007. "A conditional extreme value volatility estimator based on high-frequency returns," Journal of Economic Dynamics and Control, Elsevier, vol. 31(2), pages 361-397, February.
    5. Benavides, Guillermo & Capistrán, Carlos, 2012. "Forecasting exchange rate volatility: The superior performance of conditional combinations of time series and option implied forecasts," Journal of Empirical Finance, Elsevier, vol. 19(5), pages 627-639.
    6. 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.
    7. Degiannakis, Stavros & Xekalaki, Evdokia, 2004. "Autoregressive Conditional Heteroskedasticity (ARCH) Models: A Review," MPRA Paper 80487, University Library of Munich, Germany.
    8. 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.
    9. 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.
    10. Koopman, Siem Jan & Jungbacker, Borus & Hol, Eugenie, 2005. "Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements," Journal of Empirical Finance, Elsevier, vol. 12(3), pages 445-475, June.
    11. Shiyi Chen & Wolfgang K. Härdle & Kiho Jeong, 2010. "Forecasting volatility with support vector machine-based GARCH model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(4), pages 406-433.
    12. Rossi, Alessandro & Gallo, Giampiero M., 2006. "Volatility estimation via hidden Markov models," Journal of Empirical Finance, Elsevier, vol. 13(2), pages 203-230, March.
    13. Adrian Fernandez‐Perez & Bart Frijns & Ilnara Gafiatullina & Alireza Tourani‐Rad, 2019. "Properties and the predictive power of implied volatility in the New Zealand dairy market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(5), pages 612-631, May.
    14. Andrea BUCCI, 2017. "Forecasting Realized Volatility A Review," Journal of Advanced Studies in Finance, ASERS Publishing, vol. 8(2), pages 94-138.
    15. David Feldman & Xin Xu, 2018. "Equilibrium-based volatility models of the market portfolio rate of return (peacock tails or stotting gazelles)," Annals of Operations Research, Springer, vol. 262(2), pages 493-518, March.
    16. Athanasia Gavala & Nikolay Gospodinov & Deming Jiang, 2006. "Forecasting volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(6), pages 381-400.
    17. Bauer, Rob M M J & Nieuwland, Frederick G M C & Verschoor, Willem F C, 1994. "German Stock Market Dynamics," Empirical Economics, Springer, vol. 19(3), pages 397-418.
    18. Szakmary, Andrew & Ors, Evren & Kyoung Kim, Jin & Davidson, Wallace III, 2003. "The predictive power of implied volatility: Evidence from 35 futures markets," Journal of Banking & Finance, Elsevier, vol. 27(11), pages 2151-2175, November.
    19. Degiannakis, Stavros & Filis, George, 2017. "Forecasting oil price realized volatility using information channels from other asset classes," Journal of International Money and Finance, Elsevier, vol. 76(C), pages 28-49.
    20. Choudhry, Taufiq, 2004. "The hedging effectiveness of constant and time-varying hedge ratios using three Pacific Basin stock futures," International Review of Economics & Finance, Elsevier, vol. 13(4), pages 371-385.

    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
    • 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
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

    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:bdm:wpaper:2006-04. 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: Subgerencia de desarrollo de sistemas (email available below). General contact details of provider: https://edirc.repec.org/data/bangvmx.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.