Predictive accuracy of futures options implied volatility: the case of the exchange rate futures mexican peso-us dollar

Existe una cantidad substancial de investigación destinada a pronosticar la volatilidad de los rendimientos de precios de futuros de activos financieros. Una parte significativa de la literatura muestra que pronosticar la mencionada volatilidad con certeza no es una tarea fácil, independientemente del modelo de pronóstico utilizado. En el presente trabajo de investigación se analiza el poder predictivo de varios modelos de pronósticos de volatilidad diaria de los rendimientos de los futuros del tipo de cambio Peso Mexicano–Dólar Estadounidense. Los modelos que se utilizan son: univariado GARCH; multi-variado GARCH (modelo BEKK); dos modelos de volatilidad implícita de opciones; y, un modelo de pronóstico compuesto. Diferente a otros trabajos en la literatura, en el presente documento se realiza un análisis más riguroso de los cálculos de la volatilidad implícita de opciones. Los resultados muestran que los modelos de volatilidad implícita de opciones fueron superiores a los modelos históricos en términos de certeza al pronosticar; y, que el modelo compuesto fue el más certero en términos del error cuadrático medio, al compararlo con el resto de los modelos. Sin embargo, los resultados deben interpretarse con prudencia dado que el coeficiente de determinación en las regresiones fue relativamente bajo. De acuerdo a los resultados se recomienda utilizar modelos de pronóstico compuesto si ambos tipos de datos, series de tiempo (históricas) y de volatilidad implícita de opciones, están disponibles.

Modelos de pronóstico compuesto, tarifas de cambio, GARCH multivariado, volatilidad de opciones implicadas, pronóstico de volatilidad

Akgiray, V. (1989), “Conditional Heteroscedasticity in Time Series of Stock Returns: Evidence and Forecasts”, Journal of Business, Vol. 62 (55-80).

Baba, Y., R. F. Engle, K. F. Kroner and D. Kraft (1992), “Multivariate Simultaneous Generalized ARCH”, Economics Working Paper 92-5, University of Arizona, Tucson.

Barone-Adesi, G. and R. E. Whaley (1987), “Efficient Approximation of American Option Values”, The Journal of Finance, Vol. 42, June (301-320).

Bates, J. M. and C. W. J. Granger (1969), “The Combination of Forecasts”, Operations Research Quarterly, Vol. 20 (451-468).

Benavides, G. (2003), “Price Volatility Forecasts for Agricultural Commodities: An Application of Historical Volatility Models, Option Implied and Composite Approaches for Futures Prices of Corn and Wheat”, Workingpaper.

Berndtand, E., B. Hall, R. Hall and J. Hausman (1974), “Estimation and Inference in Nonlinear Structural Models”, Annals of Economic and Social Measurement (653–665).

Bessler, D. A. and J. A. Brandy (1981), “Forecasting Livestock Prices with Individual and Composite Methods”, Applied Economics, Vol. 13 (513–522).

Black, F. and M. S. Scholes (1973), “The Pricing of Options and Corporate Liabilities”, Journal of Political Economy, Vol. 81, May–June (637–654).

Blair, B. J., S. Poon and S. J. Taylor (2001), “Forecasting S&P 100 Volatility: The Incremental Information Content of Implied Volatilities and High-Frequency Index Returns”, Journal of Econometrics, Vol. 105 (5–26).

Bollerslev, T. P. (1986), “Generalized Autoregressive Conditional Heteroscedasticity”, Journal of Econometrics, Vol. 31 (307–327).

Bollerslev, T., R. Engle and J. Wooldridge (1988), “A Capital Asset Pricing Model with Time-Varying Covariances”, Journal of Political Economy, No. 96 (116–131).

Bollerslev, T. P., R. Y. Chou and K. F. Kroner (1992), “ARCH Modelling in Finance: A Review of the Theory and Empirical Evidence”, Journal of Econometrics, 52 (5–59).

Brooks, C., O. T. Henry and G. Persand (2002), “Optimal Hedging and the Value of the News”, Journal of Business, Vol. 75, Issue 2 (333–52).

Brooks, C. (2002), Introductory Econometrics for Finance, Cambridge University Press.

Christensen, B. J. and N. R. Prabhala (1998), “The Relation between Implied and Realized Volatility”, Journal of Financial Economics, Volume 50, Issue 2, November (125–150).

Chu, S. H. and S. Freund (1996), “Volatility Estimation for Stock Index Options: A GARCH Approach”, Quarterly Review of Economics and Finance, Vol. 36 (431–450).

Clemen, R. T. (1989), “Combining Forecasts: A Review and Annotated Bibliography”, International Journal of Forecasting, Vol. 5 (559–583).

Clements, M. P. and Hendry, D. F. (1998), Forecasting Economic Time Series, Cambridge: Cambridge University Press.

Day, T. E. and C. M. Lewis (1992), “Stock Market Volatility and the Information Content of Stock Index Options”, Journal of Econometrics, Vol. 52 (267–287).

Diebold, F. X. and R. S. Mariano (1995), “Comparing Predictive Accuracy”, Journal of Business and Economic Statistics, Vol. 13 (253–263).

Ederington, L. and W. Guan (2002), “Is Implied Volatility an Informationally Efficient and Effective Predictor of Future Volatility?”, Journal of Risk, Vol. 4 (3).

Engle, R. F. (1982), “Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of U.K. Inflation”, Econometrica, 50 (987–1008).

Engle, R. F. (2000), “Dynamic Conditional Correlation – A Simple Class of Multivariate GARCH Models”, SSRN Discussion Paper 2000-09, University of California, San Diego, May 2000.

Engle, R. F. and K. Kroner (1995), “Multivariate Simultaneous Generalized ARCH”, Econometric Theory, 11 (122–150).

Fang, Y. (2002), “Forecasting Combination and Encompassing Tests”, International Journal of Forecasting, Vol. 1, Elsevier Science B. V.

Figlewski, S. (1997), “Forecasting Volatility”, Financial Markets, Institutions, and Instruments, Vol. 6 (2–87).

Fleming, J. (1998), “The Quality of Market Volatility Forecasts Implied by S & P 100 Index Option Prices”, Journal of Empirical Finance, Vol. 5 (317–345).

Giot, P. (2003), “Implied Volatility Indexes and Daily Value-at-Risk Models”, Working paper, Department of Business Administration & CEREFIM at University of Namur, Belgium.

Granger, C. W. J. and R. Ramanathan (1984), “Improved Methods of Combining Forecasts”, Journal of Forecasting, Vol. 3 (197–204).

Haigh, M. S. and M. T. Holt (2000), “Hedging Multiple Price Uncertainty in International Agricultural Trade”, American Journal of Agricultural Economics, Vol. 82, No. 4, November (881–896).

Harvey, C. R. and R. E. Whaley (1992), “Dividends and S&P 100 Index Options”, Journal of Futures Markets, Vol. 12 (123–137).

Hull, J. (2003), Options, Futures and Other Derivatives, 5th Edition, Prentice Hall.

Jorion, P. (1995), “Predicting Volatility in the Foreign Exchange Market”, The Journal of Finance, Vol. 50 (507–528).

Kroner, K., K. P. Kneafsey and S. Claessens (1994), “Forecasting Volatility in Commodity Markets”, Journal of Forecasting, Vol. 14 (77–95).

Lamoureux, C. G. and W. D. Lastrapes (1993), “Forecasting Stock Return Variance: Toward an Understanding of Stochastic Implied Volatilities”, The Review of Financial Studies, Vol. 6 (293–326).

Levich, R. M. (1998), International Financial Markets: Prices and Policies, Boston, Mass.: Irwin McGraw-Hill.

Makridakis, S. (1989), “Why Combining Works?”, International Journal of Forecasting, Vol. 5 (601–603).

Manfredo, M. R., M. Leuthold and S. H. Irwin (2001), “Forecasting Cash Price Volatility of Fed Cattle, Feeder Cattle and Corn: Time Series, Implied Volatility and Composite Approaches”, Journal of Agricultural and Applied Economics, Vol. 33, Issue 3, December (523–538).

Martens, M. and J. Zein (2002), “Predicting Financial Volatility: High-Frequency Time-Series Forecasts vis-a-vis Implied Volatility”, Mimeo, Erasmus University Rotterdam.

Neely, C. J. (2002), “Forecasting Foreign Exchange Volatility: Is Implied Volatility the Best We Can Do?”, Federal Reserve Bank of St. Louis, Working paper 2002-017.

Ng, V. K. and S. C. Pirrong (1994), “Fundamentals and Volatility: Storage, Spreads, and the Dynamic of Metals Prices”, Journal of Business, 67 (203–230).

Park, D. W. and W. G. Tomek (1989), “An Appraisal of Composite Forecasting Methods”, North Central Journal of Agricultural Economics, 10 (1–11).

Pojarliev, M. and W. Polasek (2000), “Portfolio Construction by Volatility Forecasts: Does the Covariance Structure Matter?”, Financial Markets and Portfolio Management, 2003, 17(1), pp. 103–117.

Poon, S.-H. and C. Granger (2003), “Forecasting Volatility in Financial Markets: A Review”, forthcoming in the Journal of Economic Literature.

Pong, S., M. Shackleton, S. Taylor and X. Xu (2003), "Forecasting Currency Volatility: A Comparison of Implied Volatilities and AR(FI)MA models, forthcoming", Journal of Banking and Finance.

Ng, V. K. and S. C. Pirrong (1994), “Fundamentals and Volatility: Storage, Spreads, and the Dynamic of Metals Prices”, Journal of Business, 67 (203–230).

Samuelson, P. (1965), “Proof that Properly Anticipated Prices Fluctuate Randomly”, Industrial Management Review, 6 Spring (41–49).

Schroeder, T. C., M. L. Albright, M. R. Langemeier and J. Mintert (1993), “Factors Affecting Cattle Feeding Profitability”, Journal of the American Society of Farm Managers and Rural Appraisers, 57 (48–54).

Susmel, R. and R. Thompson (1997), “Volatility, Storage and Convenience Evidence from Natural Gas Markets”, Journal of Futures Markets, Vol. 17, No. 1 (17–43).

Taylor, S. J. (1985), “The Behaviour of Futures Prices Overtime”, Applied Economics, 17, 4 Aug. (713–734).

Wei, A. and R. M. Leuthold (1998), “Long Agricultural Futures Prices: ARCH, Long Memory, or Chaos Processes”, OFOR Paper Number 98-03.

Xu, X. and S. J. Taylor (1995), “Conditional Volatility and the Informational Efficiency of the PHLX Currency Options Market”, Journal of Banking and Finance, Vol. 19 (803–821).