Operational Research

, Volume 3, Issue 1, pp 3–23 | Cite as

Neural network architectures for efficient modeling of FX futures options volatility

  • Gordon H. Dash
  • Choudary R. Hanumara
  • Nina Kajiji

Abstract

The importance of volatility modeling is evidenced by the voluminous literature on temporal dependencies in financial market assets. A substantial body of this literature relies on explorations of daily and lower frequencies using parametric ARCH or stochastic volatility models. In this research we compare the model performance of alternate neural network models against that of the (G)ARCH framework when applied to hourly volatility of FX futures options. We report that the results obtained from the application of a closed-form Bayesian regularization radial basis function neural network are considerably more efficient than those produced by alternate neural network topologies and the (G)ARCH model formulation.

Keywords

GARCH Radial Basis Function FX Futures Options Volatility 

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References

  1. Andersen T. G. (2000). Some Reflections on Analysis of High-Frequency Data.Journal of Business & Economic Statistics vol. 18(2), 146–153.CrossRefGoogle Scholar
  2. Baillie R. and Bollerslev T. (1989). The message in daily exchange rates: A conditional-variance tale.Journal of Business & Economic Statistics vol. 7 297–306.CrossRefGoogle Scholar
  3. Beckers S. (1981). Standard Deviations Implied in Option Prices as Predictors of Future Stock Price Variability.Journal of Banking and Finance vol. 363–381.Google Scholar
  4. Black F. (1976). The Pricing of Commodity Contracts.Journal of Financial Economics vol. 3(Jan–Feb), 167–179.CrossRefGoogle Scholar
  5. Bolland P. J., Connor J. T., and Refenes A.-P. N. (1998). Application of Neural Networks to Forecast High Frequency Data: Foreign Exchange, inNonlinear Modelling of High Frequency Financial Time Series, (B. Zhou, ed.). John Wiley & sons, Chichester, 225–246Google Scholar
  6. Bollerslev T. (1986). Generalized Autoregressive Conditional Heteroskedasticity.Journal of Econometrics vol. 31 307–327.CrossRefGoogle Scholar
  7. Bollerslev T., Cai J., and Song F. M. (2000). Intraday Periodicity, Long Memory Volatility, and Macroeconomic Announcement Effects in the US Treasury Bond Market.Journal of Empirical Finance vol. 7 37–55.CrossRefGoogle Scholar
  8. Coats P. and Fant L. (1992). A Neural Network Approach to Forecasting Financial Distress.Journal of Business Forecasting vol. 10(4), 9–12.Google Scholar
  9. Dacorogna M. M., Muller U. A., Nagler R. J., Olsen R. B., and Pictet O. V. (1993). A Geographical Model for the Daily and Weekly Seasonal Volatility in the FX Market.Journal of International Money and Finance vol. 12 413–438.CrossRefGoogle Scholar
  10. Elanyar V. T. and Shin Y. C. (1994). Radial Basis Function Neural Network for Approximation and Estimation of Non linear Stochastic Dynamic Systems.IEEE Transactions on Neural Networks vol. 5(4), 594–603.CrossRefGoogle Scholar
  11. Engle R. F. (1982). Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of U.K. Inflation.Econometrica vol. 50 987–1008.CrossRefGoogle Scholar
  12. Ghysels E. A., Harvey A., and Renault E. (1996). Stochastic Volatility, inHandbook of Statistics, (G.S. Maddala, ed.). North Holland, Amsterdam,Google Scholar
  13. Hutchinson J. M., Lo A. W., and Poggio T. (1996). A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks, inNeural Networks in Finance and Investing: Using Artificial Intelligence to Improve Real-World Performance, (E. Turban, ed.). McGraw Hill, New York, Chapter 33Google Scholar
  14. Jorion P. (1995). Predicting Volatility in the Foreign Exchange Market.The Journal of Finance vol. L(2), 507–528.CrossRefGoogle Scholar
  15. Kaastra I. and Boyd M. S. (1995). Forecasting Futures Trading Volume Using Neural networks.Journal of Futures Markets vol. 15(8), 953–970.CrossRefGoogle Scholar
  16. Kajiji N. (2001). Adaptation of Alternative Closed Form Regularization Parameters with Prior Information to the Radial Basis Function Neural Network for High Frequency Financial Time Series. InApplied Mathematics. University of Rhode island, Kingston.Google Scholar
  17. Malliaris M. and Salchengerger L. (1996). Neural Networks for Predicting Options Volatility, inNeural Networks in Finance and Investing, (E. Turban, ed.). McGraw-Hill, New York, 613–622Google Scholar
  18. Muller U. A., Dacorogna M. M., Olsen R. B., Pictet O. V., Schwarz M., and Morgenegg C. (1990). Statistical Study of Foreign Exchange Rate, Empirical Evidence of Price Change Scaling Law, and Intraday Analysis.Journal of Banking and Finance vol. 14 1189–1208.CrossRefGoogle Scholar
  19. Nelson D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach.Econometrica vol. 59 pp. 347–370.CrossRefGoogle Scholar
  20. Niranjan M. (1997). Sequential Tracking in Pricing Financial Options using Model Based and Neural Network Approaches, inAdvances in Neural Information Processing Systems, (M.C. Mozer, Jordan, Michael I., and Petsche, Thomas, ed.). The MIT Press, Boston, 960–972Google Scholar
  21. Olaf W. (1997). Predicting Stock Index Returns by Means of Genetically Engineered Neural Networks. InDepartment of Management. University of California, Los Angeles.Google Scholar
  22. Refenes A. N. and Bolland P. (1996). Modeling Quarterly Returns on the FTSE: A Comparative Study with Regression and Neural Networks, inFuzzy Logic and Neural Network Handbook, (C.H. Chen, ed.). McGraw-Hill, New York, 19.1–19.28Google Scholar
  23. Sohl J. E. and Venkatachalam A. R. (1995). A Neural Network Approach to Forecasting Model Selection.Information Management vol. 29(6), 297–303.CrossRefGoogle Scholar

Copyright information

© Hellenic Operational Research Society 2003

Authors and Affiliations

  • Gordon H. Dash
    • 1
  • Choudary R. Hanumara
    • 1
  • Nina Kajiji
    • 1
  1. 1.University of Rhode IslandKingstonUSA
  2. 2.Providence

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