Neural network architectures for efficient modeling of FX futures options volatility
- 113 Downloads
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.
KeywordsGARCH Radial Basis Function FX Futures Options Volatility
Unable to display preview. Download preview PDF.
- 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
- 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
- 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
- Ghysels E. A., Harvey A., and Renault E. (1996). Stochastic Volatility, inHandbook of Statistics, (G.S. Maddala, ed.). North Holland, Amsterdam,Google Scholar
- 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
- 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
- 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
- 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
- Olaf W. (1997). Predicting Stock Index Returns by Means of Genetically Engineered Neural Networks. InDepartment of Management. University of California, Los Angeles.Google Scholar
- 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