Engineering a Generalized Neural Network Mapping of Volatility Spillovers in European Government Bond Markets

Part of the Springer Optimization and Its Applications book series (SOIA, volume 18)


Excess Return Government Bond Bond Market Bond Index Volatility Spillover 


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  1. 1.
    Amari, S.-I. Mathematical foundations of neurocomputing. IEEE Transactions on Neural Networks, 78(9):1443–1463, 1990.Google Scholar
  2. 2.
    Andersen, T., Bollerslev, T., Diebold, F., and Ebens, H. The distribution of realized stock return volatility. Journal of Financial Economics, 61(1):43–76, 2001.CrossRefGoogle Scholar
  3. 3.
    Baele, L. Volatility spillover effects in European equity markets: Evidence from a regime switching model. Ghent University, Working paper, 2002.Google Scholar
  4. 4.
    Bekaert, G., and Harvey, C. R. Time-varying world market integration. Journal of Finance, 50(2):403–444, 1995.CrossRefGoogle Scholar
  5. 5.
    Bekaert, G., and Harvey, C. R. Emerging equity market volatility. Journal of Financial Economics, 43:29–77, 1997.CrossRefGoogle Scholar
  6. 6.
    Bekaert, G., Harvey, C. R., and Ng, A. Market integration and contagion. Journal of Business, 78:39–69, 2005.CrossRefGoogle Scholar
  7. 7.
    Berndt, E. K., Hall, B. H., Hall, R. E., and Hausman, J. A. Estimation and inference in nonlinear structural models. Annals of Economic and Social Measurement, 3:653–665, 1974.Google Scholar
  8. 8.
    Bollerslev, T. Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31:307–327, 1986.MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Booth, G. G., Martikainen, T., and Tse, T. Price and volatility spillovers in Scandinavian stock markets. Journal of Banking and Finance, 21:811–823, 1997.CrossRefGoogle Scholar
  10. 10.
    Broomhead, D. S., and Lowe, D. Multivariate functional interpolation and adaptive networks. Complex Systems, 2:321–355, 1988.MATHMathSciNetGoogle Scholar
  11. 11.
    Christiansen, C. Volatility-spillover effects in European bond markets. University of Aarhus, Denmark, Working paper series no. 162, 2003.Google Scholar
  12. 12.
    Clare, A., and Lekkos, I. An analysis of the relationship between international bond markets. Bank of England, Working paper, 2000.Google Scholar
  13. 13.
    Craven, M., and Shavlik, J. Using neural networks for data mining. Future Generation Computer Systems, 1997.Google Scholar
  14. 14.
    Crouse, R. H., Jin, C., and Hanumara, R. C. Unbiased ridge estimation with prior information and ridge trace. Communication in Statistics, 24(9):2341–2354, 1995.MATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Dash, G., Hanumara, C., and Kajiji, N. Neural network architectures for efficient modeling of FX futures options volatility. Operational Research: An International Journal, 3(1):3–23, 2003.Google Scholar
  16. 16.
    Dash, G., and Kajiji, N. New evidence on the predictability of South African FX volatility in heterogenous bilateral markets. The African Finance Journal, 5(1):1–15, 2003.Google Scholar
  17. 17.
    Diamond, F., Simons, J. et al. J.P. Morgan government bond indices. J.P. Morgan, Portfolio Research Report, 2002.Google Scholar
  18. 18.
    Engle, R. F. Autoregressive conditional heteroskedasticity with estimates of the variance of U.K. inflation. Econometrica, 50:987–1008, 1982.MATHCrossRefMathSciNetGoogle Scholar
  19. 19.
    Hemmerle, W. J. An explicit solution for generalized ridge regression. Technometrics, 17(3):309–314. 1975.MATHCrossRefMathSciNetGoogle Scholar
  20. 20.
    Hoerl, A. E., and Kennard, R. W. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(3):55–67, 1970.MATHCrossRefGoogle Scholar
  21. 21.
    Kajiji, N. Adaptation of alternative closed form regularization parameters with prior information to the radial basis function neural network for high frequency financial rime series. University of Rhode Island, 2001.Google Scholar
  22. 22.
    Kaski, S. Data exploration using self-organizing maps. Neural Networks Research Centre, Helsinki University of Technology, 1997.Google Scholar
  23. 23.
    Kaski, S., and Kohonen, T. Exploratory data analysis by the self-organizing map: Structures of welfare and poverty in the world. In A. Refenes, Y. Abu-Mostafa, J. Moody, and A. Weigend, Editors, Neural Networks in Financial Engineering, World Scientific, Singapore, 1996, pages 498–507.Google Scholar
  24. 24.
    Kohonen, T. The self-organizing map. Proceedings of the IEEE, 78(9):1464–1480 1990.CrossRefGoogle Scholar
  25. 25.
    Koutmos, G., and Booth, G. G. Asymmetric volatility transmission in international stock markets. Journal of International Money and Finance, 14:747–762, 1995.CrossRefGoogle Scholar
  26. 26.
    Ljung, G., and Box, G. On a measure of lack of fit in time series models. Biometrika, 67:297–303, 1978.CrossRefGoogle Scholar
  27. 27.
    Lohninger, H. Evaluation of neural networks based on radial basis functions and their application to the prediction of boiling points from structural parameters. Journal of Chemical Information and Computer Sciences, 33:736–744, 1993.Google Scholar
  28. 28.
    Lu, H., Setiono, R., and Liu, H. Effective data mining using neural networks. IEEE Transactions on Knowledge and Data Engineering, 8(6):957–961, 1996.CrossRefGoogle Scholar
  29. 29.
    McNelis, P. D. Neural Networks in Finance: Gaining Predictive Edge in the Market. Elsevier Academic Press, Burlington, MA, 2005.Google Scholar
  30. 30.
    Mehta, K., and Bhattacharyya, S. Adequacy of training data for evolutionary mining of trading rules. Decision Support Systems, 37(4):461–474, 2004.CrossRefGoogle Scholar
  31. 31.
    Nelson, D. B. Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59:347–370, 1991.MATHCrossRefMathSciNetGoogle Scholar
  32. 32.
    Nelson, D. B., and Cao, C. Q. Inequality constraints in the univariate GARCH model. Journal of Business and Economic Statistics, 10:229–235, 1992.CrossRefGoogle Scholar
  33. 33.
    Ng, A. Volatility spillover effects from Japan and the US to the pacific-basin. Journal of International Money and Finance, 19:207–233, 2000.CrossRefGoogle Scholar
  34. 34.
    Orr, M. J. L. Introduction to Radial Basis Function Networks. Center for Cognitive Science, Scotland, UK, 1996.Google Scholar
  35. 35.
    Orr, M. J. L. MATLAB Routines for Subset Selection and Ridge Regression in Linear Neural Networks. Center for Cognitive Science, Scotland, UK, 1997.Google Scholar
  36. 36.
    Parthasarathy, K., and Narendra, K. Stable adaptive control of a class of discrete-time nonlinear systems using radial basis neural networks. Yale University, Report No. 9103, 1991.Google Scholar
  37. 37.
    Polak, E. Computational Methods in Optimization. Academic Press, New York, 1971.Google Scholar
  38. 38.
    Refenes, A. N., and Bolland, P. Modeling quarterly returns on the FTSE: A comparative study with regression and neural networks. In C. H. Chen, Editor, Fuzzy Logic and Neural Network Handbook. McGraw-Hill, New York, 1996.Google Scholar
  39. 39.
    Rustichini, A., Dickhaut, J., Ghirardato, P., Smith, K., and Pardo, J. V. A brain imaging study of procedural choice. University of Minnesota, Working paper, 2002.Google Scholar
  40. 40.
    Saarinen, S., Bramley, R., and Cybenko, G. Ill- conditioning in neural network training problems. SIAM Journal of Scientific Computing, 14(3):693–714, 1993.MATHCrossRefMathSciNetGoogle Scholar
  41. 41.
    Sanner, R. M., and Slotine, J.-J. E. Gaussian networks for direct adaptive control. IEEE Transactions on Neural Networks, 3:837–863, 1992.CrossRefGoogle Scholar
  42. 42.
    Schraudolph, N. N., and Sejnowski, T. J. Tempering backpropagation networks: Not all weights are created equal. In D. S. Touretzky, M. C. Moser, and M. E. Hasselmo, Editors, Advances in Neural Information Processing Systems, MIT Press, Cambridge, 1996, pages 563–569.Google Scholar
  43. 43.
    Shapiro, S. S., and Wilk, M. B. An analysis of variance test for normality (complete samples). Biometrika, 52(3/4):597–611, 1965.CrossRefMathSciNetGoogle Scholar
  44. 44.
    Shi, J. J. Reducing prediction error by transforming input data for neural networks. Journal of Computing in Civil Engineering, 14(2):109–116, 2000.CrossRefGoogle Scholar
  45. 45.
    Skintzi, V. D., and Refenes, A.-P. N. Volatility spillovers and dynamic correlation in European bond markets. Financial Engineering Research Center, Athens University of Economics and Business, Athens, 2004.Google Scholar
  46. 46.
    Smagt, P. Minimization methods for training feed-forward networks. Neural Networks, 7(1):1–11, 1994.CrossRefGoogle Scholar
  47. 47.
    Tikhonov, A., and Arsenin, V. Solutions of Ill-Posed Problems. Wiley, New York, 1977.Google Scholar
  48. 48.
    Weigend, A. S., and Rumelhart, D. Generalization of by weight-elimination applied to currency exchange rate prediction. Proceedings of IJCNN, New York, 1991.Google Scholar
  49. 49.
    Zirilli, J. S. Financial Prediction Using Neural Networks. International Thompson Computer Press, London, 1997.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.ProvidenceUSA
  2. 2.National Center on Public Education & Social PolicyUniversity of Rhode IslandProvidenceUSA

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