Abstract
From a mathematical point of view, neural networks allow the construction of models, which are able to handle high-dimensional problems along with a high degree of nonlinearity. In this chapter we deal with a special type of time-delay recurrent neural networks. In these models we understand a part of the world as a large recursive system which is only partially observable. We model and forecast all observables, avoiding the problem in open systems that we do not know the external drivers from present time on. This framework goes far beyond the paradigms of standard regression theory and allows us to forecast financial markets and perform a new way of risk analysis.
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References
Calvert, D., Kremer, S.: Networks with Adaptive State Transitions. In: Kolen, J.F., Kremer, S. (ed.) A Field Guide to Dynamical Recurrent Networks, pp. 15–25. IEEE Press (2001)
Elton, E.J., Gruber, M.J., Brown, J., Goetzmann, W.N.: Modern Portfolio Theory and Investment Analysis, 7th edn. John Wiley & Sons (2007)
Föllmer, H.: Alles richtig und trotzdem falsch? Anmerkungen zur Finanzkrise und Finanzmathematik. In: MDMV, vol. 17, pp. 148–154 (2009)
Haykin, S.: Neural Networks. A Comprehensive Foundation, 2nd edn. Macmillan College Publishing, New York (1998)
Hull, J.: Options, Futures and Other Derivative Securities. Prentice-Hall, Englewood Cliffs (2001)
Hornik, K., Stinchcombe, M., White, H.: Multilayer Feedforward Networks are Universal Approximators. Neural Networks 2, 359–366 (1989)
McNeil, A., Frey, R., Embrechts, P.: Quantitative Risk Management: Concepts. Princeton University Press, Princeton (2005)
Pearlmatter, B.: Gradient Calculations for Dynamic Recurrent Neural Networks. In: Kolen, J.F., Kremer, S. (eds.) A Field Guide to Dynamical Recurrent Networks, pp. 179–206. IEEE Press (2001)
Pearlmatter, B.: Gradient Calculations for Dynamic Recurrent Neural Networks: A survey. IEEE Transactions on Neural Networks 6(5), 1212–1228 (1995)
Poddig, T., Huber, C.: Renditeprognose mit Neuronalen Netzen. In: Kleeberg, J.M., Rehkugler, H. (eds.) Handbuch Portfoliomanagement, pp. 349–484. Bad Soden/Ts (1998)
Poddig, T., Sidorovitch, S.: Künstliche Neuronale Netze: Überblick, Einsatzmöglichkeiten und Anwendungsprobleme. In: Hippner, H., Küsters, U., Meyer, M., Wilde, K. (eds.) Handbuch Data Mining im Marketing, pp. 363–402 (2001)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning Internal Representations by Error Propagation. In: Rumelhart, D.E., McClelland, J.L., et al. (eds.) Parallel Distributed Processing, Foundations. vol. 1. MIT Press, Cambridge (1986)
Schäfer, A.M., Zimmermann, H.-G.: Recurrent Neural Networks are Universal Approximators. In: Kollias, S.D., Stafylopatis, A., Duch, W., Oja, E. (eds.) ICANN 2006. LNCS, vol. 4131, pp. 632–640. Springer, Heidelberg (2006)
Wei, W.S.: Time Series Analysis: Univariate and Multivariate Methods. Addison-Wesley Publishing Company, N.Y. (1990)
Werbos, P.J.: Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. PhD. Thesis, Harvard University (1974)
Williams, R.J., Zipser, D.: A Learning Algorithm for continually running fully recurrent neural networks. Neural Computation 1(2), 270–280 (1989)
Zimmermann, H.G., Grothmann, R., Neuneier, R.: Modeling of Dynamical Systems by Error Correction Neural Networks. In: Soofi, A., Cao, L. (eds.) Modeling and Forecasting Financial Data, Techniques of Nonlinear Dynamics. Kluwer (2002)
Zimmermann, H.G., Grothmann, R., Schäfer, A., Tietz, C.: Modeling Large Dynamical Systems with Dynamical Consistent Neural Networks. In: Haykin, S., Principe, J.C., Sejnowski, T.J., McWhirter, J. (eds.) New Directions in Statistical Signal Processing: From Systems to Brain. MIT Press, Cambridge (2006)
Zimmermann, H.G.: Neuronale Netze als Entscheidungskalkül. In: Rehkugler, H., Zimmermann, H.G. (eds.) Neuronale Netze in der Ökonomie, Grundlagen und wissenschaftliche Anwendungen. Vahlen, Munich (1994)
Zimmermann, H.G., Neuneier, R.: Neural Network Architectures for the Modeling of Dynamical Systems. In: Kolen, J.F., Kremer (eds.) A Field Guide to Dynamical Recurrent Networks, pp. 311–350. IEEE Press (2001)
Zimmermann, H.G., von Jouanne-Diedrich, H., Grothmann, R., Tietz, C.: Market Modeling, Forecasting and Risk Analysis with Historical Consistent Neural Networks. In: Hu, B., et al. (eds.) Operations Research Proceedings 2010, Selected Papers of the Annual International Conference of the German Operations Research Society (GOR), pp. 531–536. Springer, Heidelberg (2011)
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Zimmermann, HG., Tietz, C., Grothmann, R. (2013). Historical Consistent Neural Networks: New Perspectives on Market Modeling, Forecasting and Risk Analysis. In: Georgieva, P., Mihaylova, L., Jain, L. (eds) Advances in Intelligent Signal Processing and Data Mining. Studies in Computational Intelligence, vol 410. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28696-4_10
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DOI: https://doi.org/10.1007/978-3-642-28696-4_10
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