Abstract
This paper presents a nonlinear model for computing the time-dependent evolution of the variance in time series of returns on assets. First, we design a recurrent network representation of the variance, which extends the typically linear models. Second, we derive temporal training equations with which the network weights are inferred so as to maximize the likelihood of the data. Experimental results show that this dynamic recurrent network model yields results with improved statistical characteristics and economic performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Brooks, C., Burke, S.P., Persand, G.: Benchmarks and the Accuracy of GARCH Model Estimation. Journal of Forecasting 17, 45–56 (2001)
Donaldson, R.G., Kamstra, M.: An Artificial Neural Network - GARCH Model of International Stock Return Volatility. Journal of Empirical Finance 4, 17–46 (1997)
Engle, R.F.: Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of UK Inflation. Econometrica 50, 987–1007 (1982)
Fiorentini, G., Calzolari, G., Panattoni, L.: Analytical Derivatives and the Computation of GARCH Estimates. Journal of Applied Econometrics 11, 399–417 (1996)
Freisleben, B., Ripper, K.: Volatility Estimation with a Neural Network. In: Proc. of the IEE/IAFE Computational Intelligence for Financial Engineering Conference (CIFEr 1997), pp. 177–181. IEEE Press, Los Alamitos (1997)
Hochreiter, S., Schmidhuber, J.: Long Short-Term Memory. Neural Computation 9, 1735–1780 (1997)
Kim, S., Shephard, N., Chib, S.: Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models. The Review of Econ. Studies 65, 361–393 (1998)
Mandic, D.P., Chambers, J.A.: Recurrent Neural Networks for Prediction. Wiley, New York (2001)
Miazhynskaia, T., Dorffner, G., Dockner, E.J.: Risk Management Application of the Recurrent Mixture Density Network Models. In: Kaynak, O., Alpaydın, E., Oja, E., Xu, L. (eds.) ICANN 2003 and ICONIP 2003. LNCS, vol. 2714, pp. 589–596. Springer, Heidelberg (2003)
Neuneier, R., Zimmermann, H.G.: How to Train Neural Networks. In: Orr, G.B., Müller, K.-R. (eds.) NIPS-WS 1996. LNCS, vol. 1524, pp. 373–423. Springer, Heidelberg (1998)
Nix, D.A., Weigend, A.S.: Estimating the Mean and Variance of the Target Probability Distribution. In: Proc. of the IEEE Int. Conf. on Neural Networks (IEEE-ICNN 1994), pp. 55–60. IEEE Press, Los Alamitos (1994)
Schittenkopf, C., Dorffner, G., Dockner, E.J.: Forecasting Time-Dependent Conditional Densities: A Semi Non-parametric Neural Network. Journal of Forecasting 19, 355–374 (2000)
Williams, R.J., Zipser, D.: A Learning Algorithm for Continually Running Fully Recurrent Networks. Neural Computation 1, 270–280 (1989)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nikolaev, N., Tino, P., Smirnov, E. (2011). Time-Dependent Series Variance Estimation via Recurrent Neural Networks. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2011. ICANN 2011. Lecture Notes in Computer Science, vol 6791. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21735-7_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-21735-7_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21734-0
Online ISBN: 978-3-642-21735-7
eBook Packages: Computer ScienceComputer Science (R0)