Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30121
A Hybrid Particle Swarm Optimization-Nelder- Mead Algorithm (PSO-NM) for Nelson-Siegel- Svensson Calibration

Authors: Sofia Ayouche, Rachid Ellaia, Rajae Aboulaich

Abstract:

Today, insurers may use the yield curve as an indicator evaluation of the profit or the performance of their portfolios; therefore, they modeled it by one class of model that has the ability to fit and forecast the future term structure of interest rates. This class of model is the Nelson-Siegel-Svensson model. Unfortunately, many authors have reported a lot of difficulties when they want to calibrate the model because the optimization problem is not convex and has multiple local optima. In this context, we implement a hybrid Particle Swarm optimization and Nelder Mead algorithm in order to minimize by least squares method, the difference between the zero-coupon curve and the NSS curve.

Keywords: Optimization, zero-coupon curve, Nelson-Siegel- Svensson, Particle Swarm Optimization, Nelder-Mead Algorithm.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1124551

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 879

References:


[1] Bliss R. and Fama E. The Information in Long Maturity Forward Rates American Economic Review,Vol. 77 , 680.692.1987.
[2] Bonnin F., Planchet F. and Juillard M Application of stochastic techniques for the prospective analysis of the fiscal impact of interest rate risk-Example on financial expenses of a complex bond debt French Bulletin of Actuarial,vol. 11, no 21,2011.
[3] Diebold F.X. and LI C Forecasting the term structure of government bond yields. Journal of Econometrics,vol. 130, no 2, 337.364,2006.
[4] Diebold F. X.and Rudebusch G. D.and Borag
[caron]an Aruoba S The macroeconomy and the yield curve: A dynamic latent factor approach. Journal of Econometrics Vol.131,309-338, 2006.
[5] Gilli. M. and Grosse.S and Schumann. E. Calibrating the Nelson-Siegel-Svensson Model. Computational Optimization Methods in Statistics, Econometrics and Finance, 2010.
[6] Gimeno. R. and Nave, Juan Migue. Genetic Algorithm Estimation of Interest Rate Term Structure.. Banco de Espana Research Paper No. WP-0634,2010.
[7] Hautsch N. E and Ou Y. Analyzing interest rate risk: Stochastic volatility in the term structure of government bond yields. Journal of Banking and Finance Vol.36, 2988-3007,2012.
[8] Kao Y.T., E. Zahara, and I. W. Kao A hybridized approach to data clustering. Expert Systems with Applications, vol. 34, no. 3, pp. 17541762, 2008.
[9] Kennedy, J. and Eberhart, R. Particle Swarm Optimization In Proceedings of IEEE International Conference on Neural Network, volume IV, 1995.
[10] Koduru P., S. Das, S. M. Welch, J. L. Roe. Fuzzy Dominance Based Multi-objective GA-Simplex Hybrid Algorithms Applied to Gene Network Models Lecture Notes in Computer Science: Proceedings of the Genetic and Evolutionary Computing Conference, Seattle, Washington, (Eds. Kalyanmoy Deb et al.), Springer-Verlag, Vol 3102, pp. 356-367, 2004.
[11] Liu.A, and Yang M.T A New Hybrid Nelder-Mead Particle Swarm Optimization for Coordination Optimization of Directional Overcurrent Relays. Hindawi Publishing Corporation Mathematical Problems in Engineering Volume 2012, Article ID 456047, 18 pages,2012.
[12] McCulloch J.H The Tax-Adjusted Yield Curve The Journal of Finance Vol.30, 811-830,1975.
[13] Nelson C. and Siegel A. F Parsimonious Modeling of Yield Curves Journal of Business, Vol. 60, 473.489,1987.
[14] Svensson L. E. O Estimating and Interpreting Forward Interest Rates: Sweden 1992-1994 IMFWorking Paper , 1.49,1994.
[15] Vasicek O. A. and Fong H. G. Term Structure Modeling Using Exponential Splines. Journal of Finance,Vol. 73,339.348,1982.