Abstract
With the increasing number of data in China’s interest rate market, the model is increasingly complex. For most interest rate models at this stage, its parameter estimation has become the focus of many scholars in recent years. At present, for the parameter estimation of the interest rate model, there is often a problem that the parameter estimation accuracy is not high and the algorithm stability is poor. Therefore, this research work investigates the parameter estimation method of the no-arbitrage Nelson–Siegel model for the classical interest rate model and points out the shortcomings of the estimation method. In the iterative process, the mean error and the estimated error covariance will expand continuously. The phenomenon eventually leads to the consequences of poor estimation. In this study, an improved adaptive Kalman filtering method is proposed. In the process of algorithm updating, an exponential decay factor is added to improve the accuracy of parameter estimation and the stability of the algorithm. Finally, based on China’s national debt data, the simulation experiment is carried out.
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Acknowledgements
This work was supported by Liaoning education department humanities and social sciences general project (No. JRW2019004).
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Zhang, Q., Wang, X., Zhou, X. et al. Application of improved adaptive Kalman filter in China’s interest rate market. Neural Comput & Applic 32, 5315–5327 (2020). https://doi.org/10.1007/s00521-020-04706-z
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DOI: https://doi.org/10.1007/s00521-020-04706-z