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Bayesian Approach to Studying the Impact of Data Quality on Estimating the Term Structure of Interest Rates

Student: Kumarova Akmaral

Supervisor: Victor A Lapshin

Faculty: Faculty of Economic Sciences

Educational Programme: Applied Economics (Master)

Year of Graduation: 2017

The paper represents a study of the impact of data quality on the estimation of the term structure of interest rates on the Russian government bond market. The Russian bond market is characterized by low and heterogeneous liquidity. In the work, there were considered three models for constructing the urgent structure of interest rates: the Nelson-Siegel model, the Cox-Ingersoll-Ross model and the B-spline model. A numerical experiment was performed in order to describe a comparison of all three models. In this study, the parameters of the models were estimated by the MCMC method, namely, using the Metropolis-Hastings algorithm.

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