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Forecasting the Term Structure in Emerging Markets Using Extensions of the Dynamic Nelson-Siegel Model

Student: Maksim Anisimov

Supervisor: Oxana A. Malakhovskaya

Faculty: Faculty of Economic Sciences

Educational Programme: Economics (Bachelor)

Final Grade: 8

Year of Graduation: 2019

The dynamic Nelson-Siegel model and its extensions are used by many central banks to forecast the term structure. Their forecasting performance has been studied for many countries, but a little can be said about their accuracy for a set of emerging economies. In this work I test the traditional dynamic Nelson-Siegel models, its extensions without the "curvature" factor, with inflation, Bayesian estimation for six developing countries. Yet another extension is model selection via BIC minimization. The results indicate that adding inflation and Bayesian inference improve forecasting performance relative to the traditional models. Also, I conclude that the multivariate dynamic Nelson-Siegel models often outperform univariate ones, while the "curvature" factor is rarely helpful for forecasting at a long horizon.

Full text (added May 12, 2019)

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