Bayesian Methods for Vasicek Model Calibration

Student: Emelyanov Andrey

Supervisor: Victor A Lapshin

Educational Programme: Financial Technology and Data Analysis (Master)

Final Grade: 7

Year of Graduation: 2021

This work explores the Bayesian methods, in particular the Gibbs sampler and Metropolis-Hastings algorithms for calibrating the Vasicek model to predict the yield curve. The data on the 6-month short-term rate and the yield on Russian government bonds were used. To show the value of using Bayesian methods, we also provide maximum likelihood calibration to compare the results. To assess the quality of the algorithm, flf (firm loss function) is used. The work also analyzes the model errors and patterns in which the model shows good and bad results.

Full text (added May 26, 2021)

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