Pattern Analysis of the Dynamic of Credit Spreads Using Machine Learning Methods

Student: Savushkin Aleksey

Supervisor: Victor A Lapshin

Faculty: International College of Economics and Finance

Educational Programme: Financial Economics (Master)

Year of Graduation: 2019

Forecasting credit spreads of bond is crucially important problem which such subjects of Fixed- income markets like professional investors, trading firms, issuers of bonds face. There are variety papers which try to predict credit spreads with high explanatory power as possible by investigating predictors and forecasting techniques. This paper has a goal to predict credit bond spreads by Data Science method which based on finding patterns in time series, particular I- spread and Z-spread, using additional credit risk information in face of credit ratings these bonds and compare results with best ARIMA model specifications. We find that method which based on Data Science is not inferior too much to Arima forecasting in sense of MSE criteria.

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