Predicting Credit Default Swap (CDS) Returns with Machine Learning
DOI:
https://doi.org/10.32473/ufjur.v20i1.106275Abstract
Credit Default Swaps (“CDS”) are contracts that insure one party against default in an underlying financial instrument, usually a bond. Therefore, the price of CDS reflects the perceived risk of default in an underlying financial instrument. This project applied Support Vector Machines (“SVMs”) to the prediction of CDS price changes for several individual companies across time. Previous research applying SVMs to predicting CDS prices used historical CDS prices as model inputs. This project proposed and applied several new input variables. Tests over a period of several years, across a group of CDS time-series, indicate that a combined model which uses the new input variables in addition to historical CDS price changes outperforms models that only use historical CDS price changes.
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References
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