Citation
Predicting Credit Default Swap (Cds) Returns With Machine Learning

Material Information

Title:
Predicting Credit Default Swap (Cds) Returns With Machine Learning
Series Title:
19th Annual Undergraduate Research Symposium
Creator:
Zhang, Terrence
Language:
English
Physical Description:
Undetermined

Subjects

Subjects / Keywords:
Center for Undergraduate Research
Center for Undergraduate Research
Genre:
Conference papers and proceedings
Poster

Notes

Abstract:
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. ( en )
General Note:
Research authors: Terrence (Yidong) Zhang - University of Florida
General Note:
University Scholars Program
General Note:
Faculty Mentor: 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. - Center for Undergraduate Research, University Scholars Program

Record Information

Source Institution:
University of Florida
Rights Management:
Copyright Terrence Zhang. Permission granted to University of Florida to digitize and display this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.

UFDC Membership

Aggregations:
University of Florida Institutional Repository

Downloads

This item is only available as the following downloads:


Full Text

PAGE 1

Machine Learning and CDS Prices Researcher : Terrence (Yidong) Zhang Faculty Mentor : Mahendrarajah Nimalendran Ph.D. What is a Support Vector Machine? ( SVM) What is a Credit Default Swap? (CDS)? The Experiment Non linear Predictions Intensive Algorithm Generalizable Results Financial Contract Insurance Default Risk New Input Variables Stock Prices Sector Index Prices Implied Volatilities Broad CDS Indice s Dataset Better Results