Comparing Prediction Power of Artificial Neural Networks Compound Models in Predicting Credit Default Swap Prices through Black– Scholes– Merton Model

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Beytollahi Asghar; Zeinali Hadis

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Interdisciplinary Journal of Management Studies Year:2020 | Volume:13 | Issue:1 Page(s): 69-93

Download Full-Text

View:

226

Download:

Cites:

Information Journal Paper

Title

Comparing Prediction Power of Artificial Neural Networks Compound Models in Predicting Credit Default Swap Prices through Black– Scholes– Merton Model

Author(s)

Beytollahi Asghar | Zeinali Hadis | Issue Writer Certificate

Keywords

Derivative Financial Instruments

ANFIS

NNARX

AdaBoost

Support Vector Machine Regression

Abstract

Default risk is one of the most important types of risks, and credit default swap (CDS) is one of the most effective financial instruments to cover such risks. The lack of these instruments may reduce investment attraction, particularly for international investors, and impose potential losses on the economy of the countries lacking such financial instruments, among them, Iran. After the 2007 financial crisis, the importance of CDS has increasingly augmented because theoretically and practically, this instrument could significantly prevent catastrophes such as the mentioned crisis. The present study seeks to predict the price of CDS contracts with the Merton model as well as the compound neural network models including ANFIS, NNARX, AdaBoost, and SVM regression, and compare the predictive power of these algorithms which are among the most prestigious, intelligent models in finance. The research statistical population includes the A-rated North American and European companies which are known as the reference entities for credit default swaps. Data were collected from the Bloomberg Terminal for an eight-year period from 2008 to 2015. Contracts of 125 companies were selected as the statistical sample. The results reveal that the average predictive power of the NNARX is higher than that of other algorithms under scrutiny.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

APA: Copy

Beytollahi, Asghar, & Zeinali, Hadis. (2020). Comparing Prediction Power of Artificial Neural Networks Compound Models in Predicting Credit Default Swap Prices through Black– Scholes– Merton Model. IRANIAN JOURNAL OF MANAGEMENT STUDIES, 13(1), 69-93. SID. https://sid.ir/paper/779847/en

Vancouver: Copy

Beytollahi Asghar, Zeinali Hadis. Comparing Prediction Power of Artificial Neural Networks Compound Models in Predicting Credit Default Swap Prices through Black– Scholes– Merton Model. IRANIAN JOURNAL OF MANAGEMENT STUDIES[Internet]. 2020;13(1):69-93. Available from: https://sid.ir/paper/779847/en

IEEE: Copy

Asghar Beytollahi, and Hadis Zeinali, “Comparing Prediction Power of Artificial Neural Networks Compound Models in Predicting Credit Default Swap Prices through Black– Scholes– Merton Model,” IRANIAN JOURNAL OF MANAGEMENT STUDIES, vol. 13, no. 1, pp. 69–93, 2020, [Online]. Available: https://sid.ir/paper/779847/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops