CDS Primlerinin Farklı Makine Öğrenme Algoritmalarıyla Kestirimi ve Belirleyenleri Üzerine Bir Çalışma

Öz

Kredi risklerinin doğru yönetilmesi kapsamında yaygın olarak kullanılan Kredi Temerrüt Swaplarında (CDS) görülen oransal artış, ülkenin kredibilitesini azaltmakta ve borçlanma maliyetini yükseltmektedir. Bu bağlamda çalışmanın ilk amacı piyasa finansal durumunun resmini çizmesi ve ülkeye yapılacak yatırımların güvenilirliğini gösteren CDS’in belirleyenlerini ortaya koymaktır. Çalışmanın diğer bir amacı ise, Türkiye’de 2001:01-2023:12 dönemlerinde CDS risk primini öngörebilen en isabetli makine öğrenme algoritmasının belirlenmesidir. Bu bağlamda seçilen özniteliklerin CDS kestirimindeki etkileri denetimli öğrenme ve toplulaştırılmış öğrenme algoritmaları kullanılarak sınanmıştır. Analiz bulguları; USD/TRY (%61), DTA (%14) ve döviz rezervlerinin (%10) CDS kestirimi üzerindeki etkilerine işaret ederken, OMYH kriterine göre en iyi sonuçlar Gradient Boost algoritması (GB) (%74,38) ile gerçekleşmiştir.

Anahtar Kelimeler

• CDS
• Makine Öğrenme Algoritmaları
• Denetimli Öğrenme
• Karar Ağacı
• Topluluk Öğrenme Algoritmaları

A Study on Prediction and Determinants of CDS Premiums Using Different Machine Learning Algorithms

Öz

The proportional increase in Credit Default Swaps (CDS), which are widely used as part of the proper management of credit risks, reduces the country's creditworthiness and increases the cost of borrowing. In this context, the first purpose of the study is to draw a picture of the market financial situation and to reveal the determinants of CDS that indicate the reliability of investments to be made in the country. Another purpose of the study is to determine the most accurate machine learning algorithm that can predict the CDS risk premium in the period 2001:01-2023:12 in Turkey. In this context, the effects of the selected attributes on CDS prediction were tested using supervised learning and ensembled learning algorithms. While the analysis findings indicate the effects of USD/TRY (61%), DTA (14%) and foreign exchange reserves (10%) on CDS predicting, the best results according to the OMYH criterion were realized with Gradient Boost algorithm (GB) by (74.38%.).

Anahtar Kelimeler

• CDS
• Machine Learning Algorithms
• Supervised Learning
• Decision Tree
• Community Learning Algorithms

Kaynakça

1. Aizenman, J., Hutchison, M., & Jinjarak, Y. (2013). What is the risk of European sovereign debt defaults? Fiscal space, CDS spreads and market pricing of risk. Journal of International Money and Finance, 34, 37–59.
2. Alpaydın, E. (2017). Yapay öğrenme (3. baskı). İstanbul: Boğaziçi Üniversitesi Yayınevi.
3. Black, F., & Cox, J. C. (1976). Valuing corporate securities: Some effects of bond indenture provisions. The Journal of Finance, 31(2), 351–367.
4. Black, F., & Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of Political Economy, 81, 637–654.
5. Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.
6. Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Wadsworth.
7. Chen, W., & He, X. (2017). Pricing credit default swaps under a multi-scale stochastic volatility model. Physica A: Statistical Mechanics and its Applications, 468, 425–433. https://doi.org/10.1016/j.physa.2016.10.082 de Myttenaere, A., Golden, B., Le Grand, B., & Rossi, F. (2016). Mean absolute percentage error for regression models. Neurocomputing, 192, 38–48. https://doi.org/10.1016/j.neucom.2016.03.114
8. Dong, X., Yu, Z., Cao, W., Shi, Y., & Ma, Q. (2020). A survey on ensemble learning. Frontiers of Computer Science, 14, 241–258.

9. Duffie, D. (1999). Credit swap valuation. Financial Analysts Journal, 55(1), 73–87.
10. Finger, C., Finkelstein, V., Lardy, J. P., Pan, G., Ta, T., & Tierney, J. (2002). CreditGrades technical document. RiskMetrics Group.
11. Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.
12. Galil, K., Shapir, O. M., Amiram, D., & Ben-Zion, U. (2014). The determinants of CDS spreads. Journal of Banking & Finance, 41, 271–282.
13. Géron, A. (2019). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow (2nd ed.). O'Reilly Media.
14. Gupta, K. K., Kalita, K., Ghadai, R. K., Ramachandran, M., & Gao, X. Z. (2021). Machine learning-based predictive modelling of biodiesel production: A comparative perspective. Energies, 14(4), 1122.
15. Han, J., Kamber, M., & Pei, J. (2011). Data mining: Concepts and techniques (3rd ed.). Morgan Kaufmann.
16. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer.
17. He, X., & Chen, W. (2014). The pricing of credit default swaps under a generalized mixed fractional Brownian motion. Physica A: Statistical Mechanics and Its Applications, 404, 26–33.
18. Hull, J., & White, A. (2000). Valuing credit default swaps I: No counterparty default risk. Journal of Derivatives, 8, 29–40.
19. Hull, J., & White, A. (2001). Valuing credit default swaps II: Modeling default correlations. Journal of Derivatives, 8, 12–22.
20. Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679–688. https://doi.org/10.1016/j.ijforecast.2006.03.001 James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning: With applications in R. Springer.
21. Jarrow, R. A., & Turnbull, S. M. (1995). Pricing derivatives on financial securities subject to credit risk. The Journal of Finance, 50(1), 53–85.
22. Kajurova, V. (2015). The determinants of CDS spreads: The case of UK companies. Procedia Economics and Finance, 23, 1302–1307.
23. Karabıyık, B. K. (2021). ANFIS metodu ile CDS primi tahminlemesi: Türkiye örneği. Uluslararası Yönetim İktisat ve İşletme Dergisi, 17(4), 1175–1197.
24. Kartal, M. T., Depren, S. K., & Depren, Ö. (2022). Sovereign credit default swap (CDS) spreads changes in various economic conjunctures: Evidence from Turkey by machine learning algorithms. Journal of Management and Economics Research, 20(1), 354–374.
25. Kim, W. J., Jung, G., & Choi, S.-Y. (2020). Forecasting CDS term structure based on Nelson–Siegel model and machine learning. Complexity, 2020, Article 2518283. https://doi.org/10.1155/2020/2518283
26. Kunt, A. S., & Taş, O. (2009). Kredi temerrüt swapları ve Türkiye'nin CDS priminin tahmin edilmesine yönelik bir uygulama. İTÜ Dergisi/b, 5(1).
27. Lin, S. Y., Liu, D. R., & Huang, H. P. (2022). Credit default swap prediction based on generative adversarial networks. Data Technologies and Applications, 56(5), 720–740.
28. Litterman, R., & Iben, T. (1991). Corporate bond valuation and the term structure of interest rates. Journal of Portfolio Management, Spring, 52–64.
29. Luo, C., Wu, D., & Wu, D. (2017). A deep learning approach for credit scoring using credit default swaps. Engineering Applications of Artificial Intelligence, 65, 465–470.
30. Mahesh, B. (2020). Machine learning algorithms: A review. International Journal of Science and Research, 9(1), 381–386.
31. Mao, W., Zhu, H., Wu, H., Lu, Y., & Wang, H. (2023). Forecasting and trading credit default swap indices using a deep learning model integrating Merton and LSTMs. Expert Systems with Applications, 213, 119012. Merton, R. C. (1974). On the pricing of corporate debt: The risk structure of interest rates. The Journal of Finance, 29(2), 449–470.
32. Natekin, A., & Knoll, A. (2013). Gradient boosting machines: A tutorial. Frontiers in Neurorobotics, 7, 21. Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106.
33. Sagi, O., & Rokach, L. (2018). Ensemble learning: A survey. WIREs Data Mining and Knowledge Discovery, 8(4), e1249.
34. Schapire, R. E. (2013). Explaining AdaBoost. In B. Schölkopf, Z. Luo, & V. Vovk (Eds.), Empirical inference (pp. 37–52). Springer.
35. Skinner, F. S., & Townend, T. G. (2002). An empirical analysis of credit default swaps. International Review of Financial Analysis, 11(3), 297–309. https://doi.org/10.1016/S1057-5219(02)00077-7
36. Son, Y., Byun, H., & Lee, J. (2016). Nonparametric machine learning models for predicting the credit default swaps: An empirical study. Expert Systems with Applications, 58, 210–220. Tufaner, M. B. (2021). Kredi risk primi, döviz kurları ve bazı emtiaların BIST-100 performansına etkisi. Finansal Araştırmalar ve Çalışmalar Dergisi, 13(25), 798–810.
37. Varlık, S., & Varlık, N. (2017). Türkiye’nin CDS priminin oynaklığı. Finans Politik ve Ekonomik Yorumlar, (632), 9–17. Yu, Y. (2020). The application of machine learning algorithms in credit card default prediction. In 2020 International Conference on Computing and Data Science (CDS) (pp. 212–218). IEEE.
38. Zhang, T. Y. (2018). Predicting credit default swap (CDS) returns with machine learning. SSRN. https://ssrn.com/abstract=3152143
39. Zhang, Y., & Haghani, A. (2015). A gradient boosting method to improve travel time prediction. Transportation Research Part C: Emerging Technologies, 58, 308–324.