Finding an efficient machine learning predictor for lesser liquid credit default swaps in equity markets

Iranian Journal of Numerical Analysis and Optimization

مقاله 14، دوره 13، شماره 1 - شماره پیاپی 24، خرداد 2023، صفحه 19-37   

نوع مقاله: Research Article

شناسه دیجیتال (DOI): 10.22067/ijnao.2022.73453.1073

نویسنده

F. Soleymani^*

Department of Mathematics, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan 45137-66731, Iran.

چکیده

To solve challenges occurred in the existence of large sets of data, recent improvements of machine learning furnish promising results. Here to pro-pose a tool for predicting lesser liquid credit default swap (CDS) rates in the presence of CDS spreads over a large period of time, we investigate different machine learning techniques and employ several measures such as the root mean square relative error to derive the best technique, which is useful for this type of prediction in finance. It is shown that the nearest neighbor is not only efficient in terms of accuracy but also desirable with respect to the elapsed time for running and deploying on unseen data.

کلیدواژه‌ها

Credit default swap (CDS)؛ machine learning؛ prediction؛ liquidity؛ spread

مراجع

[1] R. Adhikari, Foundations of computational finance, The Mathematica J., 22 (2020), 1-59.
[2] R. Adhikari, Selected financial applications, The Mathematica J., 23 (2021), 1-33.
[3] A. Antonov, Variable importance determination by classifiers implemen-tation in Mathematica, Lecture Notes, Florida, 2015.
[4] G.O. Aragon, L. Li, J. Qian, The use of credit default swaps by bond mutual funds: Liquidity provision and counterparty risk, J. Finan. Econ., 131 (2019), 168-185.
[5] J. Bao, S. Franco, Y.-H. He, E. Hirst, G. Musiker, Y. Xiao, Quiver muta-tions, Seiberg duality, and machine learning, Phys. Rev. D., 102 (2020), Art. ID: 086013.
[6] T.R. Bielecki, M.R. Rutkowski, Credit Risk: Modeling, Valuation and Hedging, Springer, New York, 2004.
[7] C.M. Bishop, Pattern Recognition and Machine Learning, Springer, New York, NY, 2006.
[8] D. Brigo, N. Pede, A. Petrelli, Multi currency credit default swaps, Int. J. Theor. Appl. Finan., 22 (2019), 1950018.
[9] S. Carbo-Valverde, P. Cuadros-Solas, F. Rodríguez-Fernández, A machine learning approach to the digitalization of bank customers: Evidence from random and causal forests, Plos One, 15 (2020), Art. ID: e0240362.
[10] G.H. Chen, D. Shah, Explaining the success of nearest neighbor methods in prediction, Found. Trends Mach. Learn., 10 (2018), 337-588.
[11] K. Cortez, M. Rodríguez-García, S. Mongrut, Exchange market liquidity prediction with the K-nearest neighbor approach: Crypto vs. fiat curren-cies, Mathematics, 9 (2021), Art. ID: 56.
[12] P.P. da Silva, I. Vieira, C. Vieira, M&A operations: Further evidence of informed trading in the CDS market, J. Multi. Fin. Manag. 32-33 (2015), 116-130.
[13] M.L. De Prado, Advances in Financial Machine Learning, Wiley, New Jersey, 2018.
[14] W. E, Machine learning and computational mathematics, Commun. Comput. Phys., 28 (2020), 1639-1670.

[15] F.J. Fabozzi, S.M. Focardi, P.N. Kolm, Trends in Quantitative Finance, The Research Foundation of CFA Institute, USA, 2006.
[16] G. Gan, C. Ma, J. Wu, Data Clustering: Theory, Algorithms, and Ap-plications, SIAM, Philadelphia, 2007.
[17] N.L. Georgakopoulos, Illustrating Finance Policy with Mathematica, Springer International Publishing, Cham, Switzerland, 2018.
[18] D. Guégan, N. Huck, On the use of nearest neighbors in finance, Finance, 26 (2005), 67-86.
[19] B.M. Henrique, V.A. Sobreiro, H. Kimura, Literature review: Machine learning techniques applied to financial market prediction, Expert Sys. Appl., 124 (2019), 226-251.
[20] I. Hlivka, Credit default swap valuation, Lecture Notes, London, Quant Solutions Group, (2014), 1-2.
[21] I. Hlivka, Predictive analytics in finance: Patterns detection for outcome prediction, Lecture Notes, London, Quant Solutions Group, (2015), 1-14.
[22] A. Itkin, A. Lipton, D. Muravey, Generalized Integral Transforms in Mathematical Finance, World Scientific Publishing, Toh Tuck, Singapore, 2021.
[23] A. Itkin, V. Shcherbakov, A. Veygman, New model for pricing quanto credit default swaps, Int. J. Theor. Appl. Fin., 22 (2019), Art. ID: 1950003.
[24] M. Kuhn, K. Johnson, Applied Predictive Modeling, 1st ed., Springer Science + Business Media, New York, 2013.
[25] V. Kumar, M.L. Garg, Predictive analytics: A review of trends and techniques, Int. J. Comput. Appl., 182 (2018), 31-37.
[26] R. Mohamadinejad, A. Neisy, J. Biazar, ADI method of credit spread op-tion pricing based on jump-diffusion model, Iran. J. Numer. Anal. Optim., 11 (2021), 195-210.
[27] A. Mosavi, Y. Faghan, P. Ghamisi, P. Duan, S.F. Ardabili, E. Salwana, S.S. Band, Comprehensive review of deep reinforcement learning methods and applications in economics, Mathematics, 8 (2020), Art. ID. 1640.
[28] H. Ni, X. Dong, J. Zheng, G. Yu, An Introduction to Machine Learning in Quantitative Finance, World Scientific Publishing Europe Ltd., London, 2021.
[29] L. Sandoval Junior, Correlation of financial markets in times of crisis, Phys. A: Stat. Mech. Appl., 391 (2012), 187-208.

[30] J. Sirignano, A. Sadhwani, K. Giesecke, Deep learning for mortgage risk, J. Finan. Econometrics, 19 (2021), 313-368.
[31] Y. Son, H. Byun, J. Lee, Nonparametric machine learning models for predicting the credit default swaps: An empirical study, Expert Sys. Appl., 58 (2016), 210-220.