Research on Pricing Methods of Convertible Bonds Based on Deep Learning GAN Models

• Published in: International journal of financial studies Vol. 11; no. 4; p. 145
• Main Authors: Ren, Gui, Meng, Tao
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2023
• Subjects: Analysis; Computational linguistics; convertible bond pricing; Convertible bonds; Credit risk; Deep learning; Default; GAN; Interest rates; Language processing; least square Monte Carlo Simulation; LSTM; Machine learning; Methods; Monte Carlo simulation; Natural language interfaces; Neural networks; Numerical analysis; Partial differential equations; Valuation; China
• ISSN: 2227-7072; 2227-7072
• DOI: 10.3390/ijfs11040145

This paper proposes two data-driven models (including LSTM pricing model, WGAN pricing model) and an improved model of LSM based on GAN to analyze the pricing of convertible bonds. In addition, the LSM model with higher precision in traditional pricing model is selected for comparative study with other pricing models. It is found that the traditional LSM pricing model has a large error in the first-day pricing, and the pricing function needs to be further improved. Among the four pricing models, LSTM pricing model and WGAN pricing model have the best pricing effect. The WGAN pricing model is better than the LSTM pricing model (0.21%), and the LSM improved model (1.17%) is better than the traditional LSM model (2.26%). Applying the generative deep learning model GAN to the pricing of convertible bonds can circumvent the harsh preconditions of assumptions, and significantly improve the pricing effect of the traditional model. The scope of application of each model is different. Therefore, this paper proves the feasibility of the GAN model applied to the pricing of convertible bonds, and enriches the pricing function of derivatives in the financial field.