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

Abstract

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.

Pricing Chinese Convertible Bonds with Learning-Based Monte Carlo Simulation Model

“…They found that the support vector regression model effectively completed the prediction task. While numerous scholars have embraced the wave of machine learning models, there has been limited work carried out on integrating machine learning techniques with traditional models [34]. Therefore, we try to bridge the gap by using machine learning models to replace the regression analysis of the standard LSM.…”

“…In China, banks typically dominate the convertible bond market in terms of the largest issuance volume, and convertible bonds issued by banks tend to carry higher credit ratings [34]. Therefore, our sample primarily chooses existing convertible bonds issued by China Everbright Bank (CEB) as of 1 January 2023.…”