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Convertible bonds, as a vital means for listed companies to raise funds, are favored by both listed companies and institutional investors due to their hybrid features of equity, debt, and options. The U.S. market, with its large scale, significantly supports the rapid growth of numerous high-tech enterprises. In contrast, China's…
Convertible bonds, as a vital means for listed companies to raise funds, are favored by both listed companies and institutional investors due to their hybrid features of equity, debt, and options. The U.S. market, with its large scale, significantly supports the rapid growth of numerous high-tech enterprises. In contrast, China's convertible bond market started later and lags behind in terms of issuer numbers and issuance scale. Since the issuance regulations on February 17, 2017, the convertible bond market in China has seen substantial development opportunities, with increased enthusiasm from listed companies and various investors, leading to significant growth in market issuance and trading volume.This article employs Monte Carlo simulation to empirically study the theoretical pricing of convertible bonds, aiming to identify factors influencing differences between theoretical and market prices. Analyzing classic terms such as conversion, redemption, put, and call provisions, the study establishes a foundation for subsequent convertible bond pricing research.
Following the consideration of various terms, the article uses the Monte Carlo simulation method to attempt pricing the asset prices of convertible bonds. To enhance computational efficiency, the core simulation process undergoes encapsulation and optimization. The analysis of theoretical prices reveals minimal overall fluctuation in pricing errors, showing a negative error rate before June 2021, indicating market prices
slightly below theoretical prices, and afterward, market prices slightly above theoretical prices.
Finally, the article analyzes the reasons behind pricing errors, examining 13 variables' potential impact through regression analysis, finding significant effects from 11 variables.The article attempts to provide a reasonable interpretation of the logic behind the impact of variables on pricing errors.