Valuation Model of Chinese Convertible Bonds Based on Monte Carlo Simulation

TL;DR

This paper presents a Monte Carlo simulation-based valuation model for Chinese convertible bonds, incorporating dynamic programming and probabilistic downward adjustment clauses, and demonstrates its effectiveness through an undervalued strategy simulation.

Contribution

It introduces a novel Monte Carlo and dynamic programming approach for pricing Chinese convertible bonds, including modeling probabilistic downward adjustment clauses.

Findings

The undervalued strategy outperforms the benchmark double-low strategy.

The model effectively incorporates downward adjustment clauses as probabilistic events.

Simulation results validate the pricing accuracy and strategy effectiveness.

Abstract

We tackle the problem of pricing Chinese convertible bonds(CCBs) using Monte Carlo simulation and dynamic programming. At each exercise time, we use the state variables of the underlying stock to regress the continuation value, and apply standard backward induction to get the coefficients from the current time to time zero. This process ultimately determines the CCB price. We then apply this pricing method in simulations and evaluate an underpriced strategy: taking long positions in the 10 most undervalued CCBs and rebalancing daily. The results show that this strategy significantly outperforms the benchmark double-low strategy. In practice, CCB issuers often use a downward adjustment clause to prevent financial distress when a put provision is triggered. Therefore, we model the downward adjustment clause as a probabilistic event that triggers the put provision, thereby integrating it with the put provision in a straightforward manner.