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Valuation Model of Chinese Convertible Bonds Based on Monte Carlo Simulation

Yu Liu

TL;DR

The paper presents a Monte Carlo pricing framework for Chinese convertible bonds (CCBs) that leverages Least Squares Monte Carlo and dynamic programming to estimate continuation values under risk-neutral stock dynamics, enabling backward induction to price CCBs. It explicitly models downward adjustments as a probabilistic event triggering the put, integrating this with standard call/put/conversion decisions. Empirical validation on real market data shows that multi-regression improvements reduce pricing errors (RMSE down to about 2.96%) and that a trading strategy based on undervalued CCBs using the Least Squares factor outperforms the Double Low benchmark in backtests. The work demonstrates robust pricing of complex CCB features and provides a practical, scalable approach for both pricing and strategy development in markets with downward adjustment clauses.

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.

Valuation Model of Chinese Convertible Bonds Based on Monte Carlo Simulation

TL;DR

The paper presents a Monte Carlo pricing framework for Chinese convertible bonds (CCBs) that leverages Least Squares Monte Carlo and dynamic programming to estimate continuation values under risk-neutral stock dynamics, enabling backward induction to price CCBs. It explicitly models downward adjustments as a probabilistic event triggering the put, integrating this with standard call/put/conversion decisions. Empirical validation on real market data shows that multi-regression improvements reduce pricing errors (RMSE down to about 2.96%) and that a trading strategy based on undervalued CCBs using the Least Squares factor outperforms the Double Low benchmark in backtests. The work demonstrates robust pricing of complex CCB features and provides a practical, scalable approach for both pricing and strategy development in markets with downward adjustment clauses.

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.
Paper Structure (12 sections, 22 equations, 5 figures, 7 tables)

This paper contains 12 sections, 22 equations, 5 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Monte Carlo Simulations for CCB Pricing
  3. The Problem Setup
  4. Least Squares Regression
  5. Framework of Least Square regression and dynamic programming
  6. Existence and uniqueness of the solution for Least Squares regression at each time t
  7. Improvement for the Least Squares regression framework
  8. Empirical Studies on CCB Pricing
  9. Data Description
  10. Performance of the Pricing Algorithm on 10 CCBs
  11. Trading Strategy by the Least Squares Factor
  12. Discussion

Figures (5)

  • Figure 1: Simulation of stock price for Daqin CB.
  • Figure 2: The performance of convertible bond prices can be roughly classified into three categories based on the underlying stock price, "Bond-like", "Balanced" and "Equity-like".
  • Figure 3: Model price vs market price for various CCBs.
  • Figure 4: Model price vs market price for various CCBs with "Multiple Regression".
  • Figure 5: Least Squares factor backtesting for CCBs.