Measuring Convertible Bond Convexity with Gamma Factors
Summary
The document presents two ways to estimate convexity in Chinese convertible bonds. The Black–Scholes approach substitutes implied volatility for the underlying stock’s volatility, calibrating the model price to the observed bond price before using its analytical gamma. The cross-sectional difference approach instead estimates the relationship between conversion value and conversion premium across bonds, then derives a convexity measure linking bond and stock returns. The authors describe the latter measure as producing smoother curves across individual bonds and time.
A monthly strategy selecting the 30 bonds with the highest Black–Scholes gamma is reported to have earned around 10% annualized since 2020, with relatively even yearly returns. The account says both methods were profitable and that gamma selections often differed from low-price and double-low portfolios. These are historical backtest claims, not guarantees. The reported drawdowns during credit-risk concerns illustrate that a stock-option-based convexity signal may be less effective when bond credit factors dominate. The document also flags liquidity, clause-related, earnings, and macroeconomic risks.
Key ideas
- Convertible bond convexity can amplify gains when the underlying stock rises and cushion losses when it falls.
- The Black–Scholes method uses implied volatility and derives gamma after matching modeled and observed bond prices.
- The cross-sectional method estimates convexity from the relationship between conversion value and conversion premium.
- A monthly portfolio of the 30 highest-gamma bonds is reported to have earned around 10% annualized since 2020.
- Credit concerns can weaken the selection signal, and historical results may not persist.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.