Pricing bonds with optional sinking feature using Markov Decision Processes

TL;DR

This paper introduces a fast and accurate method for pricing bonds with optional sinking features by formulating the problem as a Markov Decision Process within a complex credit-equity model.

Contribution

It develops a novel pricing algorithm that handles the randomness of cash flows due to sinking options using Markov Decision Processes in a sophisticated credit model.

Findings

The method is both accurate and computationally efficient.

Demonstrated effectiveness in a 1.5-factor credit-equity model.

Applicable to bonds with optional sinking features in complex credit environments.

Abstract

An efficient method to price bonds with optional sinking feature is presented. Such instruments equip their issuer with the option (but not the obligation) to redeem parts of the notional prior to maturity, therefore the future cash flows are random. In a one-factor model for the issuer's default intensity we show that the pricing algorithm can be formulated as a Markov Decision Process, which is both accurate and quick. The method is demonstrated using a 1.5-factor credit-equity model which defines the default intensity in a reciprocal relationship to the issuer's stock price process, termed jump-to-default extended model with constant elasticity of variance (JDCEV).