# A Monte Carlo-Based Framework for Two-Stage Stochastic Programming: Application to Bond Portfolio Optimization

## TL;DR

This paper introduces a Monte Carlo method to optimize bond portfolios under uncertain market conditions, ensuring cost efficiency and liability fulfillment.

## Contribution

A novel Monte Carlo-based framework for two-stage stochastic programming applied to bond portfolio optimization.

## Key findings

- The approach successfully minimizes bond portfolio costs under random market conditions.
- The method meets liabilities effectively, providing robust portfolio solutions.
- The algorithm determines the required number of scenarios to convert stochastic problems into deterministic ones.

## Abstract

This paper presents a Monte Carlo simulation-based approach for solving stochastic two-stage bond portfolio optimization problems. The main objective is to optimize the cost of the bond portfolio while making decisions on bond purchases, holdings, and sales under random market conditions such as interest rate fluctuations and liabilities. The proposed algorithm identifies the number of randomly generated scenarios required to convert the stochastic problem into a deterministic one, subsequently solving it as a Mixed-Integer Linear Program. The practical relevance of this research is shown through an application of the proposed method to a real-world bond market. The results indicate that the proposed approach successfully minimizes costs and meets liabilities, providing a robust solution for bond portfolio optimization.