Operations Research and Financial Engineering-Inspired Thesis: Hedging Interest Rate Risks in the Reverse Mortgage Market

Abstract

This thesis investigates interest rate hedging strategies for mitigating financial risks in reverse mortgage portfolios. Reverse mortgages are impacted by a complex interplay of interest rate volatility, borrower longevity, and housing price fluctuations. Using a two-pronged approach combining theoretical Monte Carlo simulations and empirical analysis of Ginnie Mae HECM loan data, the study evaluate the effectiveness of various hedging instruments, with particular emphasis on interest rate swaps. The simulation model applies a NNEG (No-Negative-Equity Guarantee) framework to conduct a sensitivity analysis on reverse mortgage risk factors. While theoretical models suggests an optimized barbell hedging strategy could have high hedge effectiveness (87-93%), Ginnie Mae data shows a dramatically lower effectiveness. This divergence between theory and practice highlights how diversified hedging approaches with portfolio segmentation might be more effective than traditional duration-matching techniques. The findings contribute to a realistic and theoretical understanding of reverse mortgage risk management, with implications to how lenders can minimize the burden of their risk in a increasingly popular retirement financing market.