Statistical modeling of SOFR term structure

Overview

The paper introduces a statistical model for the SOFR term structure, tailored for risk management and derivatives pricing in markets characterized by illiquidity and incompleteness. This model is particularly relevant for handling the jumps in SOFR rates driven by macroeconomic factors.

Key Insights

• Model Development: The model is designed for incomplete markets, incorporating macroeconomic factors that influence central bank policy rates and cause observable jumps in SOFR rates.
• Calibration: It can be calibrated using historical data, current market quotes, and user projections of macroeconomic factors.
• Applications: Suitable for large-scale simulations necessary in risk management, portfolio optimization, and indifference pricing of interest rate derivatives.

Why It Matters

This model provides a practical tool for financial services, particularly in risk management and derivatives pricing, where traditional models fall short due to market illiquidity.

Actionable Implications

• Implement the model for better risk management in derivatives trading.
• Use the model for portfolio optimization in environments with incomplete market data.
• Apply the model to simulate various macroeconomic scenarios affecting SOFR rates.