Statistical modeling of SOFR term structure

TL;DR

This paper introduces a statistical model for the SOFR term structure that accounts for market incompleteness, macroeconomic influences, and rate jumps, facilitating risk management and derivatives pricing.

Contribution

It develops a novel statistical model tailored for the illiquid SOFR derivatives market, incorporating macroeconomic factors and jumps, and enabling easy calibration and large-scale simulations.

Findings

Model effectively captures SOFR rate dynamics.

Eases calibration to market data and macroeconomic views.

Supports risk management and derivatives pricing.

Abstract

SOFR derivatives market remains illiquid and incomplete so it is not amenable to classical risk-neutral term structure models which are based on the assumption of perfect liquidity and completeness. This paper develops a statistical SOFR term structure model that is well-suited for risk management and derivatives pricing within the incomplete markets paradigm. The model incorporates relevant macroeconomic factors that drive central bank policy rates which, in turn, cause jumps often observed in the SOFR rates. The model is easy to calibrate to historical data, current market quotes, and the user's views concerning the future development of the relevant macroeconomic factors. The model is well suited for large-scale simulations often required in risk management, portfolio optimization and indifference pricing of interest rate derivatives.