Paper: arXiv 2508.02691

Authors: Teemu Pennanen, Waleed Taoum

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.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 4.0/10
  • Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper presents a sophisticated statistical model with stochastic differential equations and macroeconomic factor integration, which requires advanced mathematical understanding. However, it lacks implementation details like code or backtests, focusing instead on theoretical calibration and simulation setups without empirical validation on real market data.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Develop statistical SOFR term structure model<br>for incomplete markets"] --> B{"Key Methodology"}
  B --> C["Incorporate macroeconomic factors<br>driving policy rates & jumps"]
  B --> D["Statistical modeling within<br>incomplete markets paradigm"]
  C --> E["Data & Inputs<br>Historical data, market quotes,<br>macroeconomic views"]
  D --> E
  E --> F["Computational Process<br>Calibration &<br>large-scale simulations"]
  F --> G["Key Outcomes<br>Risk management & derivatives pricing<br>for illiquid SOFR market"]