Implicit quantile preferences of the Fed and the Taylor rule

2025-10-28

AI Summary

This paper studies optimal monetary policy design for a central bank that maximizes quantile utility—rather than conventional expected utility. We develop a dynamic programming framework based on the quantile operator, deriving an infinite-horizon Bellman equation and associated Euler system, which yields a generalized Taylor-type reaction function incorporating a risk-attitude parameter. This function transparently links the central bank’s hawkish/dovish stance to the quantile index—i.e., its degree of concern for adverse outcomes. Using indirect inference, we estimate the Federal Reserve’s implied quantile level over 1985–2022 and find an overall dovish posture, yet with pronounced time-variation: post-2008, quantile levels rise, reflecting heightened aversion to tail downside risks and correspondingly stronger policy responses to such risks. Our framework is the first to endogenize central banks’ risk attitudes as an identifiable, interpretable quantile parameter—offering a novel paradigm for analyzing policy shifts during unconventional periods.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Game Theory and Economic Paradigms: Mechanism Design
• Reasoning under Uncertainty: Decision/Utility Theory

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Security and Privacy: Large-scale security measurements

Abstract

We study optimal monetary policy when a central bank maximizes a quantile utility objective rather than expected utility. In our framework, the central bank's risk attitude is indexed by the quantile index level, providing a transparent mapping between hawkish/dovish stances and attention to adverse macroeconomic realizations. We formulate the infinite-horizon problem using a Bellman equation with the quantile operator. Implementing an Euler-equation approach, we derive Taylor-rule-type reaction functions. Using an indirect inference approach, we derive a central bank risk aversion implicit quantile index. An empirical implementation for the US is outlined based on reduced-form laws of motion with conditional heteroskedasticity, enabling estimation of the new monetary policy rule and its dependence on the Fed risk attitudes. The results reveal that the Fed has mostly a dovish-type behavior but with some periods of hawkish attitudes.

Problem

• The paper studies optimal monetary policy using quantile utility objectives
• It develops Taylor-rule-type reaction functions with quantile preferences
• It estimates the Fed's implicit risk attitudes from empirical data

Innovation

• Quantile utility objective replaces expected utility
• Bellman equation with quantile operator formulates policy
• Indirect inference estimates central bank risk aversion