Implicit quantile preferences of the Fed and the Taylor rule

TL;DR

This paper explores how a central bank's risk preferences, modeled through quantile utility, influence monetary policy rules, revealing that the Fed's stance varies between dovish and hawkish over time.

Contribution

It introduces a novel framework linking quantile-based risk attitudes to monetary policy, deriving new Taylor-type reaction functions and empirically estimating Fed risk preferences.

Findings

Fed exhibits mostly dovish behavior with occasional hawkish periods

Quantile-based modeling captures the risk attitude spectrum of the Fed

New monetary policy rule depends on the implicit quantile index

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.