Monetary Policy as a Driver of Financial Markets

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The actions and communications of central banks were viewed by many as important tools to contain the economic fallout from the financial crisis of 2008-09 and the Covid-19 pandemic. Even though financial markets are clearly an important source of information for central bankers, the analyses of financial markets data on the one hand and inflation and unemployment on the other usually proceed separately, limiting their joint potential. This project will develop a new fully structural macro-finance model and solution method integrating a small-scale standard New Keynesian model of monetary policy with a standard model of risk discounts in financial markets based on habit formation preferences. The computational platform developed by this project will allow a broad set of researchers to analyze finance habit preferences. By using this platform, researchers in academia and policy institutions will be able to develop their own models of the macroeconomic drivers of risks in financial markets, ultimately leading to a better understanding of how these risks are impacted by forces external to policy and policy decisions. The PI will develop the first proof of concept model that integrates a standard New Keynesian model of monetary policy (e.g. Gali (2008)) with highly non-linear finance preferences and a corresponding solution technique. While both the finance and the macroeconomic sides of this proposed model have been successful in their independent literatures, the task of combining them has proven challenging. One shortcoming of standard New Keynesian models is that they assume constant risk aversion and therefore imply overly smooth prices for stocks and bonds. On the other hand, a long-standing insight from finance research is that investors discount risky stocks and bonds, and that these risk discounts can be large and volatile (see e.g. Cochrane (2017) for a review). A successful approach in finance research has been based on households’ habit formation, which implies that investors require steeper risk discounts as consumption falls towards a slowly moving habit level, as would be the case after a sequence of adverse economic shocks (Campbell and Cochrane (1999), Wachter (2005)). A key technical contribution of this research will be to specify a macroeconomic model and solution method preserving the full nonlinearity of Campbell, Pflueger and Viceira (2020)’s finance habit preferences, which is needed to simultaneously model volatile stock returns and a monetary policy rule for interest rates. The research team will then use this model to advance our economic understanding of three empirical high-frequency facts in financial markets: (a) Why do stock markets respond so strongly to announcements by the Federal Reserve (Bernanke and Kuttner (2005))? This project proposes that monetary policy moves risk discounts in the stock market precisely because it is powerful for the real economy. (b) How effective is monetary policy when short-term interest rates are stuck at zero? This project will newly disentangle constraints on monetary policy from time-varying risk aversion as reasons why the stock market responds more strongly to macroeconomic news during recessions (Boyd, Hu and Jagannathan (2005) and Law, Song, and Yaron (2020)). (c) This project will study to what extent learning about the monetary policy rule occurs after the Federal Reserve has repeatedly surprised the public and show how this learning contributes to sometimes puzzling responses of long-term Treasury bond yields to monetary policy announcements, such as during the “interest rate conundrum” of 2004.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.