Research Archives
In this section, we provide links from our archives for PDFs of selected research we have previously published that delve deeply into topics related to the conduct of monetary policy and many other issues, even artificial intelligence (AI).
A Framework for Analyzing Tariff Policies
The analysis of the impact of a tariff war on commodities is more complex than one might anticipate. Changes in tariff policy by one country always set off retaliation by other countries, and responses are not limited to counter tariffs but can also involve regulations, export controls, and sanctions. In the land of an eye-for-an-eye, one can easily be blindsided by unanticipated consequences as behavioral feedback effects bounce through a complex global economic system. There are three pillars to our framework for tariff analysis. (1) Every policy shift is inter-connected and one should not analyze a specific policy shift on its own. (2) One should divide the analysis into a transition period and the longer-run equilibrium to appropriate consider the short-term serious implications of shocking the global economic system. And (3) focus on the two primary competing narratives. That is, there are often two competing narratives of what may occur given policy shocks and markets will tend to price the probability-weighted average of the two different and competing scenarios until it becomes clear which narrative is more correct. The bottom line is that when there are multiple major policy shifts underway simultaneously, the analysis quickly gets exponentially more complex, and a simple bi-lateral trade analysis will always be misleading and potentially far off the mark.
[Published in the Commodity Insights Digest Summer 2025 edition, supported by the Bayes School of Business in London.]
Tail Risk and Geo-Politics
A case study on using oil options to analyze tail risk and geo-politics.
Tail Risk, Geo-Politics, and Oil
Commodity research with time-varying parameters
The U.S. corn market responds in powerful ways to shifts in crude oil prices and potential droughts, yet not in consistent patterns. In this research, we use a time-varying parameter regression methodology to highlight how the corn market’s reaction to oil and drought has changed over time, and more specifically to highlight the characteristics of corn, oil, and drought price interaction that seem to be more significant than just tracking typical price momentum and volatility metrics and assuming persistent and consistent patterns.
Time-varying parameter (TVP) research is essentially regression analysis that weights more recent data more heavily than older data using an exponential decay process. We believe that market participants behave this way -- the more recent data is more important. By using our TVP technique, we can identify pattern shifts more quickly than traditional methods, and we can better understand what is driving the pattern shifts. While we started with corn, oil, and drought as a proof of concept case study, we will be extending our research into many other topics in financial and commodity markets.
Time Varying-Parameter Research: Corn, Oil, Drought
[Published in the Commodity Insights Digest in 2023 supported by the Bayes School of Business in London.]
Quantitative Easing
Quantitative easing (QE) was widely mis-analyzed. This research published in 2012, suggests that QE was mainly going to impact asset price inflation and not the real economy.
Essential concepts necessary to consider when evaluating the efficacy of quantitative easing
[published in the Review of Financial Economics in 2012.]
Understanding the economic impact of the Pandemic of 2020
This article on how to analyze the pandemic of 2020 was published in the fall of 2020, as the pandemic was in full swing and before vaccines had been brought to bear on the health crisis. The pandemic of 2020 shifted the economy from a relatively stable equilibrium state to a state deemed "Far from Equilibrium" by my former professor, Dr. John Rutledge. To analyze an economy far out of equilibrium and to appreciate how the recovery phases might evolve, we borrowed from the physics of phase transitions as our analytical technique.
From phase transitions to Modern Monetary Theory: A framework for analyzing the pandemic of 2020
[published in the Review of Financial Economics in 2020.]
Artificial intelligence and financial market behavior
Artificial intelligence is already having a huge impact on many sectors of the economy. Yet the impact on the behavior of financial markets may be limited, even as financial firms embrace AI in many parts of their businesses. To gain perspective, we examine the impact of AI on health care, education, and chess, to acquire some lessons about how AI might influence trading activity and market behavior.
Early Lessons from the AI Revolution for Financial Market Behavior
DNA of CFTC, SEC, and the Fed
There are varying objectives and cultural differences among the major regulators of derivative markets in the U.S. This article seeks to shed some light on the sources of differing missions among the Federal Reserve Board (Fed), Securities and Exchange Commission (SEC), and the Commodity Futures Trading Commission (CFTC) by exploring their origins. While the CFTC was not created until 1974, it has its origins in the Cotton Futures Act of 1914/16, and its focus was on the integrity of markets. The SEC was created by the Securities Exchange Act of 1934 in response to the Great Depression with a focus on investor protections. After a series of banking panics in the late 1800s and early 1900s, the Federal Reserve Act of 1913 established the Fed to promote banking system stability. After the Great Depression and WWII, the Fed’s objectives were broadened to include a focus on managing the economy to achieve full employment and price stability. Our perspective is that to understand the regulatory ecosystem in the U.S., one has to appreciate the implications of the different priorities of each regulator and, critically,whether its original focus was on market integrity, investor protections,or systemic risk.
DNA of US Regulatory Institutions