Edmund Phelps - more than a macroeconomist

Edmund Phelps died this week. Unfortunately, many may not remember his path-breaking work in macroeconomics, which opened the door to neo-Keynesian models. His most important macro work examined the inflation-growth trade-off through the lens of inflation expectations and addressed the natural rate of employment. First, if inflation expectations rose, there would be a corresponding impact on wages, leading to a wage-price spiral. Second, if central banks attempt to maintian emeplyent above the natural rate (the equilibrium rate) there will be a surge in inflaiton. 

While he was viewed early on as an important macroeconomist, he provided significant insights into innovation and creativity as drivers of economic dynamism. Few economists have had many important pieces of research across fields. 

He could not be placed in any school of thinking and should be viewed as one of the few truly independent thinkers on a range of topics. 

Large moves in the FX markets

 

In the paper “Large Moves in the Foreign Exchange Market", the researchers show that large currency moves are not random but related to the term structure of option-implied volatility. The difference between short-term and long-term implied volatility is a good predictor of the absolute value of current moves. Given this information, investors should buy straddles when the volatility curve is inverted. Being long the straddle allows for gains in either direction.

The implied volatility inversion provides useful information that can be exploited through straddles. It makes sense that high short-term volatility will likely see greater-than-average moves. 

Unsustained sales growth and AI

 

We are seeing very strong expectations for AI sales growth. There is no question that new technology will see stronger sales growth than the average firm and that, during the initial growth period, sales may be well above average. The question is, how do you temper these sales expectations to form more realistic estimates? The power of compounding will work against you. You can, of course, rely on some form of mean reversion, yet this can be guesswork.  

This problem was addressed in two papers by Counterpoint Global - Bayes and Base Rates: How History Can Guide Our Assessment of the Future and Bayes and Base Rates 2.0  I like this work because it takes the emotion out of the sales forecasts associated with AI and focuses on what we know across decades of data, within different industries, and with major changes in technology. We can form base rates using priors and then derive normal forecasts. The conclusion is that the sales forecasts are just too large, even if we isolate new technology, focus on specific industries, and account for the very best historical events. 

This does not mean investors should short these companies. It is unclear when reality will be realized, but arriving late is problematic, shorting can be a fool's game, but buying on these aggressive growth forecasts will be disappointing.  

The time series of risk shocks

In an earlier post, we discussed the differences in risk regime through decomposing the VIx index. 

The time series of risk regimes

We can also do the same for risk shocks, which are measured by changes in the VIX. We use bin analysis based on quantiles to form three groups of risk shocks. 

Again, a simple null hypothesis is that risk shocks occur during periods of market extremes, such as recessions and market turning points, yet we find that the time series of changes in the VIX, or risk shocks, appears more random. 

There is a different market response to risk shocks than to the risk regime; more simply, positive changes in the VIX index are associated with large market downturns, but their clustering differs from what we see in the risk regime.