Narayana Kocherlakota is planning to step down from his job as President of the Minneapolis Federal Reserve Bank. I know this because he came to a job talk at the University of Michigan yesterday. (Michigan is only one of the many job talks he is doing lately.) From 11:40 to 1:00 PM yesterday, Narayana presented his paper with Ron Feldman, Ken Heinecke, Sam Schulhofer-Wohl and Tom Tallarini: “Market-Based Probabilities: A Tool for Policymakers.” It was an excellent presentation.
Narayana explained that given their intended audience, he and his coauthors had felt the need to coin a more accessible term–“market-based probabilities.” for what financial economists call a “risk-neutral probability measure” or “stochastic discount factor.” One alternative description would be “market-importance-weighted probabilities.” The contention of the paper is that these weights are more relevant for many policy purposes than the raw statistical probabilities.
Right after the talk I mentioned it to Yichuan Wang when I saw him in my 1:00-2:30 PM “Monetary and Financial Theory” class and gave him a copy of the paper. Because Yichuan had already written and thought deeply about this issue, he turned around a post by 3:13 PM (well before I talked to Narayana about my proposal for eliminating the zero lower bound and the interaction of monetary policy and supply-side reforms in Japan during a 4 PM office visit). I am grateful for Yichuan’s permission to mirror it here as a guest post–the 9th student guest post this semester. (You can see the other student guest posts here.)
The gap between market forecasts of inflation based on securities prices and where inflation actually goes is a feature, not a bug. This gap is a risk premia that can be informative about what scenarios are worrisome to investors, and as such may be useful for policy makers deciding on how to weight the relative costs of inflation and deflation.
Justin Wolfers’ NYT article on market based inflation expectations explains how to derive inflation expectations from asset prices. In the article, he walks through an academic asset pricing paper that estimates a probability distribution for future inflation based on the prices of bets on inflation. The basic idea is that there is a betting market in which people can place bets on where they think inflation will be going. Just like how a bookie’s prices say something about the probability of certain horses winning a race, the prices on this inflation betting market make statements about the probability inflation ends up in certain zones.
Justin summarizes the findings:
While traders view inflation of roughly 2 percent as the most likely outcome, the market is also telling us the probability of other levels of inflation — or deflation. And it is saying that the risks of missing the 2 percent target are extremely unbalanced: It is twice as likely that inflation will come in below the Fed’s target as above it.
But there’s another aspect to asset prices that doesn’t show up for horse betting: risk premia. Whether inflation is high or low is related to the strength of the economy as a whole. In particular, if I were to tell you that there was going to be deflation in two years, your best bet would be that we were going through a double dip recession in which aggregate demand fell. You should then be willing to pay a premium to buy insurance against that scenario. In other words, you should be willing to pay better than fair odds that there will be deflation. Sure you might lose the bet on average, but when deflation hits and you lose your job, at least you got your racetrack winnings to cushion the blow.
Therefore the market forecast is equal to the true future expected inflation plus a risk premium that reflects whether low inflation or high inflation scenarios are scarier. If people are scared of a Japan style deflation, then the market forecast will underestimate true inflation. If on the other hand people are worried about 1970’s style stagflation, the market forecast will overestimate true inflation.
While this can be a nuisance if you want to get the best physical forecast of actual inflation, it can actually be tremendously valuable for central bankers who need to decide on whether to be more worried about the costs of high inflation or low inflation scenarios. For example, negative risk premia on inflation expectations tell policy makers that low inflation scenarios are much worse than high inflation scenarios. If this is the case, then the inflation target should be asymmetric — better to avoid scary deflation than deal with temporarily higher inflation.
Narayana Kocherlakota* made this argument in a recent macro seminar at the University of Michigan. (I’m borrowing the post title from his paper). In the context of a theoretical model he showed that the central bank’s objective function should focus on maximizing household welfare, not minimizing its own forecast errors. But based on the analysis above, the different levels of household welfare across different states of the world are embedded into the market forecast based on prices. So with some caveats about the financial constraints facing households, the central bank should try and make the market forecast equal the target.
As a more general point, this risk premium analysis shows how asset pricing is a form of quantitative psychology. Estimating risk premia helps answer the question “what do these asset prices say about the events that scare people”? And once policy makers know about these feared scenarios, they can adjust policy to make sure they don’t happen.
*As Narayana was quick to remind us, these are implications of a model from his own research, and not meant to represent the views of others in the Federal Reserve System.