Ponder this one on your daily walk:
The key question asked by standard monetary models used for policy analysis is, How do changes in short-term interest rates affect the economy? All of the standard models imply that such changes in interest rates affect the economy by altering the conditional means of the macroeconomic aggregates and have no effect on the conditional variances of these aggregates. We argue that the data on exchange rates imply nearly the opposite: the observation that exchange rates are approximately random walks implies that fluctuations in interest rates are associated with nearly one-for-one changes in conditional variances and nearly no changes in conditional means. In this sense standard monetary models capture essentially none of what is going on in the data. We thus argue that almost everything we say about monetary policy using these models is wrong.
Or put it this way:
We have focused on exchange rates rather than the term structure of interest rates because the implications of exchange rates are so striking. Specifically, if exchange rates are random walks, then all of the fluctuations in interest differentials are accounted for by fluctuations in conditional variances and none by fluctuations in conditional means. The data are so opposite of what standard models assume that even the most die-hard defenders of them should take note: If these data are accurate, then almost everything we say about monetary policy is wrong.
That is from the May 2007 American Economic Review, here is an earlier version of the paper. I doubt if changes in interest rate differentials are driven by risk premia of the standard sort; I would sooner cite "noise plus news," but resist the pull toward calling that a "conditional mean." I’ll also note that calling exchange rates a "random walk" is in the "do not reject" rather than "accept" statistical category. Both asset price moves contain lots of junk information, so we shouldn’t be totally surprised if they don’t fit together in some simple manner. Those moves weaken the paradox presented, but don’t come close to offering a coherent account of what is going on.
[I'll also note that calling exchange rates a "random walk" is in the "do not reject" rather than "accept" statistical category]
no, it’s in the “definitely reject” category – Siegel’s Paradox. If the £/$ exchange rate is a random walk, then by Jensen’s inequality, the $/£ rate can’t be.
I looked up the Siegel Paradox. In particular, I suspect you were taken in by something like the Bolle paper
This paper is a classic case of “assume a circular cow”.
1) Futures markets are not rational, and never will be.
2) All of the random walks discussed tend to infinite expected aggregate value over time, thus obliterating any “paradox” that particapants gain infinite expected value over time regardless which of considered strategies is pursued.
3) No amount of proof that an observed result cannot be correct changes the correctness of the observed result. If the market passes all known tests of randomness, then it is our job to figure out either the error in the methodology or in our theory.
I’m suspicious of the random walk thesis. It seems like laziness to me. Random walk is a throw of the dice with no cause/effect relationship. But we know that people have reasons for preferring one currency over another, so a cause/effect relationship must exist unless people are irrational. FX rates changes appear random because we don’t know which variables drive the decision making process, or the number of variables is too large and their interactions too complex. However, my bet is that theory is wrong, which leads to misspecification of the models. Maybe trying to model Austrian theory more closely would help. Start with Mises’s insight that relative changes in the money supplies of two countries affect exchange rates.
“Assuming log-normalit of all variables …..” I really wonder how sensitive their results are to this. most modern econometrics is about how parametric assumptions drive conclusions and i would suspect this is at least partly the case here. of course one could take recourse to the “first order” excuse but (a) then you’d have to show the approximation is good and (b) you have a prima facie misspecified model
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Very interesting read. I have often wondered about macroeconomics but don’t fully have a firm grasp on the matter. I enjoyed reading this though. ultrasonic thickness gauge
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