Mean reversion, momentum, and volatility term structure
volatility(τ)=√(Var(z(t)-z(t-τ)))
where Var means taking the variance over many sample times. If the prices really follow a geometric random walk, then Var(τ)≡Var((z(t)-z(t-τ)) ∝ τ, and the volatility simply scales with the square root of the sampling interval. This is why if we measure daily returns, we need to multiply the daily volatility by √252 to obtain the annualized volatility.
Traders also know that prices do not really follow a geometric random walk. If prices are mean reverting, we will find that they do not wander away from their initial value as fast as a random walk. If prices are trending, they wander away faster. In general, we can write
Var(τ) ∝ τ^(2H)
where H is called the “Hurst exponent”, and it is equal to 0.5 for a true geometric random walk, but will be less than 0.5 for mean reverting prices, and greater than 0.5 for trending prices.
If we annualize the volatility of a mean-reverting price series, it will end up having a lower annualized volatility than that of a geometric random walk, even if both have exactly the same volatility measured at, say, 5-min bars. The opposite is true for a trending price series. For example, if we try this on AUDCAD, an obviously mean-reverting time series, we will get H=0.43.
All of the above are well-known to many traders, and are in fact discussed in my book. But what is more interesting is that the Hurst exponent itself can change at some time scale, and this change sometimes signals a shift from a mean reversion to a momentum regime, or vice versa. To see this, let’s plot volatility (or more conveniently, variance) as a function of τ. This is often called the term structure of (realized) volatility.
Start with the familiar SPY. we can compute the intraday returns using midprices from 1 minutes to 2^10 minutes (~17 hrs), and plot the log(Var(τ)) against log(τ). The fit, shown below, is excellent. (Click figure to enlarge). The slope, divided by 2, is the Hurst exponent, which turns out to be 0.494±0.003, which is very slightly mean-reverting.
We can do the same analysis for USO (the WTI crude oil futures ETF). The intraday H is 0.515±0.001, indicating significant trending behavior. The daily H is 0.56±0.02, even more significantly trending. So momentum strategies should work for crude oil futures at any reasonable time scales.
- Nick over at mintegration.eu discusses the new intraday databases at Quandl and Kerf.
- Factorwave.com (Euan Sinclair’s creation) started a new forum: slack.factorwave.com. It has some very active and in-depth discussions of many trading and investing topics.
- Prof. Matthew Lyle at Kellogg School of Management has a new paper out that relates fundamentals to variance risk premiums: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2696183.
- April 20-21: Mean Reversion Strategies.
Source: http://epchan.blogspot.com/2016/04/mean-reversion-momentum-and-volatility.html
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