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Choosing Return Regressions for Hedge Effectiveness

Article Quant Q&A · Author: Question Anxiety
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Summary

This discussion compares estimating a hedge ratio by regressing spot and futures returns with regressing their price levels. The question reports much lower fit and a smaller hedge ratio from returns, while levels appear to track closely. The answer cautions that level regressions can show spurious correlation when the series are nonstationary, and suggests cointegration analysis as a possible alternative when a long-run relationship is the subject of interest. Returns are often more stationary and therefore commonly used, though they are not automatically appropriate for every hedging objective.

The answer also emphasizes checking whether spot and futures observations are synchronized. Their quoted closing prices may refer to different times, which can weaken measured relationships even when the instruments are economically related. If timestamps cannot be aligned, using returns over longer intervals may reduce the effect of timing mismatches. The discussion offers diagnostic guidance rather than a definitive hedge-effectiveness procedure; the appropriate regression depends on the task and data quality.

Key ideas

  • Regressing nonstationary spot and futures price levels can produce misleadingly strong relationships.
  • Returns are often more stationary, but their suitability still depends on the hedging objective.
  • Cointegration analysis may help assess a long-run relationship between price levels.
  • Check that spot and futures observations use aligned timestamps and sampling conventions.
  • Longer-horizon returns can reduce the impact of mismatched closing times.

Tags

FuturesCommoditiesStatisticsRisk management
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# Measuring Hedge Effectiveness


# Measuring Hedge Effectiveness












So I was trying to estimate the performance of a static hedge vs dynamic hedge in the electricity market and I came up with some weird findings. When I used the minimum variance hedge approach using regressed returns of spot to futures I was receiving an R-Square of around 0.05 (Hedge effectiveness also in this case) and a Hedge Ratio of 0.46, which seems extremely low, but when I regress the prices of spot to futures (both via OLS) I receive an R-Square of around 0.75 and a Hedge Ratio of 0.96, which makes a lot more sense to me since spot and futures are more or less moving at the same pattern. But if I remember correctly f.e. Hull (2012) sais to regress the returns.

My second question would be if it is correct to when using a Rolling-Beta approach (Hedge-Ratio = Beta) to calculate the hedge effectiveness, to estimate the series of Betas and then multiply it with the vector of the Futures prices. To get the hedge effectivness now I would calculate the vector of spot price - the beta vector multiplied with the vector of futures prices? I wasn't sure if this procedure is correct.

## Answer by Ivan (score 1)

https://quant.stackexchange.com/a/52993

If you regress spots and futures prices you are likely to end up with a case of spurious correlations. Perhaps a cointegration analysis would be a better tool. This is because the time series may not be stationary. Returns are typically (more) stationary which is why regressing them is usually more sensible. But no guarantee there either.

That said, it is really not clear a priori what the correct answer is. Regression of returns may or may not be appropriate for the task at hand.

One specific point in any case is this: are you 100% sure your time series are synchronous ? If you sensibly expect two series to be highly correlated but they are in fact not based on collected data, then you may want to check that the sampling is done on the same basis. It is often the case with futures and spot that the “close” prices for example do not denote the same timestamp at all. So I would double check that in the first instance. If you can’t fix it, then look at longer horizon returns to smooth out the discrepancy (one hour lag over a day is a lot, but one hour over a week is less important).

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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.