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I am interested in calculating the effect of the roll return using different roll strategies. In specific I want to mimic a long-only futures investments. I have historical data for several agricultural commodities. In order to compare the returns of the different roll strategies I created ratio back-adjusted time series using the following formula's.
Ratio adjustment = (Price new contract - Price old contract)/(Price old contract)
Adjusted prices = (all old futures * ratio adjustment) + Prices new contract
Since I use historical data ranging from the 80's to the end of 2014 there are multiple rolls. For every I make these adjustments (iterative process). So the latest contract is not adjusted while the first contract is adjusted tens of times (depending on the roll strategy). I want to know what the return of the various roll strategies are. Hence, I am not interested in the prices of the futures contracts itself (I realize that these get distorted by the ratio adjustment). Furthermore, since futures are traded on margin I will take a more academic approach and make the assumption that the futures are fully collateralized. Meaning that a 'x' dollar amount is invested in T-bills, where x is equal to the price of the futures contract. Hence I calculate the return of the roll strategies as follows:
Return = (new adjustment price - old adjustment price)/(old adjustment price) - (t-bill interest earned on amount x) * 100
However, I think I am making a mistake since the return on a soybean position is extraordinary. Somewhere in the 600% (when I do not consider the t-bill interest earned) over a time period from 1997 to 2014 when rolling on the last trading day. What am I missing? How should I calculate and compare the performance of the different roll strategies if this approach is wrong?
Can you help answer these questions from other members on NexusFi?
I do not think you can calculate returns on back adjusted contracts. Anything that uses a ratio or divides on a back adjusted contract just gets screwy. So you are right to think something is up.