The problem with spliced futures
No doubt, the correct datasets are the key when one does some analysis in the financial markets. For some financial instruments, the data can be found for free and ready for the upcoming process, but on the other hand, some instruments are more complicated.
Nowadays, futures contracts are widely spread and popular among practitioners. However, each delivery month is connected with a different price where the price of the underlying asset should stand at a given date in the future (the expiration date). Clearly, this complicates any possible analysis, since there are different prices for different maturities. The industry standard for backtesting futures strategies is to construct one data sequence from a stream of contracts – a continuous futures contracts data series.
If somebody wants to hold futures contracts for a longer time, one has to roll the contracts each month, but there is a problem that the prices would be different. For example, at a rolling date, the contract ending in May can be traded for 60 dollars, and the contract ending in June can be traded for 70 dollars.
Algorithms for creating continuous futures series and removing the jumps are dependent on two main factors. It is important to choose the date when the successive contracts are rolled and secondly, which adjustments would be made to the raw contract prices. Since there are many options for both key elements, there are many variations of continuous futures contracts series.
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