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Latest inflation fixing not taken into account going forward #1914
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Thanks for posting! It might take a while before we look at your issue, so don't worry if there seems to be no feedback. We'll get to it. |
I'm not sure of your setup, but I suspect that the problem is that your forecast inflation curve, as of latest release, needs to be defined by means of an observation lag. Starting from the next release (1.34), it will be possible to specify explicitly a base date corresponding to the latest inflation fixing (see #1896). That should solve the problem. |
Yes I use observation lag. Can the latest inflation fixing in version 1.34 be in the future? As I mentioned it would be great to pass in a list of upcoming CPI fixing expectations for the next x months. |
I don't think a future fixing would be used. In that case, I would calculate the corresponding zero rates and create the curve as an |
This issue was automatically marked as stale because it has been open 60 days with no activity. Remove stale label or comment, or this will be closed in two weeks. |
This issue was automatically closed because it has been stalled for two weeks with no further activity. |
Hello, I noticed an error looking at daily inflation CPI levels generated. A drop is introduced ones a new actual fixing has come in after the base month used for inflation calculation. So for example looking at US Inflation on 1st June 2023. As the index has a 3 months fixing lag, everything is based on the historical March 2023 fixing (301.836). But for value date 1st June 2023, also the April 2023 actual CPI fixing has been released of 303.363. This is priced back in QuantLib, though is not used anymore going forward. The CPI for 2nd April 2023 does not use the April 2023 fixing anymore, but builds again from the base CPI of March 2023 and the 1 year swap rate and seasonality. This results in a drop between 2nd and 1st April as can be seen in example below (calculated with no seasonality). So you get a jump in the curve and a loss of information by not using the latest actual inflation data number.
I think the latest information of the April CPI should be used going forward by calculating a new constant growth rate for the remaining 11 months and apply the 11 months of seasonality.
Another indirect solution and also a nice enhancement would be to provide for the first year instead of the seasonality vector a list of user given CPI values for certain months up to the 1 year point. Then apply seasonality vector for all the months after.
It happens often that the next couple of CPI levels differ from the levels implied through seasonality as for example oil is spiking higher. Hence it would be good to correct for this using your own predicted CPI levels for the next couple of months and supply them to QuantLib through an additional input vector.
Thanks,
Vincent
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