Timeline for Dimension reduction of par risk strips
Current License: CC BY-SA 4.0
7 events
| when toggle format | what | by | license | comment | |
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| Nov 7, 2023 at 18:30 | comment | added | SI7 | Another view: in the equity derivatives world there is a concept called „weighted Vega“. Some participants aggregate all the Vega buckets into just one anchor bucket assuming a perfect square root of time relationship between the volatility movements. I‘m looking for a similar „weighted DV01“ approach, acknowledging that we cannot aggregate all the risk into just one bucket as in the square root of time approach | |
| Nov 7, 2023 at 18:28 | comment | added | SI7 | I would target something like in your first comment above. Let’s make it easier: We probably all agree that 7y and 8y par swaps have more or less a beta=1 relationship (in OLS sense). Hence I can aggregate the 7y bucket into the 8y bucket without loosing significant accuracy in delta P&L explain. But 8y and 20y will not have a similarly strong relationship, hence we cannot aggregate the 8y into the 20. (but obviously the 19y into the 20y). How would you approach the risk bucketing into a smaller subset of nodes? | |
| Nov 7, 2023 at 10:45 | comment | added | Attack68♦ | PCA will not be allowed under your deffinition of P, since P is a subset of S. Any PCA is not a subset of S, it is an object that is some linear combination of all the elements in S. | |
| Nov 7, 2023 at 9:38 | comment | added | Attack68♦ | Do you want the PnL error to be minimised over time? i.e. youy have to define your buckets P with in sample data, and then keep the same P buckets for out of sample data over the next, say 100 days. and assert that your buckets are the best representation of the dimension reduction to capture the smallest pnl error? | |
| Nov 7, 2023 at 7:58 | comment | added | SI7 | Well, as I said, it's a first guess and I would be happy to read about different/easier approaches. Obviously, some degree of correlation/volatility between buckets need to be taken into account. | |
| Nov 7, 2023 at 1:10 | comment | added | Cloud Cho | Is any reason behind that you want to use the PCA? Isn't the PCA for mainly seeing important component not reducing dimension? | |
| Nov 6, 2023 at 20:31 | history | asked | SI7 | CC BY-SA 4.0 |