# stress testing zero coupon yield curve

i'm currently trying to stress test the zero coupon yield curve using daily observations from 2003 to 2019.

Each Zero coupon yield curve originate from an actuarial curve with 37 tenors that range between 1 day and 30 years.

the 1 day actuarial rate is the weighted average rate in interbank money market.

As for the other tenors,they're calculated by linear interpolation of weighted average rate from treasury bond's secondary market.

I divided the yield curve into 3 segments :short tenors(less than 1year) mid tenors (2y to 15y) long tenors (16y to 30y)

The tenors in every segment have similar tendencies over time.

I calculated the yields relative and absolute changes and i've noticed the following:

1. the one day yield is very volatile compared with other short tenors (7d,1m,2m ..)

2.the volatility decreases with segments

Since we're in a low rate enviromnent, we're intrested by the rates tendency to increase.

I've already calculated the 99% quantile for absolute and relative changes for every tenors but how can i apply them to reflect different levels of volatility.

I also did a principal component analysis PCA and i found that the first axe explains 56% of my data and it's the only one with all positive coordinate.The first four axe explain 98%. i entend to apply shifts on the 4 PC and reverse the process to find the original tenors.but i don't know how to interpret the results.

I'm looking for a way to capture different level of volatility in my yirld curve.

• Your question is unclear. Firstly it is helpful to actually define your data correctly: "average monetary interbank rate" is ambiguous (and important). What is a "short", "mid" and "long term" increase? What scenarios do you hope to exclude by only considering "non-parallel"? Volatility of different curve segments has been historically volatile and varied depending upon the regime. If you are hoping for reasonable levels of stress conditioned on the current low-rates regime, it might be better to use more recent data, like last 5-6 years.
– Attack68
Commented Mar 12, 2019 at 6:49
• Thank you.i've made the necessary changes and i hope it's clear now Commented Mar 12, 2019 at 12:54