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CFAI Text Reading 21 - Linking Pension Liab to Assets

Page 454 - 458 - Setting Asset and Liability Sensitivities is a difficult read and is not covered by Sch. I don’t see any LOS related to it as well.
Can anyone explain in simple words what are they trying to do here?
Thanks

risk parameter is the std deviation I thought

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Many thanks to both. Just one clarification question: On Table 5 they are listing both assets (nominal bonds, equity, etc) and liabilities (accrued benefit, etc.) in column one and the “Risk” parameter is the Beta?
As for studying on a Sat night, I was a little behind this week and need to catch up. I will all the sat nights after the exam.

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Trying to identify a Liability mimicking portfolio… what numbers - – at the end they arrive at 85% nominal bonds, 5% real bonds and 10% equities is the right mix for the liability mimicking portfolio.
Steps there:
1. Determine factors involved.
Nominal Risk free rate = by product of both real rate of return, expected inflation and inflation risk premium. These risk premia can be derived from historical data, growth rate is derived from long term relationship between economy and stock market.
2. Develop a correlation matrix
includes equity premium, real bond premium, nominal bond premium, real rate, inflation and growth rates.
3. Develop a sensitivity matrix
degree to which assets and liabs move in response to movements in the risk factors.
4. estimate liability noise
noise is less substantial for accrued benefits, hence easier to hedge, than any noise associated with future wage liabilities.
5. assess results and create portfolio
likely results:
accrued benefits - will be highly correlated with real and nominal bonds.
future wage liability: highly correlated with equity.

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The CFAI reading 21 talks about the liabilities of DB plans and how thay can be modeled with different asset types. Some liabilities are fixed, so they are matched with fixed bonds, some others are inf-adjusted so they are matched with real bonds etc. By the way, Hey, studying on a sat night?

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