Abstract
Pricing ultra-long-dated pension liabilities under the market-consistent valuation is challenged by the scarcity of the long-term market instruments that match or exceed the terms of pension liabilities. We develop a robust self-financing hedging strategy which adopts a min–max expected shortfall hedging criterion
to replicate the long-dated liabilities for agents who fear parameter misspecification.We introduce a backward robust least squares Monte Carlo method to solve this dynamic robust optimization problem. We find that both naive and robust optimal portfolios depend on the hedging horizon and the current
funding ratio. The robust policy suggests taking more risk when the current funding ratio is low. The yield curve constructed by the robust dynamic hedging portfolio is always lower than the naive one but is higher than the model-based yield curve in a low-rate environment.
to replicate the long-dated liabilities for agents who fear parameter misspecification.We introduce a backward robust least squares Monte Carlo method to solve this dynamic robust optimization problem. We find that both naive and robust optimal portfolios depend on the hedging horizon and the current
funding ratio. The robust policy suggests taking more risk when the current funding ratio is low. The yield curve constructed by the robust dynamic hedging portfolio is always lower than the naive one but is higher than the model-based yield curve in a low-rate environment.
Original language | English |
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Pages (from-to) | 273-300 |
Number of pages | 28 |
Journal | Journal of Pension Economics & Finance |
Volume | 20 |
Issue number | 2 |
Early online date | 5 Jun 2020 |
DOIs | |
Publication status | Published - Apr 2021 |
JEL classifications
- c61 - "Optimization Techniques; Programming Models; Dynamic Analysis"
- g11 - "Portfolio Choice; Investment Decisions"
- e43 - Interest Rates: Determination, Term Structure, and Effects
Keywords
- Least squares Monte Carlo
- Parameter uncertainty
- incomplete market
- liability valuation
- robust optimization
- least squares Monte Carlo
- parameter uncertainty
- Incomplete market