The Impact of Inflation Risk on Financial Planning and Risk-Return Profiles

Abstract

The importance of funded private or occupational old-age provision is expected to increase due to demographic changes and the resulting problems for government-run pay-as-you-go systems. Clients and advisors therefore need reliable methodologies to match offered products with clients' needs and risk appetite. In Graf et al. (2012), the authors have introduced a methodology based on stochastic modeling to properly assess the risk-return profiles -i.e. the probability distribution of future benefits -of various old-age provision products. In this paper, we additionally consider the impact of inflation on the risk-return profile of old-age provision products. In a model with stochastic interest rates, stochastic inflation and equity returns including stochastic equity volatility, we derive risk-return-profiles for various types of existing unit-linked products with and without embedded guarantees and especially focus on the difference between nominal and real returns. We find that typical "rule of thumb" approximations for considering inflation risk are inappropriate and further show that products that are considered particularly safe by practitioners because of nominal guarantees may bear significant inflation risk. Finally, we propose product designs suitable to reduce inflation risk and investigate their risk-return profile in real terms.

Signature-based validation of real-world economic scenarios

“…Those applications relate to (i) valuation of insurance products, (ii) hedging strategies for annuity portfolios and (iii) risk calculation for economic capital assessment. On item (i), we can mention the work of Boudreault and Panneton (2009) who study the impact on conditional tail expectation provision of GARCH and regime-switching models calibrated on historical data, and the work of Graf et al (2014) who perform simulations under the real-world probability measure to estimate the risk-return profile of various old-age provision products. On item (ii), Zhu et al (2018) measure the hedging error of several dynamic hedging strategies along real-world scenarios for cash balance pension plans, while Lin and Yang (2020) calculate the value of a large variable annuity portfolio and its hedge using nested simulations (real-world scenarios for the outer simulations and risk-neutral scenarios for the inner simulations).…”

“…On item (i), we can mention the work of Boudreault and Panneton (2009) who study the impact on conditional tail expectation provision of GARCH and regime-switching models calibrated on historical data, and the work of Graf et al. (2014) who perform simulations under the real-world probability measure to estimate the risk-return profile of various old-age provision products. On item (ii), Zhu et al.…”

Signature-based validation of real-world economic scenarios

“…Those applications relate to (i) pricing and valuation of insurance products, (ii) hedging strategies for annuity portfolios and (iii) risk calculation for economic capital assessment. On item (i), we can mention the work of Boudreault et al [4] who study the impact on Conditional Tail Expectation provision of GARCH and regime-switching models calibrated on historical data, and the work of Graf et al [15] who perform simulations under the real-world probability measure to estime the risk-return profile of various old-age provision products. On item (ii), Zhu et al [30] measure the hedging error of several dynamic hedging strategies along real-world scenarios for cash balance pension plans while Lin and Yang [23] calculate the value of a large variable annuity portfolio and its hedge using nested simulations (real-world scenarios for the outer simulations and risk-neutral scenarios for the inner simulations).…”