Valuation of capital protection options
Xiaolin Luo, Pavel V. Shevchenko

TL;DR
This paper introduces an efficient numerical algorithm using Gauss-Hermite quadrature and splines for pricing capital protection options, accounting for optimal policyholder behavior, which significantly impacts the fair fee calculation.
Contribution
It develops a novel Gauss-Hermite quadrature method with spline interpolation for pricing capital protection options under optimal withdrawal policies, improving computational efficiency and accuracy.
Findings
Optimal policyholder behavior increases the fair fee significantly.
Extra fees can reach over 40% at low interest rates and high volatility.
Static withdrawal assumptions underestimate the true cost of capital protection options.
Abstract
This paper presents numerical algorithm and results for pricing a capital protection option offered by many asset managers for investment portfolios to take advantage of market growth and protect savings. Under optimal withdrawal policyholder behaviour the pricing of such a product is an optimal stochastic control problem that cannot be solved using Monte Carlo method. In low dimension case, it can be solved using PDE based methods such as finite difference. In this paper, we develop a much more efficient Gauss-Hermite quadrature method with a one-dimensional cubic spline for calculation of the expectation between withdrawal/reset dates, and a bi-cubic spline interpolation for applying the jump conditions across withdrawal/reset dates. We show results for both static and dynamic withdrawals and for both the asset accumulation and the pension phases (different penalties for any excessive…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
