9 research outputs found
Valuation Perspectives and Decompositions for Variable Annuities with GMWB riders
The guaranteed minimum withdrawal benefit (GMWB) rider, as an add on to a
variable annuity (VA), guarantees the return of premiums in the form of peri-
odic withdrawals while allowing policyholders to participate fully in any
market gains. GMWB riders represent an embedded option on the account value
with a fee structure that is different from typical financial derivatives. We
consider fair pricing of the GMWB rider from a financial economic perspective.
Particular focus is placed on the distinct perspectives of the insurer and
policyholder and the unifying relationship. We extend a decomposition of the VA
contract into components that reflect term-certain payments and embedded
derivatives to the case where the policyholder has the option to surrender, or
lapse, the contract early.Comment: 18 pages, proof of Lemma A.1 expanded for clarit
Deep Learning in a Generalized HJM-type Framework Through Arbitrage-Free Regularization
We introduce a regularization approach to arbitrage-free factor-model
selection. The considered model selection problem seeks to learn the closest
arbitrage-free HJM-type model to any prespecified factor-model. An asymptotic
solution to this, a priori computationally intractable, problem is represented
as the limit of a 1-parameter family of optimizers to computationally tractable
model selection tasks. Each of these simplified model-selection tasks seeks to
learn the most similar model, to the prescribed factor-model, subject to a
penalty detecting when the reference measure is a local martingale-measure for
the entire underlying financial market. A simple expression for the penalty
terms is obtained in the bond market withing the affine-term structure setting,
and it is used to formulate a deep-learning approach to arbitrage-free affine
term-structure modelling. Numerical implementations are also performed to
evaluate the performance in the bond market.Comment: 23 Pages + Reference