|dc.contributor.advisor||McNeil, Professor Alexander||
|dc.contributor.author||Cathcart, Mark J.||
|dc.description.abstract||With the introduction of the Solvency II regulatory framework, insurers face the
challenge of managing the risk arising from selling unit-linked products on the market.
In this thesis two approaches to this problem are considered:
Firstly, an insurer could project the value of their liabilities to some future time using
Monte Carlo simulation in order to reserve adequate capital to cover these with a
high level of confidence. However, the complex nature of many liabilities means that
valuation is a task requiring further simulation. The resulting `nested-simulation' is
computationally inefficient and a regression-based approximation technique known
as least-squares Monte Carlo (LSMC) simulation is a possible solution. In this thesis,
the problem of configuring the LSMC method to efficiently project complex insurance
liabilities is considered. The findings are illustrated by applying the technique
to a realistic unit-linked life insurance product.
Secondly, an insurer could implement a hedging strategy to mitigate their exposure
from such products. This requires the calculation of market risk sensitivities
(or `Greeks'). For complex, path-dependent liabilities, these sensitivities are typically
estimated using simulation. Standard practice is to use a `bump and revalue'
method. As well as requiring multiple valuations, this approach can be unreliable
for higher order Greeks. In this thesis some alternative estimators are developed.
These are implemented for a realistic unit-linked life insurance product within an
advanced economic scenario generator model, incorporating stochastic interest rates
and stochastic equity volatility.||en_US
|dc.publisher||Mathematical and Computer Sciences||en_US
|dc.rights||All items in ROS are protected by the Creative Commons copyright license (http://creativecommons.org/licenses/by-nc-nd/2.5/scotland/), with some rights reserved.||
|dc.title||Monte Carlo simulation approaches to the valuation and risk management of unit-linked insurance products with guarantees||en_US