Ken Seng Tan

Optimal Retention for a Stop-loss Reinsurance Under the VaR and CTE Risk Measures

We propose practical solutions for the determination of optimal retentions in a stop-loss reinsurance. We develop two new optimization criteria for deriving the optimal retentions by, respectively, minimizing the value-at-risk (VaR) and the conditional tail expectation (CTE) of the total risks of an insurer. We establish necessary and sufficient conditions for the existence of the optimal retentions for two risk models: individual risk model and collective risk model. The resulting optimal solution of our optimization criterion has several important characteristics: (i) the optimal retention has a very simple analytic form; (ii) the optimal retention depends only on the assumed loss distribution and the reinsurer’s safety loading factor; (iii) the CTE criterion is more applicable than the VaR criterion in the sense that the optimal condition for the former is less restrictive than the latter; (iv) if optimal solutions exist, then both VaR- and CTE-based optimization criteria yield the same optimal retentions. In terms of applications, we extend the results to the individual risk models with dependent risks and use multivariate phase type distribution, multivariate Pareto distribution and multivariate Bernoulli distribution to illustrate the effect of dependence on optimal retentions. We also use the compound Poisson distribution and the compound negative binomial distribution to illustrate the optimal retentions in a collective risk model.

Optimal Reinsurance under VaR and CVaR Risk Measures: A Simplified Approach

In this paper, we study two classes of optimal reinsurance models by minimizing the total risk exposure of an insurer under the criteria of value at risk (VaR) and conditional value at risk (CVaR). We assume that the reinsurance premium is calculated according to the expected value principle. Explicit solutions for the optimal reinsurance policies are derived over ceded loss functions with increasing degrees of generality. More precisely, we establish formally that under the VaR minimization model, (i) the stop-loss reinsurance is optimal among the class of increasing convex ceded loss functions; (ii) when the constraints on both ceded and retained loss functions are relaxed to increasing functions, the stop-loss reinsurance with an upper limit is shown to be optimal; (iii) and finally under the set of general increasing and left-continuous retained loss functions, the truncated stop-loss reinsurance is shown to be optimal. In contrast, under CVaR risk measure, the stop-loss reinsurance is shown to be always optimal. These results suggest that the VaR-based reinsurance models are sensitive with respect to the constraints imposed on both ceded and retained loss functions while the corresponding CVaR-based reinsurance models are quite robust.

Uncertainty in mortality forecasting: an extension to the classical Lee-Carter approach

Traditionally, actuaries have modeled mortality improvement using deterministic reduction factors, with little consideration of the associated uncertainty. As mortality improvement has become an increasingly significant source of financial risk, it has become important to measure the uncertainty in the forecasts. Probabilistic confidence intervals provided by the widely accepted Lee-Carter model are known to be excessively narrow, due primarily to the rigid structure of the model. In this paper, we relax the model structure by considering individual differences (heterogeneity) in each age-period cell. The proposed extension not only provides a better goodness-of-fit based on standard model selection criteria, but also ensures more conservative interval forecasts of central death rates and hence can better reflect the uncertainty entailed. We illustrate the results using US and Canadian mortality data.

Optimal Reinsurance with General Premium Principles

In this paper, we study two classes of optimal reinsurance models from the perspective of an insurer by minimizing its total risk exposure under the criteria of value at risk (VaR) and conditional value at risk (CVaR), assuming that the reinsurance premium principles satisfy three basic axioms: distribution invariance, risk loading and stop-loss ordering preserving. The proposed class of premium principles is quite general in the sense that it encompasses eight of the eleven commonly used premium principles listed in Young (2004). Under the additional assumption that both the insurer and reinsurer are obligated to pay more for larger loss, we show that layer reinsurance is quite robust in the sense that it is always optimal over our assumed risk measures and the prescribed premium principles. We further use the Wang’s and Dutch premium principles to illustrate the applicability of our results by deriving explicitly the optimal parameters of the layer reinsurance. These two premium principles are chosen since in addition to satisfying the above three axioms, they exhibit increasing relative risk loading, a desirable property that is consistent with the market convention on reinsurance pricing.

Pricing Annuity Guarantees Under a Regime-Switching Model

Abstract We consider the pricing problem of equity-linked annuities and variable annuities under a regimeswitching model when the dynamic of the market value of a reference asset is driven by a generalized geometric Brownian motion model with regime switching. In particular, we assume that regime switching over time according to a continuous-time Markov chain with a finite number state space representing economy states. We use the Esscher transform to determine an equivalent martingale measure for fair valuation in the incomplete market setting. The paper is complemented with some numerical examples to highlight the implications of our model on pricing these guarantees.

On Pricing and Hedging the No-Negative-Equity Guarantee in Equity Release Mechanisms

In a roll-up mortgage, the borrower receives a loan in the form of a lump sum. The loan is rolled up with interest until the borrower dies, sells the house, or moves into long-term care permanently. The house is sold at that time, and the proceeds are used to repay the loan and interest. Most roll-up mortgages are sold with a no-negative-equity guarantee (NNEG), which caps the redemption amount at the lesser of the face amount of the loan and the sale proceeds. The core of this study is to develop a framework for pricing and managing the risks of the NNEG.

Modeling Period Effects in Multi-Population Mortality Models: Applications to Solvency II

Recently Cairns et al. introduced a general framework for modeling the dynamics of mortality rates of two related populations simultaneously. Their method ensures that the resulting forecasts do not diverge over the long run by modeling the difference in the stochastic factors between the two populations with a mean-reverting autoregressive process. In this article, we investigate how the modeling of the stochastic factors may be improved by using a vector error correction model. This extension is highly intuitive, allowing us to visualize the cross-correlations and the long-term equilibrium relation between the two populations. Another key benefit is that this extension does not require the user to assume which one of the two populations is dominant. This benefit is important because, as we demonstrate, it is not always easy to identify the dominant population, even if one population is much larger than the other. We illustrate our proposed extension with data from a pair of populations and apply it to the calculation of Solvency II risk capital.

Pricing Standardized Mortality Securitizations: A Two‐Population Model with Transitory Jump Effects

Mortality dynamics are subject to jumps that are due to events such as wars and pandemics. Such jumps can have a significant impact on prices of securities that are designed for hedging catastrophic mortality risk, and therefore should be taken into account in modeling. Although several single‐population mortality models with jump effects have been developed, they are not adequate for modeling trades in which the hedgers population is different from the population associated with the security being traded. In this article, we first develop a two‐population mortality model with transitory jump effects, and then we use the proposed model and an economic‐pricing framework to examine how mortality jumps may affect the supply and demand of mortality‐linked securities.

An Accelerating Quasi-Monte Carlo Method for Option Pricing Under the Generalized Hyperbolic Lévy Process

In this paper, we develop a simple and yet practically efficient algorithm for simulating high-dimensional exotic options. Our method is based on an extension of Imai and Tans linear transformation method, which is originally proposed in the context of simulating a Gaussian process. By generalizing this method to other stochastic processes and exploiting the numerical inversion method of Hormann and Leydold, this method can be used to enhance quasi-Monte Carlo method in a wide range of applications. We demonstrate the relative efficiency of our proposed simulation technique using exotic option examples including Asian, lookback, barrier, and cliquet options for which the underlying asset price follows an exponential generalized hyperbolic Levy process. We also illustrate the impact of our proposed method on dimension reduction.

Minimizing Effective Dimension Using Linear Transformation

In recent years, constructions based on Brownian bridge [11], principal component analysis [1], and linear transformation [7] have been proposed in the context of derivative pricing to further enhance QMC through dimension reduction. Motivated by [16, 18] and the ANOVA decomposition, this paper (i) formally justifies the dimension minimizing algorithm of Tan and Imai [7], and (ii) proposes a new formulation of linear transformation which explicitly reduces the effective dimension (in the truncation sense) of a function. Another new application of LT method to an interest rate model is considered. We establish the situation for which linear transformation method outperforms PCA.This method is not only effective on dimension reduction, it is also robust and can easily be extended to general diffusion processes.