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Ayşe Arık [CV] ORC-id
| Last modified | |
| Institution | Heriot-Watt University |
| Status | Research Associate in the ARC connected to IFoA funded project |
| Manager | Andrew Cairns |
| Contact | a.arik@hw.ac.uk |
| Last modified | |
| Institution | Heriot-Watt University |
| Status | Research Associate in the ARC connected to IFoA funded project |
| Manager | Andrew Cairns |
| Contact | a.arik@hw.ac.uk |
Modelling, measurement and management of longevity risk. Morbidity risk modelling. Stochastic short rate and mortality models. Longevity securitization. Actuarial and financial pricing.
It is important to model development and changes in morbidity risk accurately due to its direct impact on health and social care. Our aim is to identify morbidity inequalities and trends of all cancer incidence in addition to incidence of more common cancers at different ages in different regions of England since 1981. We consider a Bayesian hierarchical model to take into account statistical uncertainty, and employ generalised linear model structures to estimate cancer morbidity rates at various age, year, gender, and region levels. Several models are assessed in a systematic way and best fitted models are determined using a Bayesian variable selection procedure based on marginal likelihood and Deviance Information Criterion. Morbidity inequalities across regions, measured as relative and absolute differences between the highest and lowest age-standardised fitted incidence rates, are assessed.
In this study we investigate pension buy-out price for a hypothetical pension scheme using jump diffusion models. Whilst the pricing model is based on a continuous time setting, a dependence assumption is considered where mortality rates (risk) can influence the financial market. The dependence assumption is imposed in two different ways. Numerical illustrations are presented to show the applicability of the model with various scenarios based on Monte Carlo simulations. We also provide a sensitivity analysis for different levels of parameter values.
Pension buy-out is a special financial asset issued to offload the pension liabilities holistically in exchange for an upfront premium. In this paper, we concentrate on the pricing of pension buy-outs under dependence between interest and mortality rates risks with an explicit correlation structure in a continuous time framework. Change of measure technique is invoked to simplify the valuation. We also present how to obtain the buy-out price for a hypothetical benefit pension scheme using stochastic models to govern the dynamics of interest and mortality rates. Besides employing a non-mean reverting specification of the Ornstein–Uhlenbeck process and a continuous version of Lee–Carter setting for modeling mortality rates, we prefer Vasicek and Cox–Ingersoll–Ross models for short rates. We provide numerical results under various scenarios along with the confidence intervals using Monte Carlo simulations.
Pension buy-ins and buy-outs have become an important aspect of managing pension risk in recent years. As a step toward understanding these pension de-risking instruments, we develop models for pricing investment risk and longevity risk embedded in pension buy-ins and buy-outs. We also bring a contingent-claims framework to price credit risk of buy-in bulk annuities. Overall, our model can be used to assess the pricing of investment, longevity, and credit risks being transferred in pension buy-in and buy-out transactions.
In this paper we consider different models for Turkish male mortality data. We fit the Lee-Carter model, Poisson log-bilinear model and two-factor model of Cairns, Blake and Dowd to the Turkish data. We compare these three models and price the future annuities using underlying models. We also analyse the effect of different mortality models on future annuity prices by calculating the commonly used risk measures. It turns out that two-factor model of Cairns, Blake and Dowd fits the Turkish male mortality better and produces more reasonable results.
In this study risks of an investor, that has a position in IMKB-100 return index, have been analyzed by using two different methods of extreme value theory: block maxima method and peaks over threshold method. Value at Risk, expected shortfall and return level are calculated to measure financial risks for different percentages.
Some emerging markets have been modelled by extreme value theorem and financial risks are evaluated by using value–at-risk and expected shortfall. Risk measurement enables to evaluate potential risks and effects of these risks in financial markets. Financial risks of an investor who has a position on stock indices in the emerging markets such as Turkey, Poland and Chile have been analyzed and risk measures are compared for different confidence levels.
In this study a bond which is written on Turkish longevity risk has been priced. Whilst small variations of mortality improvements are modelled using random walk model with drift, rare longevity cases are modelled by extreme value theory. The payoff of this bond is expressed as put option spreads, and risk cubic method is carried out for pricing.