The Institute and Faculty of Actuaries (IFoA) has selected a number of research papers and publications which may be of interest to delegates of the 20th Asian Actuarial Conference (AAC):
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IFoA Research Journals
Simulation Based Capital Models: Testing, justifying and communicating choicesAbstract
The development of an economic capital model requires a decision to be made regarding how to aggregate capital requirements for the individual risk factors while taking into account the effects of diversification. Under the Individual Capital Adequacy Standards (ICAS) framework, UK life insurers have commonly adopted a correlation matrix approach due to its simplicity and ease in communication to the stakeholders involved, adjusting the result, where appropriate, to allow for non-linear interactions. The regulatory requirements of Solvency II have been one of the principal drivers leading to an increased use of more sophisticated aggregation techniques in economic capital models. This paper focuses on a simulation based approach to the aggregation of capital requirements using copulas and proxy models. It describes the practical challenges in parameterising a copula including how allowance may be made for tail dependence. It also covers the challenges associated with fitting and validating a proxy model. In particular, the paper outlines how insurers could test, communicate and justify the choices made through the use of some examples.
Copula; Tail Dependence; Proxy Model; Validation; Communication
Designing successful post retirement solutionsAbstract
The move from Defined Benefit (DB) to Defined Contribution (DC) has transferred the longevity and investment risks from the plan sponsor to the individual plan member. Without the actuarial cross-subsidies implied by pooling these risks, the danger of outliving one’s savings is significant. Much attention has been focused on pre-retirement investment design but less on post-retirement. In most countries, the post-retirement systems in place are insufficient to solve this challenge for small asset sizes or small proportions of individuals’ retirement accounts. However, a number of DC markets are mature, such as Australia and Chile, and the principles of a solution that works for all must be identified. This paper researches a number of post-retirement systems around the world and identifies ten key factors that contribute to post-retirement solution design. These factors can result in an inconsistency between countries regarding the most appropriate post-retirement solution. Additionally, a disconnect is apparent between what retirees need and want in post-retirement. Successful post-retirement solutions will inevitably blend investment and insurance components in a balanced manner. With lengthening life expectancies, research supports strategies that blend a growth and income account-based approach for the first 15-20 years after retirement with longevity protection in later life.
Post-retirement; Longevity risk; Inflation risk; Investment risk; Pensions; Defined contribution
The Future of Social Care FundingAbstract
With the UK population ageing, deciding upon a satisfactory and sustainable system for the funding of people’s long term care (LTC) needs has long been a topic of political debate. Phase 1 of the Care Act 2014 (‘the Act’) brought in some of the reforms recommended by the Dilnot Commission in 2011. However, the Government announced during 2015 that Phase 2 of ‘the Act’ such as the introduction of a £72,000 cap on Local Authority care costs and a change in the means testing thresholds1 would be deferred until 2020. In addition to this delay, the ‘freedom and choice’ agenda for pensions has come into force. It is therefore timely that the potential market responses to help people pay for their care within the new pensions environment should be considered.
In this paper, we analyse whether the proposed reforms meet the policy intention of protecting people from catastrophic care costs, whilst facilitating individual understanding of their potential care funding requirements. In particular, we review a number of financial products and ascertain the extent to which such products might help individuals to fund the LTC costs for which they would be responsible for meeting. We also produce case studies to demonstrate the complexities of the care funding system. Finally, we review the potential impact on incentives for individuals to save for care costs under the proposed new means testing thresholds and compare these with the current thresholds.
Download: The Future of Social Care Funding
Mis-estimation risk: measurement and impactAbstract
When deriving a demographic basis from experience data it is useful to know (i) what uncertainty surrounds that basis, and (ii) the financial impact of that uncertainty. Under the Solvency II regime in the European Union, insurers must hold capital against a number of risks. One of these is mis-estimation risk, i.e. the uncertainty over the current rates of mortality and other biometric risks experienced by a portfolio. We propose a general method for assessing mis-estimation risk, and by way of illustration we look at how mis-estimation risk can be assessed for a portfolio of pensions in payment from a U.K. pension scheme. We find that the impact of mis-estimation risk varies according to the risk factors included in a model, and that the inclusion of some necessary risk factors increases the financial impact of mis-estimation risk. In particular, the inclusion of risk factors which improve the model's fit and financial applicability can lead to an increase in the mis-estimation risk. We also find that a full portfolio valuation is preferable to using model points when assessing mis-estimation risk.
Mis-estimation risk, parameter risk, Solvency II, mortality risk, longevity risk, survival model, annuities
Bias, guess and expert judgement in actuarial workAbstract
Expert judgement is frequently used within general insurance. It tends to be a method of last resort and used where data is sparse, non-existent or non-applicable to the problem under consideration. Whilst such judgements can significantly influence the end results, their quality is highly variable. The use of the term 'expert judgement' itself can lend a generous impression of credibility to what may be a little more than a guess. Despite the increased emphasis placed on the importance of robust expert judgements in regulation, actuarial research to date has focused on the more technical or data driven methods, with less emphasis on how to use and incorporate softer information or how best to elicit judgements from others in a way that reduces cognitive biases. This paper highlights the research that the Getting Better Judgement Working Party has conducted into this area. Specifically it covers the variable quality of expert judgement, both within and outside the regulatory context, and presents methods that may be applied to improve its formation. The aim of this paper is to arm the insurance practitioner with tools to distinguish between low quality and high quality judgements and improve the robustness of judgements accordingly, particularly for highly material circumstances.
Expert judgement; Elicitation; Cognitive biases; Heuristics; Bayesian statistics
Application of the Solvency II actuarial function to general insurance firmsAbstract
The Solvency II Directive introduces the idea of a formal Actuarial Function to have responsibility over delivering the requirements of Article 48 of the Directive. Article 48 describes the responsibilities as being concerned with technical provisions, an opinion on reinsurance adequacy, an opinion on underwriting policy and contributing to the risk management system. Considerable documentation has been produced by the Prudential Regulation Authority (PRA), the Institute and Faculty of Actuaries (IFoA) and the European Insurance and Occupational Pensions Authority (EIOPA) on the subject, much of it very recent to the publication of this paper. The purpose of this paper is to provide the reader with some practical insights and suggestions around addressing the requirements of Article 48 of the Solvency II Directive in General Insurance firms, taking into consideration the publications of the aforementioned regulatory authorities. It is not our intention to give advice, nor to be seen to give advice, but rather to make suggestions and observations that we hope the reader will find useful.
Solvency II; General insurance; Actuarial function; Regulation
Expert judgement has been used since the actuarial profession was founded. In the past, there has often been a lack of transparency regarding the use of expert judgement, even though those judgements could have a very significant impact on the outputs of calculations and the decisions made by organisations. The lack of transparency has a number of dimensions including the nature of the underlying judgements, as well as the process used to derive those judgements. This paper aims to provide a practical framework regarding expert judgement processes, and how those processes may be validated. It includes a worked example illustrating how the process could be used for setting a particular assumption. It concludes with some suggested tools for use within expert judgement. Although primarily focused on the insurance sector (including consideration of the impact of Solvency II), the proposed process framework could be applied more widely without the need for significant changes.
Download: Expert judgement
Resource and Environment literature review (Sustainability)Abstract
Much actuarial work is underpinned by the use of economic models derived from mainstream academic theories of finance and economics which treat money as being a neutral medium of exchange. The sustainability of a financial system whose understanding is based on a limited view of the role of money has increasingly been subject to criticism. In order to identify needed research programmes to address such criticisms and improve these disciplines, we sought to understand the current state of knowledge in economics and finance concerning the link between monetary and financial factors and sustainability
Literature review; Banking; Finance; Monetary policy; Sustainability; Green economy; Actuarial science
Model risk: daring to open the black boxAbstract
With the increasing use of complex quantitative models in applications throughout the financial world, model risk has become a major concern. Such risk is generated by the potential inaccuracy and inappropriate use of models in business applications, which can lead to substantial financial losses and reputational damage. In this paper we deal with the management and measurement of model risk.
Model; Model Risk; Model Error; Model Risk Management Framework; Model Risk Governance & Control; Model Uncertainty; Model Risk Policy; Modelling Standards; Model Risk Appetite; Model Risk Identification; Model Risk Assessment; Model Risk Monitoring; Model Risk Reporting; Model Risk Mitigation; Model Risk Cultures; Intuitive Decision Makers; Confident Model Users; Conscientious Modellers; Uncertainty Avoiders
Download: Model risk: daring to open the black box
Non traditional investments: key considerations for insurersAbstract
Life insurers have historically relied upon investment markets as a key source of profit and crucially have been able to do this whilst embarking on relatively ‘vanilla’ investment strategies. In the current low yield environment, broadening their investment horizons is critical to maintaining profitability. This paper summarises some relevant external literature and the working party’s own research in understanding the potential benefits and pitfalls for insurers seeking to invest in non-traditional assets. The objective of this paper is to help educate and promote understanding by all (the many) relevant parties. In doing so, we hope to help organisations to achieve some further economic success for the ultimate benefit of society.
Whilst this paper has primarily been written from the perspective of a life insurer, we hope it will be of interest to a much wider audience. Many of the asset classes considered here are relevant to general insurers, pension funds and the wider capital markets.
It is very important to note that the paper does not contain investment advice and the analysis represents the views of the individuals and the working party and not the companies which they represent or the Profession. The paper does not make any comment as to the suitability (or otherwise) of specific investments for particular investors.
Non-traditional assets, alternative assets, investments, ALM, loans, infrastructure, PPP, PFI, social housing, real estate, residential mortgage, commercial mortgage, ground rent, student accommodation, asset backed securities, ABS, RMBS, CMBS, aircraft lease, emerging markets debt, high yield, private placement, private equity, hedge fund, insurance linked security, ILS.
Actuarial Research Centre (ARC)
The ARC is an international network of actuarial researchers around the world delivering cutting-edge research programmes that aim to address some of the significant global challenges in actuarial science over a number of years through a partnership of the actuarial profession, academics and practitioners.
As a Learned Society a key objective of the IFoA is to promote and support a wide range of research and knowledge exchange activities with members, external stakeholders and international research communities.
IFoA research is often carried out by members as part of a Working Party. Larger scale projects are commissioned and driven through the Actuarial Research Centre (ARC). View the full list of current IFoA research projects.
The IFoA is currently supporting three world-class programmes offering quality research with real impact, seeking to address challenges involving:
- Modelling, Measurement and Management of Longevity and Morbidity Risk - the development of a new generation of mortality and morbidity models with a specific focus on the drivers for mortality
- Use of Big Health and Actuarial Data for understanding Longevity and Morbidity - the development of new statistical and actuarial methods in the use of Big Data, in the context of health and wider applications
- Minimising longevity and investment risk while optimising future pension plans - future pension products that meet customer needs, balancing stability, performance and cost.
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