The Pensions, Retirement and Ageing Seminar Series is jointly hosted by CEPAR and the School of Risk & Actuarial Studies at UNSW Sydney.
It takes place on a Monday (or Wednesday) from 12-1pm and provides an excellent opportunity to network with pensions and superannuation experts from Australia and overseas. This is an interdisciplinary group with backgrounds in economics, actuarial studies, finance, psychology, law, accounting, demography, marketing, medicine and related fields. We invite attendance of participants from other universities as well as from industry and government who are interested in both theoretical and applied research on pensions, retirement and ageing.
We also welcome presenters who are early career academics or practitioners. There is no charge to attend these seminars.
In response to the current COVID-19 situation, upcoming seminars are being held online as webinars. All seminar registrants will be notified via email. Please contact Inka Eberhardt if you are interested in presenting, participating or would like to be added to the Pensions, Retirement and Ageing Seminar Series mailing list.
We wish to express our best wishes to the community during this challenging time.
11 March - Geoff Warren (ANU College of Business and Economics) "The ‘Right’ Level for the Superannuation Guarantee: A Straightforward Issue by No Means"
(Location: UNSW Kensington Campus, in Quadrangle Building, Room 2063)
6 April - Katja Hanewald (CEPAR, UNSW School of Risk and Actuarial Studies) "Long-term Care Insurance Financing using Home Equity Release: Evidence from an Experimental Study"
20 April - Ioana Ramia (Centre for Social Impact, UNSW Sydney) and Mălina Voicu (Research Institute for the Quality of Life, Romanina Academy) "Life Satisfaction and Happiness among Older Europeans: The Role of Active Ageing"
4 May - Brooke Brady (UNSW Ageing Futures Institute, CEPAR) "The Development of a New App-based Assessment of Shared Financial Decision Making"
1 June - Xiao Xu (CEPAR, UNSW School of Risk and Actuarial Studies) "Deep Reinforcement Learning for Variable Annuities Hedging"
15 June - TBA
29 June - TBA
13 July - George Kudrna (CEPAR)
27 July - TBA
10 August - TBA
Speaker: Piet de Jong (Department of Applied Finance and Actuarial Studies, Macquarie University)
Topic: Lockboxes and Glide Paths
If you are an average Australian you will immediately spend your super when you retire, go on the age pension, and, if aged care is needed, get the government to pony up. With more old people, government age support spending is likely to rocket. Standing idly by is a bloated and inefficient super "helpers" industry, exploiting a captive, ill-informed customer base, corroding retirement savings with fees, and adding no value. I you want to stand on your own retired feet, you won’t find the one product you’ll likely crave: fairly priced pensions. This submission proposes: 1) employees pay for their future age pension and aged care while employed, saving the government an estimated $30 Bn pa; 2) the winding down of the super industry saving an estimated $1,500 pa per employee and doubling retirement incomes; 3) the super system to be properly choreographed so that retirees can buy fairly priced pensions.
Speaker: Geoff Warren (ANU College of Business and Economics)
Topic: The ‘Right’ Level for the Superannuation Guarantee: A Straightforward Issue by No Means
We deploy a stochastic life-cycle model to examine how differing levels of the superannuation guarantee (SG) impact on the welfare of individual Australians under existing superannuation, tax and pension eligibility rules. Our main focus is the effect of various assumptions on the optimal SG, emphasising the role of income and the retirement objectives of the individual. The analysis supports estimating the gains and losses from changing the SG for various individuals, and associated impacts on net government revenue. We find the optimal SG to vary substantially with income and objectives. While our baseline analysis indicates a SG of below the current level of 9.5%, higher estimates emerge if access to the Age Pension is excluded, and if the SG is used as a mechanism to self-insure against living to a very old age, being forced into early retirement, or incurring lower investment returns. We conclude that the case for raising the SG above 9.5% depends on the underlying assumptions, with the policy objectives that the SG is intended to achieve being critical.
Speaker: Katja Hanewald (CEPAR, UNSW School of Actuarial Studies and Risk)
Topic: Long-term Care Insurance Financing using Home Equity Release: Evidence from an Experimental Study
Abstract: Population ageing is a global trend and many countries including China face increasing pressures to provide long-term care services for the elderly. We explore new mechanisms to fund long-term care using housing wealth. We conduct and analyze an experimental online survey fielded in China that assesses the potential demand for new financial products that allow individuals to access their housing wealth to buy long-term care insurance. We find in our sample of 1,200 Chinese homeowners aged 45-64 that the stated demand for long-term care insurance increases when individuals can use housing wealth in addition to savings to purchase long-term care insurance. Individuals prefer to access housing wealth via reverse mortgage loans rather than via home reversion, which is a partial sale of housing wealth. Our results inform current policy reforms in China which aim at developing the private market for health and long-term care insurance products.
Speakers: Ioana Ramia (Centre for Social Impact, UNSW) and Mălina Voicu (Research Institute for the Quality of Life, Romanina Academy)
Topic: Life satisfaction and happiness among older Europeans: the role of active ageing
Abstract: The older population is growing globally, and more so in some European countries. Aimed at enhancing the quality of life of older people, active ageing has been on the policy agenda in Europe since the beginning of the 21st century. Using a subsample of the European Quality of Life Survey consisting of individuals aged 65 and over living in 27 European countries we explore the effect of active ageing on subjective quality of life. The central argument of the paper is that active ageing is cumulative, consisting of a mix of various interconnected activities. Hence, when assessing the impact of active ageing on quality of life we include the whole collection of activities in which seniors engage and avoid limiting to a single activity. Latent Class Analysis is employed to find the mix of interconnected activities in which older adults engage. We identify three classes: home keepers (mainly engaging in housekeeping activities), carers (mainly engaged in caring, but also some housekeeping activities) and those engaged outside their homes (engaged primarily in paid or unpaid work). Multilevel regression models test the connection between the different strategies to remain active in later life on life satisfaction and happiness, the cognitive and affective indicators of subjective quality of life. Our results show that remaining active in later life does not always lead to improvements in subjective quality of life and that separate strategies to remain active in later life are at work to increase life satisfaction and happiness in later stages of life.
Speaker: Brooke Brady (UNSW Ageing Futures Institute, CEPAR)
Topic: The Development of a New App-based Assessment of Shared Financial Decision Making
Abstract: Older adults are increasingly expected to make complex financial decisions, to promote their current and future wellbeing. Research has shown that older adults have lower financial literacy, are less likely to seek financial advice, and may be less discriminating in the type of advice they use. Very little is currently understood about the family and social contexts in which older adults make financial decisions, including how and when these decisions are shared. This project aims to develop and pilot test a new smartphone application. The shared decision making tool at the heart of this app includes a bank of hypothetical ‘real-world’ financial decision scenarios, designed to enable researchers to measure the dynamics of shared financial decision-making among older adults. This app aims to collect novel data to address existing research gaps, and has the potential to be used for implementing future interventions aimed at promoting financial wellbeing among older adults.
Speaker: Nicolás Salamanca (Melbourne Institute: Applied Economics & Social Research, University of Melbourne)
Topic: How People React to Pension Risk
Abstract: We show that people exposed to greater pension risk are less likely to invest in risky assets. We exploit a reform that links people’s future pension benefits to their pension funds’ funding ratio—a measure of the fund’s financial health—making funding ratios a fund-specific measure of pension risk. The effect of pension risk is stronger for people who are better informed about their pensions, for retirees and pension-age non-retirees, and for wealthier people. The funding ratio does not affect investments in a pre-reform period, nor does it affect bequest intentions, (expected) retirement, or the motivations for saving.
Speaker: Xiao Xu (CEPAR, UNSW School of Risk and Actuarial Studies)
Topic: Deep Reinforcement Learning for Variable Annuities Hedging
Abstract: Variable annuities with guaranteed minimum benefits (VA+GMBs) have become increasingly popular in retirement planning, providing policyholders with life-contingent income and protection against the downside risk of equity markets. The US market for VA products was more than 2 trillion US Dollars by the year-end of 2017. For life insurers offering these products, a challenging issue is developing more efficient pricing and hedging of their VA+GMBs portfolios, something also of interest to insurance regulators. With the artificial intelligence (AI) revolution, deep learning has been applied increasingly applied to classical problems in finance, including portfolio management. This research investigates hedging VA+GMBs contracts with an efficient model-free deep learning algorithm. The deep hedging is performed by implementing a four-layered recurrent neural network (RNN) with long short-term memory (LSTM) units to optimise a convex risk measure – conditional value at risk (CVaR) at 95%. The deep hedging framework is applied to hedge and obtain comparable risk measures for a range of equity model assumptions, including the Black-Scholes, stochastic volatility, Levy and time-changed Levy process models. Compared to the traditional model-based delta hedging strategies, the deep hedging algorithm provides significant improvements in computation and speed. The results also confirm that the resulting solvency measure, value at risk (VaR) at 99.5%, for equity models with jumps and stochastic volatilities are significantly higher than those implied by the Black-Scholes framework. As the 99.5% VaR is the suggested measure adopted in the Solvency II framework, using VaR with commonly used Black-Scholes assumptions will significantly underestimate the required capital in a more volatile market for insurance providers. For both life insurers providing VAs and insurance regulators, equity model assumptions are found to be critical, including in the model-free deep learning environment.