Research Team: Thomas Sneddon (CSIRO), Colin O’Hare (Monash University), Bernard Casey (University of Warwick), Pavel Shevchenko, Xiaolin Luo, Chenming Bao, Peter Toscas (CSIRO), Andrew Reeson (CSIRO), Clair Mason (CSIRO).
Activity 1: QUANTITATIVE MODELLING FOR DECUMULATION PHASE: CONSTRUCTING DYNAMIC OPTIMAL PORTFOLIOS F OR RETIREES
In the first stage of research, this project will focus on the following tasks:
1. To establish what individuals are currently doing in retirement with their funds: SMSF and super balances, spending pattern (from ATO/DSS/DHS data).
2. To determine the probability of ruin before death given fund balances at retirement and annual withdrawal patterns.
Annual death probability is also modelled.
3. To develop an analytic Real-Options framework for establishing optimal decisions in meeting retirement objectives (e.g. to maximise the fund balance at a particular age with x% probability of ruin or to minimise the probability of fund ruin before death).
4. To study the % of an individual’s salary contribution to super funds in order to limit their probability of ruin.
The main focus for the second stage of this project will on developing innovative financial products and analytic tools for Australian retirees. We will aim to implement a suite of prototype software tools that can be used to design new products and help people to plan for retirement. The tools will have the following functionalities.
Optimal timing to make decisions for and during retirement.
Optimal split ratio between lump sum and annuity pension according to individual circumstances.
Optimal asset allocation into different financial asset classes, including owner occupied and investment housing.
Building life cycle dynamic models that consider the health risk, longevity risk, consumption, housing, investment, and government policies such as compulsory superannuation contributions and age pension entitlements.
The other areas of interest covered in this project are:
1. Modelling of potential solutions to increase consumer demand for annuities (e.g. compulsory purchase of immediate or deferred lifetime annuities at a particular age) and investment strategies incorporating annuity purchases (e.g.
staged purchase of annuities within a portfolio).
2. To understand retirees behaviour and preferences for new and innovative products, such as annuity products.
3. The probability of ruin of an entire super fund consisting of multiple members, regarding expected withdrawal/contribution patterns, and expected entry/exit patterns of the fund’s members.
4. Optimal fund investment strategy to achieve objectives regarding probability of ruin– this question may also address the optimal fund strategy given past returns within the fund and their effect upon membership levels of the fund.
DELIVERABLES
A multi-factor Wilkie cascade model for predicting ruin years for retirement income. -Prototype computer program is ready for users to model various assumptions.
Technical report to detail the multi-factor Wilkie cascade model. -Technical report can be used as a manual.
Longitudinal data on Self-Managed-Super-Fund (SMSF) from ATO. -10% of all ATO data reaches CSIRO.
A multi-factor Wilkie cascade model for predicting ruin years for retirement income. -Prototype computer program is ready for users to model various assumptions.
An optimal portfolio decision tool is delivered in the form of prototype software tool (June 2015).
Activity 2: ANNUITY PRODUCTS
The focus of this activity is to design, develop and recommend new retirement income products in the Australian context.
Currently, the Australian Government pension is the basic safety net provided to all Australians whose income falls below certain thresholds. The superannuation industry and financial markets in general provide a large number of pre-retirement products: i.e. wealth accumulation super products. These accumulation style products in general satisfy the requirements of the working population. However, there are not enough post retirement products that cover a wide range of the requirements for the post retirement phase. For example, for a confident and comfortable retirement, medical costs and extra expenses from longevity should be part of any new post retirement products.
Currently for retirees, being eligible for a health card is an extremely desirable way to manage the ever increasing
medical expenses. Commercial post-retirement products can aggregate resources to manage the high medical cost and longevity risk, thus can alleviate for the government the cost burden associated with providing health cards and the pension as a safety net. As pointed out in recent literature, there is not enough awareness of longevity protection products among Australian retirees and there is very little incentive for Australian retirees to purchase life annuities given the seemingly high prices for these products. An examination of alternative forms of annuity that might meet older peoples’ aspirations or needs - here the role of guarantees, and of opportunities to convert annuities into lump sums to meet, for example catastrophic health or care costs are subjects of importance.
DELIVERABLES
Variable Annuities products with Guaranteed Minimum Death and Living Benefits (for rational policy holders and under the forecast for the policy holder future behaviour with respect to withdrawals).
Annuity style super products that provide cover for extra private health insurance in retirement, account for aged pension entitlements, and for transitioning to accommodation bonds in retirement villages.
Pricing algorithm for variable annuity with guaranteed minimum withdraws and/or guaranteed minimum death benefit. -Pricing algorithm is implemented and available for users.
Numerical methodology for pricing variable annuities with GMWB and GMDB is implemented successfully.-Conference/journal paper on the methodology is submitted.
Prototype annuity pricing software for estimating fair prices for annuity products.
Planned activity: PROFILING MEMBERS TO INFORM COMMUNICATION AND ENGAGEMENT STRATEGIES
Achieving more effective communication and engagement with superannuation fund members is vital to ensure that superannuants make informed decisions regarding their fund investments. However, the long time frames and perceived complexity involved in superannuation decisions mean that people are predisposed not to engage, and may put off making decisions indefinitely. This research stream will apply insights from psychology and behavioural economics to better understand how different individuals relate to superannuation and to develop strategies which take individual circumstances into account to enhance member engagement.
Individuals’ financial behaviour is strongly influenced by demographic and attitudinal factors such as perceptions of risk and self-efficacy. Thus, communication and engagement strategies can be enhanced by profiling fund members and exploring how they react to different types of message framing. Behavioural economics research provides guidance as to the types of communication and message framing strategies that are likely to be effective in engaging superannuation fund members with important information.
A questionnaire survey of the adult population can be used both to identify key demographic and attitudinal factors that are associated with superannuation-related behaviour and to trial potential communication strategies. We propose conducting an online survey that targets adults from a range of socio-economic and demographic backgrounds and assesses their attitudes towards employment, retirement, superannuation, the pension, personal finances and financial institutions.
These individual factors will be assessed in combination with their superannuation behaviours, such as:
How recently they have checked their superannuation balance
Whether they make voluntary contributions to their superannuation balance
Whether they have estimated the amount of money they will need in retirement
Whether they have switched between super funds or super fund options within the past 12 months
Whether they believe their superannuation balance will be adequate to meet their financial needs in retirement
Whether they belong to a self-managed and/or institutional superannuation fund
From these measures we will be able to determine how member characteristics relate to their superannuation behaviours, and thus, how to target communication and engagement initiatives so as to engage more effectively with the diverse population of Australian superannuants. For example, we can explore whether SMSF members are motivated by greater choice and flexibility of investments, or a greater sense of empowerment and self reliance, which can help superannuation funds design products which are likely to appeal to such people. The questionnaire will also be used to assess the efficacy of different message framing strategies in terms of promoting member engagement. Within the questionnaire, we can incorporate different types of
A better understanding of retirement can be applied to help identify the most appropriate asset classes for investment. For example, retirees may spend a greater proportion of their incomes on food and health than other households, so investing relatively more in these sectors might help to safeguard retirement living standards. Some sectors, such as primary production and energy, may serve to hedge against future cost of living increases. There may also be potential for people’s retirement savings to be invested in specific assets such as retirement accommodation. Given people are uncertain as to when and where they will retire, and what their needs will be, a superannuation fund could invest in a portfolio of such assets, offering members income but also access if and when they need it. A similar approach might be applied to residential aged care (nursing homes etc), though this is a heavily regulated sector at present.
A model will be developed using detailed data obtained from the Australian Bureau of Statistics, the Australian Taxation Office and Roy Morgan. The model will show how much money retired people spend, on what categories (e.g. housing, transport, recreation, health etc) and how this tends to vary with age and circumstance. The model will avoid the shortcomings associated with a one-size-fits-all approach by considering retiree circumstances and reporting ranges and distributions rather than just averages. Trends over time can be identified by comparing current data with previous surveys (which have been carried out regularly over at least 20-30 years) and demographic factors (e.g. changes in life expectancy, retirement age, partners’ ages etc). Examining differences in spending patterns between lower and higher income individuals and households will identify what lower income people are missing out on. We will also consider current and likely future health and residential aged care costs.
Schedules of Deliverables:
Database, model and accompanying report – December 2014
CSIRO website outlining research findings relating to financial needs in retirement – June 2015.
Planned activity: The Pension and Government Safety Net
There is a complex web of relationships between privately provided retirement savings (superannuation) and government provided welfare entitlements (the aged pension, healthcare concessions etc). Currently, the Australian government pension is essentially a basic safety net provided to all Australians whose income falls below certain thresholds. Individual decision-making around superannuation can only be understood in the context of these interactions, which have broader implications for retirement incomes and government expenditure. To identify the underlying drivers affecting the relationships between the government pension (health card) and retirement assets such as the family home, super and even bequest planning, we will focus on analysing data accessible from ATO, DHS, as well as ABS.
This project will begin by examining current legislation and identifying the incentives it creates for individuals to manage their affairs in order to optimise their overall returns. For example, income may be split among partners or deferred in order to access entitlements or tax concessions. While some of these responses are anticipated by policy makers, others are not. Data from the ATO and DHS will be used to examine how individuals have responded to previous policy changes.
Schedule of Deliverables:
Develop life cycle utility or simulation model to model retiree’s preferences of bequest, housing, consumption, health and age pension for the different groups of people.
An optimal decision support model will be developed to describe the optimal individual decision-making behaviour within this complex environment. The model will facilitate exploration of scenarios around future policy changes to determine how decision-making is impacted, taking into account interactions with other policies. It will also scale up individual decisions, based on demographic data, to estimate the overall impacts on government finances.
Database, model and accompanying reports – December 2015.
Commercial software for the prototype Life-cycle model is delivered (June 2015).
Planned activity: LIQUIDITY RISK FOR UNLISTED ASSETS
Super funds typically have only 5 – 10% invested in unlisted assets. These unlisted assets can provide higher, more stable returns. For example, infrastructure based illiquid assets are desirable superannuation assets because these assets can act as natural hedge against future inflations. However, the unlisted asset class also brings its challenges:
More accurate risk measure of this asset class in superannuation portfolio
Members can switch/withdraw at short notice
Valuation of unlisted assets
Appropriate limits for unlisted investments in a superannuation portfolio.
The research outcome in this stream is the delivery of risk and valuation models in the form of prototype software so that industry partners can use and customise the models and software for their own business requirements. Academia partners can use these models and prototype software to conduct experiment and to verify scenario forecasts.
This project will focus on the following tasks:
Analyse sparse data on historical performances of unlisted assets, the focus is on periods of economical crises and their correlation to member withdraws/switching. Unlisted assets are classified according to their risk features.
Construct a liquidity risk model by relying on the Value-at-Risk (VaR) approach as the risk measurement for unlisted assets in a portfolio for given rates of member withdraw/switching during a crisis.
The liquidity risk model can thus be used to analyse the limits for unlisted investment in a portfolio to meet the possible redemption from members during financial crisis.
For a more accurate valuation of unlisted assets, we can use a multi-factor model, and the data and analysis outcome of the sub-project of quantitative modelling decumulation. The data will be used as input to train and calibrate the multi-factor estimation model. This model can be relied upon to provide forecast for the values of unlisted assets in time of crisis and when members withdraw/switch from this asset classes. Importantly, such a valuation estimation model can be a valuable tool in the optimal portfolio management when substantial rebalancing is required during financial crisis.
Another possible extension of this project is to link this estimation model with the CSIRO RiskLab platform to identify optimal timing for exiting unlisted assets when their future valuation reaches some levels.
Schedule of Deliverables
Preliminary model specification (October 2014)
Code development of the multi-factor estimation model is completed (February 2015)
Testing and calibration of model is completed (May 2015)
A paper on the multi-factor model for predicting valuation is submitted for a conference/journal (July 2015)
Prototype software of the valuation forecasting model for unlisted assets is delivered (September 2015)
Customised modules of the multi-factor valuation forecasting model for unlisted assets are distributed to individual industry partners for trial and feedbacks (February 2016).
The multi-factor valuation forecasting model for unlisted assets is distributed as a commercial software to industry partners (June 2016).