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CSIRO-Monash Superannuation Research Cluster


Research Report

The CSIRO-Monash Superannuation Research Cluster Newsletter
Issue 4, 8 October 2015

Update from the Cluster Leader
I am delighted to bring you the latest edition of the CSIRO-Monash Superannuation Research Cluster Newsletter. The Cluster is now almost two years old and the excellent work of all Cluster Project research teams is starting to come to fruition with six new working papers being delivered during so far this year. As many as ten further papers are scheduled for completion before the end of 2015.

In this edition we are also very pleased to report for the first time on the exciting work both completed and under way by the teams at CSIRO. These outputs, together with the six working papers mentioned above, are all listed in this Newsletter and I trust you will find the research findings of great interest.

As always, we welcome any feedback you may care to give on the research findings and would encourage you to email this to us at the contact details listed at the end of the Newsletter.

One of the great strengths of this Research Cluster is the active engagement between the research teams and the Stakeholders. Not only do the Stakeholders provide a generous financial contribution to the Cluster but they also provide an important sounding board for Cluster Project teams.

Our major Event for the year is the Annual Conference which will be held in Melbourne on the 1st and 2nd of December. More information on this event is contained later in this newsletter.

 
I am pleased to be able to tell you that we have recently gone ‘live’ with the standalone website for the Research Cluster. Most of the material on the website is freely accessible to the public and I would urge all readers to visit the site which can be found at: www.superresearchcluster.com

Finally, I should mention that I have recently concluded my term as Executive Director of the Australian Centre for Financial Studies and returned to Monash University as a Professor in the Department of Banking and Finance. I will be continuing as Cluster Leader for the Research Cluster, and ACFS will remain Project Manager.

I hope you enjoy this edition of the Newsletter.
 
Professor Deborah Ralston
Cluster Leader
CSIRO-Monash Superannuation Research Cluster

More Working Papers Released
Since our last Newsletter six new working papers have been delivered from the nine Cluster Project teams. In addition, several of the papers previously listed in the Newsletter have subsequently been accepted for publication in leading journals in their respective fields. A list of papers accepted for publication and the relevant journals will be included in our next edition.
 
The recent working papers are listed below, with links to the papers themselves on the Cluster website:
 
Retirement savings trajectories: An analysis of the experience of fund members. Part One: Experience
  Paul Gerrans, Maria Strydom, Carly Moulang, Jimmy Feng, Maurizio Fiaschetti, Gordon Clark
August 2015, Working Paper 29 (Cluster Project 3)

Since 1992, all employees in Australia have enjoyed a common condition of employment, namely an entitlement to payment of retirement savings contributions by their employer to a complying retirement savings (superannuation) fund. While this is a universal entitlement, individuals can alter their retirement savings trajectory, and ultimately their retirement standard of living, through choices they make, most notably through additional savings and the investment strategy applied to these savings. We are interested in the latter. Specifically, we are interested in the extent to which individual trajectories are influenced, or nudged, by demographic and social factors in retirement savings choices.
Superannuation within a Financial CGE Model of the Australian Economy
  Peter B. Dixon, James A. Giesecke, Maureen T. Rimmer
June 2015, Working Paper 20 (Cluster Project 6)

Australia’s superannuation sector has become both a major institution in guiding the allocation of the nation’s financial capital across asset classes, regions, and sectors, and a central intermediary in channelling the nation’s annual savings into domestic capital formation and foreign financial asset accumulation. The sector’s influence over the allocation of the nation’s physical and financial assets continues to grow. We model this important institution within an economy-wide setting by embedding explicit modelling of the sector within a model of the financial sector which is in turn linked to a dynamic multi-sectoral CGE model of the real side of the economy. We develop the financial CGE model by building on a multi-sectoral dynamic model of the real side of the Australian economy. In particular, we introduce explicit treatment of: (i) financial intermediaries and the agents with which they transact; (ii) financial instruments describing assets and liabilities; (iii) the financial flows related to these instruments; (iv) rates of return on individual assets and liabilities; and (v) links between the real and monetary sides of the economy.
Locus of Control and Savings
  Deborah A. Cobb-Clark, Sonja C. Kassenboehmer, Mathias G. Sinning
June 2015, Working Paper 49 (Cluster Project 9)

This paper shows that personality plays an important role in the level of wealth at retirement. We analyze the relationship between individuals’ locus of control and their savings behavior, i.e. wealth accumulation, savings rates, and portfolio choices. Locus of control is a psychological concept that captures individuals’ beliefs about the causal relationship between their own behavior and life events and is a key component of self-control. People who believe they can influence the direction of their own lives by the decisions they make, save more both in terms of levels and as a percentage of their permanent incomes. Although the locus-of-control gap in savings rates is largest among rich households, the gap in wealth accumulation is particularly large for poor households. Finally, people who believe they can influence the direction of their own lives by the decisions they make hold significantly less financial wealth, but significantly more pension wealth, than otherwise similar households.
A Longitudinal Analysis of Superannuation Outcomes: Gender Differences
  Jimmy Feng, Paul Gerrans, Noel Whiteside, Maria Strydom, Carly Moulang, Gordon Clark, Maurizio Fiaschetti
May 2015, Working Paper 24 (Cluster Project 3)

This research investigated the extent to which women’s superannuation savings fall behind those of men - and the main reasons why this happens, using longitudinal data based on a sample of member accounts provided by a major Australian superannuation fund. It was specifically interested to discover whether established gender-derived savings gaps are likely to diminish in the future. This is a report of work in progress: the findings at this stage remain tentative. Further investigations into the data are promised.
The social construction of retirement and evolving policy discourse of working longer
  Philip Taylor and Catherine Earl
February 2015, Working Paper 13 (Cluster Project 10)

This article is concerned with the evolving ‘social construction’ of older workers and retirement, drawing on the notion that the meaning of ageing derives not from innate biological processes but is socially determined. Evolving and competing ‘worldviews’ from current Australian public policy and social advocacy of productive and vulnerable older workers are described and critiqued. Contradictions and disjunctions in terms of public policies aimed at changing employer behaviour towards older workers are identified. We argue that present representations of older workers have serious flaws that provide a weak basis for policy development, and may not only undermine the prospects for overcoming prejudicial societal attitudes but may in fact strengthen them. It is argued that the mainstreaming of issues of older workers’ employment is justified, being cognisant of negative attitudes towards ageing but not sheltering them in employment placements that limit the extent and nature of their labour force participation.
These new working papers add to the substantial body of work previously acknowledged in the Newsletter. Copies of all deliverables are available on the Research Cluster website at:
http://www.superresearchcluster.com/projects-and-research-teams


Prototype Software Available from CSIRO

CSIRO is developing a suite of software models for applications in the superannuation area, including the Simulation of Uncertainty for Pension Analysis model (SUPA) which simulates the evolution of superannuation fund balances across time. The following software is now available to Cluster Stakeholders.
  • SUPA: Users can use the model to forecast the whole-of life retirement outcome.
  • SUPA_Retirement: Users can rely on the model to forecast post-retirement outcome.
  • Pricing Software for GMWDB
  • Pricing Software for Capital Guaranteed Investment Product.

Papers Published and Draft Papers Available from CSIRO
CSIRO has made available to the Cluster Stakeholders a number of the papers they will deliver under this project. These papers are available through the Cluster Web Portal. Their titles and abstracts of these papers are listed as follows.

ATO Data Analysis on SMSF and APRA Superannuation Accounts
  Zili Zhu, Thomas Sneddon, Alec Stephenson, Aaron Minney
September 2015, CSIRO, Theme 1: Government Data Analysis

Currently, over $500 billion of assets are controlled by SMSFs. To gain more insight into SMSFs, it is useful to compare SMSFs with conventional superannuation funds that are regulated by Australian Prudential & Regulatory Authority (APRA). The Australian Tax Office (ATO) provided CSIRO with a large dataset of individual and self-managed superannuation fund (SMSF) annual return information to facilitate the comparative analysis of APRA-regulated superannuation funds and SMSFs. The analysis of SMSF and APRA fund activity can shed light on how older Australians behave in relation to withdrawal, contribution and maintenance of their superannuation entitlements. This study represents the first time the original raw ATO return data has been used directly as evidence. In this report, we provide the outlines on the analysis of this ATO data, and also some insights into the behaviour of older Australians in relation to their superannuation fund entitlements.


Modelling retirement outcomes: a stochastic approach using Australia as a case study
  Thomas Sneddon, Zili Zhu, Colin O’Hare
CSIRO, Theme 2: Retirement Income Forecasting

In this paper we present a stochastic forecast model in a defined contribution pension system for projecting the accumulation and decumulation phases from an individual fund perspective. We use the Australian superannuation system as the context to demonstrate this “SUPA” (Simulation of Uncertainty for Pension Analysis) model. The SUPA model can be used to simulate the evolution of superannuation fund balances across time during the accumulation and decumulation phases. The model comprises four elements: (i) a stochastic projection of investment returns; (ii) a stochastic projection of income levels (upon which contributions to the fund are based); (iii) a projection of levels of withdrawal in retirement; and (iv) a stochastic projection of increasing longevity (life table). The combination of these four elements within the SUPA model is described in detail in this paper. The SUPA model can be used to model the potential impacts of any changes to a superannuation system.


Extension of SUPA Model to Unlisted Infrastructure Price Modelling
  Zili Zhu and Thomas Sneddon
March 2015, CSIRO, Theme 2: Retirement Income Forecasting

This report outlines a multi-factor stochastic model for forecasting unlisted infrastructure returns. This multi-factor model was developed by utilising and extending an established stochastic investment model in actuarial science, the Wilkie model. The model is extended here in this report to incorporate unlisted infrastructure return data. Results for back-testing the multi-factor model show that the accuracy of the implemented model is sufficient for the risk management objectives that are the focus of this study.


The impact on superannuation fund balances from the new compulsory superannuation rate
  Zili Zhu and Thomas Sneddon
September 2014, CSIRO, Theme 2: Retirement Income Forecasting

The federal government has announced an amendment to the compulsory superannuation minimum rates, delaying the rise in contribution rate to 12 per cent from 2019-20 to 2025-26. This study will use the new compulsory minimum rates to project and analyse the impact on likely fund balances of compulsory superannuation. The results presented here are produced by applying the CSIRO Simulation of Uncertainty for Pension Analysis Model (the “SUPA” model - the SUPA model has been developed to assist superannuation-related research within the CSIRO-Monash Superannuation Research Cluster). In this paper, we will use the SUPA model to study retirement outcomes under both the newly-legislated superannuation contribution rate regime and the previous schedule. These results will provide some insight as to the accuracy of recent statements made by relevant participants in the politically-charged superannuation debate.


Multi-period Dynamic Portfolio Optimization through Least Squares Learning
  C. Bao, Z. Zhu, N. Langrené And G. Lee
CSIRO, Theme 3: Retirement Investment Life-Cycle

This paper describes an algorithm to solve a dynamic portfolio selection problem. The portfolio selection problem is modelled as multiple switching problem, and a simulation-based numerical method is implemented for solving the dynamic portfolio optimization problem. A recursive numerical approach based on the Least Squares Monte Carlo method is used to calculate the conditional value functions of investors for a sequence of discrete decision dates. The methodology is data driven, is not restricted to specific asset models. Importantly, intermediate transaction costs associated with portfolio rebalancing is considered in the dynamic optimisation process. Investors’ risk preferences and risk management constraints are also taken into account in the current implementation. A case study is presented for a global equity portfolio invested in five equity markets, and foreign exchange risks are also included. The case study provides a numerical example of using the methodology for 8-dimensions.


Dynamic Portfolio Optimisation with Liquidity Cost: A Least-Squares Monte-Carlo Simulation Approach
  Rongju Zhang, Nicolas Langrené, Yu Tian and Zili Zhu
July 2015, CSIRO, Theme 3: Retirement Investment Life-Cycle

We propose a dynamic portfolio optimisation strategy that takes liquidity risk into account. Our liquidity cost model is based on the marginal supply-demand curve model for the limit order book, which models asset prices as a function of the quantity traded. To numerically compute our asset allocation strategy in practice, we extend the least-squares Monte Carlo algorithm to stochastic control problems with switching costs and endogenous variables. This simulation approach is very versatile, and can deal with very general asset dynamics, many different assets, and different risk preferences, making it suitable for portfolio selection in practice. We benchmark our dynamic strategy to several alternative portfolio strategies, based on the distribution of final wealth. Overall, our dynamic strategy outperforms other standard asset allocation strategies, especially in less liquid markets.


Optimal Life-cycle Investment Strategies for Post-retirement
  Thomas Sneddon, Chenming Bao and Zili Zhu
CSIRO, Theme 3: Retirement Investment Life-Cycle

Australia has a compulsory defined contribution retirement provision system, whereby employers must contribute a proportion of the pre-tax salary of their employees towards an individual account which cannot be accessed until retirement except in extraordinary circumstances. These funds are generally invested in a portfolio of financial assets from which the retiree may draw throughout retirement. Retirees under this system face two key problems when making investment and withdrawal decisions regarding this portfolio. Firstly, retirees must manage their superannuation investment portfolio to maximise their risk-adjusted returns and thereby best financially provide for their own retirement. Secondly, retirees must optimise their withdrawal pattern from the superannuation account throughout retirement so as to maximise their post-retirement lifetime utility given the need to minimise the risk of portfolio ruin prior to death.


A State-Space Estimation of the Lee-Carter Mortality Model and Implications for Annuity Pricing
  M. C. Fung, G.W. Peters and P. V. Shevchenko
July 2015, CSIRO, Theme 3: Retirement Investment Life-Cycle

A common feature of retirement income products is that their payouts depend on the lifetime of policyholders. A typical example is a life annuity policy which promises to provide benefits regularly as long as the retiree is alive. Consequently, insurers have to rely on “best estimate” life tables, which consist of age-specific mortality rates, in order to price these kind of products properly. Recently there is a growing concern about the accuracy of the estimation of mortality rates since it has been historically observed that life expectancy is often underestimated in the past (so-called longevity risk), thus resulting in longer benefit payments than insurers have originally anticipated. To take into account the stochastic nature of the evolution of mortality rates, Lee and Carter (1992) proposed a stochastic mortality model which primarily aims to forecast age-specific mortality rates more accurately.


Forecasting Leading Death Causes using Extended CreditRisk+
  P.V. Shevchenko, J. Hirz and U. Schmock
July 2015, CSIRO, Theme 3: Retirement Investment Life-Cycle

Recently we developed a new framework in Hirz et al. (2015) to model stochastic mortality using extended CreditRisk+ methodology which is very different from traditional time series methods used for mortality modelling previously. In this framework, deaths are driven by common latent stochastic risk factors which may be interpreted as death causes like neoplasms, circulatory diseases or idiosyncratic components. These common factors introduce dependence between policyholders in the annuity portfolios or between death events in population. This framework can be used to construct life tables based on mortality rate forecast. It also provides an efficient, numerically stable algorithm for an exact calculation of the one-period loss distribution of annuities or life insurance products portfolios and associated risk measures such as value-at-risk and expected shortfall required by many regulators. Moreover this framework allows stress testing and, therefore, offers insight into how certain health scenarios influence annuity payments of an insurer. Such scenarios may include improvement in health treatments or better medication. In this paper, using publicly available data for Australia, we estimate the model using Markov chain Monte Carlo method to identify leading death causes across all age groups including long term forecast for 2031 and 2051. On top of general reduced mortality, the proportion of deaths for certain causes has changed massively over the period 1987 to 2011. Our model forecasts suggest that if these trends persist, then the future gives a whole new picture of mortality for people aged above 40 years. Neoplasms will become the overall number-one death cause. Moreover, deaths due to mental and behavioural disorders are very likely to surge whilst deaths due to circulatory diseases will tend to decrease. This potential increase in deaths due to mental and behavioural disorders for older ages will have a massive impact on social systems as, typically, such patients need long-term geriatric care.


Modelling Annuity Portfolios and Longevity Risk with Extended CreditRisk Plus
  Jonas Hirz, Uwe Schmock, and Pavel V. Shevchenko
CSIRO, Theme 3: Retirement Investment Life-Cycle

Using an extended version of the credit risk model CreditRisk+, we develop a flexible framework to estimate stochastic life tables and to model credit, life insurance and annuity portfolios, including actuarial reserves. Deaths are driven by common stochastic risk factors which may be interpreted as death causes like neoplasms, circulatory diseases or idiosyncratic components. Our approach provides an efficient, numerically stable algorithm for an exact calculation of the one-period loss distribution where various sources of risk are considered. As required by many regulators, we can then derive risk measures for the one-period loss distribution such as value at risk and expected shortfall. Using publicly available data, we provide estimation procedures for model parameters including classical approaches, as well as Markov chain Monte Carlo methods. We conclude with a real world example using Australian death data. In particular, our model allows stress testing and, therefore, offers insight into how certain health scenarios influence annuity payments of an insurer. Such scenarios may include outbreaks of epidemics, improvement in health treatment, or development of better medication. Further applications of our model include modelling of stochastic life tables with corresponding forecasts of death probabilities, death rates and demographic changes.


Variable Annuity with Guaranteed Minimum Withdrawal Benefit (GMWB): surrender or not, that is the question
  X. Luo and P.V. Shevchenko
July 2015, CSIRO, Theme 4: Retirement Product Design

In this paper we extend our algorithm to include surrender option in GMWB and compare prices under different policyholder strategies: optimal, static and bang-bang. Results indicate that following a simple but sub-optimal bang-bang strategy does not lead to significant reduction in the price or equivalently in the fee, in comparison with the optimal strategy. We also observed that the extra value added by the surrender option strongly depends on volatility and the penalty charge, among other factors such as contractual rate, maturity and interest rate etc. At high volatility or at low penalty charge, the surrender feature adds very significant value to the GMWB contract - the required fair fee is more than doubled in some cases; thus it is critical to account for surrender feature in pricing of real products. We also performed calculations for static withdrawal with surrender option, which is the same as bang-bang minus the “no-withdrawal” choice. We find that the fee for such contract is only less than 1% smaller when compared to the case of bang-bang strategy, meaning that the “no-withdrawal” option adds little value to the contract.


Valuation of capital protection options
  Xiaolin Luo and Pavel V. Shevchenko
August 2015, CSIRO, Theme 4: Retirement Product Design

This paper presents numerical algorithm and results for pricing a capital protection option offered by many asset managers for investment portfolios to take advantage of market growth and protect savings. Portfolio with capital protection option has the basic features of a variable annuity with Guaranteed Minimum Accumulation Benefit plus arbitrary withdrawals at regular intervals and anniversary reset of the protected capital to the portfolio value if the latter is higher. Withdrawal above a certain threshold level will attract a penalty in the form of extra reduction in the protected capital. Under optimal withdrawal policyholder behaviour the pricing of such a product is an optimal stochastic control problem for controlled Markov process that cannot be solved using Monte Carlo method.


Valuation of Variable Annuities with Guaranteed Minimum Withdrawal and Death Benefits
  Xiaolin Luo and Pavel V. Shevchenko
February 2015, CSIRO, Theme 4: Retirement Product Design

In this paper we present a numerical valuation of variable annuities with combined Guaranteed Minimum Withdrawal Benefit (GMWB) and Guaranteed Minimum Death Benefit (GMDB) under optimal policyholder behavior solved as an optimal stochastic control problem. This product simultaneously deals with financial risk, mortality risk and human behavior. We assume that market is complete in financial risk and mortality risk is completely diversified by selling enough policies and thus the annuity price can be expressed as an expectation. The computing engine employed to solve the optimal stochastic control problem is based on a robust and efficient Gauss-Hermite quadrature method with cubic spline.


Fast Numerical Method for Pricing of Variable Annuities with Guaranteed Minimum Withdrawal Benefit
  Xiaolin Luo and Pavel V. Shevchenko
June 2015, CSIRO, Theme 4: Retirement Product Design

In this paper we present a very efficient new algorithm for pricing these contracts in the case when the transition density of the underlying asset between withdrawal dates or its moments are known. This algorithm relies on computing the expected contract value through a high order Gauss-Hermite quadrature applied on a cubic spline interpolation. Numerical results from the new algorithm for a series of GMWB contract are then presented, in comparison with results using the finite difference method solving corresponding PDE. The comparison demonstrates that the new algorithm produces results in very close agreement with those of the finite difference method, but at the same time it is significantly faster; virtually instant results on a standard desktop PC.


Behavioural Economics and Digital Services
  Dr Andrew Reeson and Dr Claire Mason
CSIRO, Theme 5

This new project will examine behavioural issues associated with the delivery of automated financial services. It will explore where and how digital channels are most effective, and also where human agents as alternatives or complements to digital technology. The broader implications for financial service delivery in the digital economy will also be discussed. The project will also consider behavioural aspects of the drawdown phase.

 

Research Cluster website
The standalone website for the CSIRO-Monash Superannuation Research Cluster finally went ‘live’ in June this year. We are grateful to the eSolutions team at Monash University and their contracted web developers, Squiz, for getting us to this stage. The website can be found at: http://www.superresearchcluster.com/ and can also be reached via hotlink from the ACFS website.

The site provides a login capability through which authorised users (such as Stakeholders and cluster researchers) have access to relevant research material and findings that are not available in the public domain.

A major purpose of the dedicated website is to encourage and facilitate communication and collegiality between everyone involved in the various Cluster Projects, both within and across the project teams and the Stakeholders and their nominated users.

As we are keen to ensure the website includes the features that users will find useful, readers are invited to pass on any suggestions to the Project Manager, Richard Dillon at richard.dillon@australiancentre.com.au.

Third Annual Conference
Each year the Research Cluster conducts an Annual Conference in December. As the project nears the end of its second year and the number of research outputs continues to grow, it has been decided that this year’s event - scheduled for 1st and 2nd December – should be expanded to include a broader presence including public and media. It is increasingly important that the wonderful work of all our researchers be effectively disseminated to the broader community including industry participants, regulators (government) and the public.

This year’s Conference will be divided into two distinct parts. The first day’s program will be structured to showcase the best of the research outcomes from the last two years. The second day will revert to the formula employed so successfully in both 2013 and 2014: all researchers and representatives of the Stakeholders will be invited to attend and presentations will be given by each of the research Clusters, focussing on their very latest research results and papers.

Further details will be provided within the next few weeks and interested readers should refer to the Cluster website to keep up-to-date with arrangements.
 
Quick Links 
Cluster Homepage

Research Projects

Upcoming Events

 
Third Annual Conference
1-2 Dec 2015
End-of-year Festive Cocktails 
2 Dec 2015
 
The Cluster Research Program

Contact Us


Prof Deborah Ralston
Cluster Leader
T: +61 3 9666 1010 
email
australiancentre.com.au

Dr Richard Dillon
Project Manager
T: +61 3 9666 1014
M: +61 412 102 224
email
australiancentre.com.au
 
Dr Andrew Reeson
Organisational & Socio-Economic Sciences
DATA61 | CSIRO
T: +61 2 6216 7323
email
www.csiro.au/super 
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About the CSIRO-Monash Superannuation Research Cluster
The CSIRO-Monash Superannuation Research Cluster is a collaboration between CSIRO, Monash University, Griffith University, the University of Western Australia, the University of Warwick, and stakeholders of the retirement system in the interest of better outcomes for all. 

The Cluster Consortium 


In association with industry leading stakeholders and research partners
Superannuation Cluster industry leading stakeholders and research partners

The Cluster would also like to acknowledge the support of major industry bodies and government departments, including the Australian Institute of Superannuation Trustees (AIST), Association of Superannuation Funds of Australia (ASFA), Australian Prudential Regulation Authority (APRA), Australian Taxation Office (ATO), the Financial Services Council (FSC) and the Productive Ageing Centre, National Seniors Australia.