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1 Zili Zhu, Thomas Sneddon, Colin O Hare, Chenming Bao, Alec Stephenson, Xiaolin Luo and Pavel Shevchenko Using ATO Data to Understand How SMSF & APRA Funds Perform During Retirement Phase
2 Using ATO data to establish what people do with their superannuationinretirementin retirement For SMSF and APRA funds during retirement phase, to understand: o Funds performance o Member behaviour: contribution, withdrawal, closure. Outcome from the ATO data analysis as input to Simulation of Uncertainty t for Pension Analysis (SUPA): forecasting ruin years Rely on stochastic dynamic decisions tools to plan for optimal superannuation life-cycle. Fast pricing algorithms for innovative products such as capital protected, income guaranteed and death benefits.
3 Frequency Details of the ATO data o Valid lddata points for people (out of samples). o, gender, SMSF account balance, SMSF contributions, SMSF earnings, SMSF withdrawals, APRA account balance, APRA contributions. o 10 year span from 2004 to 2014, between years of age o 27% SMSF only, 39% APRA only, 34% both SMSF&APRA o Rounded to the nearest $1000, or to the nearest $10000 for values above $ Values are censored for above $ SMSF Data Frequen ncy APRA Data
4 Super accounts balances Only using non zero balances to calculate o SMSF: Median $440000, mean value $ o APRA: Median $48000, mean value $ Including zero balances to calculate o SMSF: Median $330000, mean value $ o APRA: Median$29000, meanvalue $ Balances Dolla ars 10^3 10^4 10 0^5 10^6 SMSF APRA
5 Balances of SMSF/APRA Funds for Groups SMSF Balances APRA Balances Dollars Dollars
6 Median averages of super fund performances Super fund performances when members contributions are also counted in: Performance rate per year: Performance rates at different age: Average Logdiff SMSF APRA g bt bt 1 Average lo SMSF APRA Calendar Year
7 1. Account balances change during 2006 and 2013 SMSF Balances 2006 APRA Balances 2006 Dollars Dollars SMSF Balances 2013 APRA Balances 2013 Do ollars lars Dol
8 APRA Accounts from 2006 and 2009 APRA Balances 2007 Dollars Dollars APRA Balances
9 SMSF Accounts from 2007 and 2009 SMSF Balances 2007 Dollar rs SMSF Balances Dollars
10 APRA Accounts from 2007 and 2009 APRA Balances 2007 Dollar rs APRA Balances Dollars
11 APRA Accounts for 2011 and APRA Balances 2011 Dollars Dollars APRA Balances
12 SMSF Accounts for 2011 and SMSF Balances 2011 s Dollar Dollars SMSF Balances
13 Fund performances excluding member contribution The median average: Performance rate per year: Balance ratio moves with age: c t b t 1 Average b t SMSF APRA c t b t 1 Average b t SMSF APRA Calendar Year
14 SMSF & APRA Performances SMSF APRA SMSF APRA SMSF APRA bt ct bt bt c t bt bt c t bt Average b Average b Average b SMSF APRA SMSF APRA SMSF APRA Average bt ct bt Average bt ct bt Average bt ct bt t SMSF APRA Average bt ct bt SMSF APRA Average bt ct bt
15 SMSF Withdraw Rates For Groups SMSF Withdrawals s SMSF Withdrawals s F requency F requency Withdrawal Proportion SMSF Withdrawals s Withdrawal Proportion SMSF Withdrawals s 75+ Frequency Frequency Withdrawal Proportion Withdrawal Proportion
16 SMSF withdrawals according to account balances 0% 20% 40% 60% 80% 100% Medium Balance rawal Proportion Average Withdrawal Proportion Very Low Balance High Balance Average Withdr Average Withdrawal Proportion Average Withdrawal Proportion Low Balance Very High Balance Averag ge Withdrawal Proportion Averag ge Withdrawal Proportion
17 Fund Closure Rates for SMSF & APRA SMSF Closure Data (SMSF Only) APRA Closure Data (APRA Only) Proportion Proportion SMSF Closure Data (Both Only) APRA Closure Data (Both Only) Proportion Proportion
18 Decumulation Phase: Using SUPA model for simulation The SUPA model projects long-term economic behaviour: applies established stochastic asset model (Wilkie model) Output from SUPA is used for Monte Carlo simulation: i l any number of paths of desired time span Additional models extend SUPA: models for withdrawals, death probability and fund entry/exit behaviour
19 2. SUPA can answer questions in decumulation phase Member s questions: Probability of ruin of individual s super (before x years or before death) $ needed in super fund at retirement (to last for x years or until death) % of salary that should be contributed to fund to achieve some objective Optimal investment strategies to reach $ amount or avoid ruin Annuity related purchase/investment strategies Fund level questions: Probability ofruin ofanentire groupfund $ needed in fund at given time (liquidity risk) % aggregate fund balance that fund should collect Optimal investment strategy (from fund mgmt POV)
20 2. Front page of SUPA Model Output
21 Example: Impact of altered mandatory contribution rate schedule (September 2014) Assumptions: Starting annual expense $42, Asset Allocation (constant) To right Mandatory contribution rate Australian Equities International Equities 35.00% 25.00% schedule for comparison is pre- Domestic Bonds 12.00% International Bonds 8.00% September schedule vs new Cash 20.00% schedule (see below) Starting 25 Starting Salary $54,425 YEAR Pre % 10% 10.5% 11% 11.5% 12% 12% 12% 12% 12% 12% 12% New 9.5% 9.5% 9.5% 9.5% 9.5% 9.5% 9.5% 10% 10.5% 11% 11.5% 12%
22 Example: Impact of altered mandatory contribution rate schedule (September 2014) Statistic Old CR structure New CR structure Difference Average fund length postretirement (years) Standard deviation of fund length post-retirement ti t Probability of super funds ruin before death 23.49% 26.00% +2.51% Expected number of years between ruin and death Fund balance at retirement at t65( (current t$) $781, $749, $32, Fund balance at retirement at 65 (nominal $) $2,160, $2,071, $89, Fund balance at mid-2025 (current $) $87, $77, $10, Fund balance at mid-2025 (nominal $) $115, $102, $13,892.20
23 Using the current withdraw rate, we can expect Withdrawal multiplier OUTPUT STATISTICS Average fund length Std deviation of fund length r(ruin) 10.78% E(ruin yrs)
24 Using Median Balance at 65 and real withdraw rates Retirement age level set? 1=YES l l? Retirement age level (if above = YES) Withdrawal multiplier OUTPUT STATISTICS OUTPUT STATISTICS Average fund length Std deviation of fund length Pr(ruin) E(ruin yrs) ( y ) Risk Analytics, Real Options and Optimal Decisions 1 $ 480, % Retirement age level set? 1=YES Retirement age level (if above = YES) Withdrawal multiplier OUTPUT STATISTICS OUTPUT STATISTICS Average fund length Std deviation of fund length Pr(ruin) E(ruin yrs) 1 $ 480, %
25 Dynamic Portfolio Analytics for Superannuation Life Cycle Objectives: o Link the empirical research findings to strategic financial a planning; o Generic/flexible multi-period dynamic portfolio optimization engine: Simulation based & data driven; Asset/liability model free; Provide recommendation of fund management decisions.
26 Prototype Software: RiskLab
27 Result: multi period vs CAPM Blueline line: interpolated efficient frontier of a CAPM style investment Red line: multi period optimization Mathematical & Information Sciences
28 Example: Visualization of Dynamic Decisions Dynamic solution display through motion plots; Interactive for decision makers; Intuitive illustration of future investment decisions in an uncertain ti environment.
29 4. Example: hedge inflation for post retirement phase o An post retirement client: Invested in 2 equity markets: domestic & international; A dynamicwithdraw pattern subject to CPI; Some income protection products to hedge the risks, for example, an CPI linked annuity product. o Objective: find optimal strategy to maximise the total expected utility function for the investor.
30 4. Dynamic Portfolio Decisions Simulation scenarios: Optimal strategies:
31 Innovation for Protected Capital, Income and Death Benefit Variable annuities with guarantees (GMWB, GLWB, ) also marketed as various wealth protection/guarantee retirement products Guarantee to return the entire initial investment through cash withdrawals during contract or guarantee of income (even for market downturns when investment account depleted) Returns remaining portfolio at maturity (gain from market ktupside) Withdrawals can be fixed or flexible subject to penalty if contract rate is exceeded or contract is terminated earlier Fixed term or lifetime contract duration Death D hbenefit can be added d for extra fee but cheaper than separate life insurance Sales of Variable ibl Annuities ii in USA, Source: Milliman Q3 2014
32 3. Market for advanced products and challenges USA, Japan and UK: significant market for variable annuities (e.g. $150b in US 2013) In I July 2014, Forethought htlaunched dforeretirement tfoundation VA: GLWB with fees increase/decrease based on 10 year Treasure rate. Australia market early stage: e.g. MLC protected wealth and protected income products; AMP North Protected Retirement guarantee. Pricing sophisticated products with equity risk, interest rate risk, systematic mortality risk and human behaviour is a difficult numerical/mathematical problem especially in the case of flexible/dynamic withdrawals. Existing products are limited by numerical difficulties; e.g. industry practice is to use Monte Carlo forward simulation while correct pricing for flexible withdrawals requires backward solution for optimal control problem. Quoted academic literature computing time is hours per price in the case of flexible withdrawals. Also, over simplistic stochastic ti models dl( (e.g. lognormal lwalk) are used. Some industry approaches restrict product features, underestimate risks or overpricing. E.g. assumption of pre determined withdrawals, Normal distribution for risky asset returns, deterministic interest rate, constant volatility, mortality from simple life table (ignoring systemic mortality risk)
33 ir fee (bp) Fa pricing engine for advanced retirement products Pricing combined death and minimum withdrawal guarantees with flexible withdrawals engine capability: pricing accurately and in real time; can be used to develop new wealth retirement products, portfolio risk management and hedging/trading strategies for competitive advantage Pricing different guarantee features mathematics is similar to pricing exotic options: exotic pricing ii engine Rdit Reditus for FX exotics showcase application static/fixed withdrawal optimal withdrawal (10% penalty) optimal withdrawal (5% penalty) 0 5% 7% 9% 11% 13% 15% minimum guaranteed withdrawal rate (%) Australia data: life table and interest rate curve, male 60 years old, 20% volatility t (bp) benefit payment Quarterly death GMWDB, male separate life insurance, male 0 5% 7% 9% 11% 13% 15% minimum guaranteed withdrawal rate (%)
34 Summary o o o o o o Use ATO data to have a good overview of the superannuation landscape: Performances Contributions Withdraws Closures High quality data is needed to answer more questions. In negotiation with DHS to receive data. SUPA can be used to forecast economic scenarios and super outcomes. Dynamic and Optimal Decisions analytics for Superannuation Life- Cycle planning. Innovative products with features for protected capital, income and death benefit.
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