Financial advisers tell us that limiting the dispersion of future investment outcomes in or near retirement is a key goal of income-oriented portfolios. Our analysis, however, shows that the equity risk factor contributes up to 99% of the total risk in many retirement portfolios, conflicting with investors’ objectives. Many Australian investors over-allocate to domestic income-generating securities, resulting in concentrated exposure to high dividend shares, property and hybrid securities. Tax advantages for local stock investors and the property boom have further fueled concentration risk. Fortunately, there are practical ways to construct portfolios that address these typical shortfalls in Australian investing for retirement income.

Retirement portfolios generally seek to produce attractive total return, preserve capital and provide income. The risk of capital loss stemming from market downturns is a major concern for retirees, who have a shorter time horizon to recover losses. Unsurprisingly, a consequence of over-allocating to equity and property is increased tail risk – the likelihood of extreme events and drawdowns. With this in mind, we have analysed portfolios based on expectations for total return, income and downside risk.

Risk/return characteristics of Australian income portfolios

To uncover the inherent risk factors in Australian retirement accounts, we modelled two investment portfoliosi:

  • The self-managed superannuation fund (SMSF) model
  • The retail superannuation fund model

The retail superannuation and SMSF models show vastly different risk characteristics. Based on our analysis, the SMSF model exhibits about double the volatility of the equivalent retail superannuation fund model and the conditional value at riskii (CVaR), a downside risk measure, is almost three times higher. The higher risk in the SMSF model compared to the retail superannuation model results from a larger allocation to stocks and property, and a reliance on cash instead of fixed income as a portfolio diversifier (see Figure 1).

The analysis of risk and estimated volatility shows that simple asset class diversification does not necessarily equate to risk factor diversification. This is especially the case if capital is allocated to highly correlated asset classes. For example, even though equity investments make up 39% in the SMSF model, the equity risk factor contributes 99% of total volatility, partly due to the interaction with property risk and the insufficient exposure to truly diversifying risk factors. Since the underlying drivers of property risk are essentially equity, illiquidity and idiosyncratic risk, in our view, property is not a reliable diversifier from equity risk.

Diversification of income sources: PIMCO solutions

A well-known facet of investing is that allocating across asset classes that demonstrate low to negative correlation with each other tends to improve a portfolio’s risk and return dynamics. Translating this to risk factors, adding or increasing allocations to investment strategies that have limited risk factor overlap with the existing portfolio is generally beneficial for the risk/return trade-off.

Compared with most retirement portfolios in other developed economies, numerous risk factors are underrepresented in Australian retirement and income portfolios. The SMSF and retail superannuation models lack exposure to duration, global credit spread and securitised credit risks, as well as alternative risk premia (such as the momentum risk factor captured by trend-following strategies). These risk factors, in the context of a retirement portfolio dominated by equity risk, can improve the risk/return trade-off. Additionally, the asset classes carrying these risk factors can offer desirable features that help achieve retirement objectives, including income-generation and a defensive bias.

PIMCO’s income-oriented solutions –Income Fund, Capital Securities Fund, Global Credit Fund and Australian Short-Term Bond Fund – offer a range of building blocks for Australian retirement portfoliosiii. Due to their unique mix of risk factors, these strategies tend to reduce dependence on the Australian stock and real estate markets (see Figure 2).

Each PIMCO solution can help to fill a gap of underrepresented risk factors in a particular retirement portfolio:

  • PIMCO Income Fund – As a true multi-sector fixed income offering, this fund has a diversified set of risk factors. They are driven mainly by securitised, investment grade credit and high yield spread, while the duration risk factor can serve as a diversifier.
  • PIMCO Australian Short-Term Bond Fund – This strategy offers a potentially lower risk alternative designed to provide modest defensive returns, with duration as the main risk driver.
  • PIMCO Global Credit Fund – A solution designed to offer marginally greater returns than core fixed income, whilst retaining defensive and diversifying features by demonstrating both duration and credit spread risk factors.
  • PIMCO Capital Securities Fund – While this fund offers a level of risk below but close to a typical equity allocation, the risk is composed predominantly of credit and high yield spread factors, which diversify favourably with equity risk.

Case study: PIMCO Income Fund in Australian retirement portfolios

PIMCO Income Fund became available to Australian investors in 2015 and the strategy has a successful offshore track record of more than 10 years. The strategy is a complement to but not necessarily a replacement for a core fixed income allocation. The Income Fund invests in a wide range of sectors of the bond market to meet income objectives and manages duration flexibly to help diversify overall risk factors. The strategy therefore has a low correlation to global bonds but also tends to have lower diversification benefits to equities than typical core bonds. With a global opportunity set of more than AUD 140 trillioniv, the global fixed income market provides plenty of opportunity to generate sustainable income.

This case study modifies the SMSF and retail platform models by adding a 15% allocation to the Income Fund, financed by an equal reduction of Australian shares and cash (see Figure 3).

Figure 4 shows the positive influence the Income Fund may have as a complement to multi-asset portfolios. These portfolios increase their expected returnv, as well as the level of income, and decrease estimated volatility and CVaR. As a result, the Sharpe ratio, a measure of portfolio efficiency, increases substantially for the retail superannuation model and increases to a lesser extent for the SMSF model. Due to its high carry, exposure to credit and securitised spread as well as interest rate duration, the Income strategy is especially appealing for retirement portfolios as it adds a range of risk factors outside of equity risk.


The optimal income portfolio

Using PIMCO’s capital market assumptions, as well as proprietary asset allocation tools developed by PIMCO’s quantitative research team, we have optimised the Australian retirement portfolio as a function of estimated return and tail risk (CVaR). We have chosen CVaR as opposed to volatility to consider the high desire of income investors to avoid large drawdowns. To model the optimal Australian income portfolio, Figure 5 uses the same asset classes as our SMSF and retail superannuation models and extends the investable universe with the Income

The efficient frontier in Figure 5 shows that an estimated return of 5% can be achieved with a risk budget of less than 5% CVaR. This risk/return trade-off may appear favourable to defensive investors compared with an estimated return of 6%, which requires a risk budget more than twice as high. The SMSF model is far below the efficient frontier and therefore too risky considering the level of expected return. As Figure 5 shows, including a 15% allocation to the Income Fund can drag both model portfolios closer to the efficient frontier.

Our optimization algorithm has a preference for the Income Fund given the low risk factor overlap with most other asset classes. Only if required returns are above 6% will the optimal allocation to the Income Fund fall below the highest possible weight of 30%. For an expected return of 5%, a sizable allocation of at least 20% to core bonds is necessary to place a portfolio on the efficient frontier. To achieve estimated returns of 6% or more, the optimization algorithm increasingly allocates to stocks, property and alternatives. The higher expected return, however, comes with a disproportionate increase in tail risk. As an example, the return expectation of 6.8% for Australian stocks, which is boosted by tax advantages for domestic investors, has a CVaR of more than 30%. This level of risk should be considered excessive for most income investors in or close to retirement.

Benefits of increased risk factor diversification in Australian income portfolios

Our analysis demonstrates that Australian income portfolios could benefit from more diverse risk factor exposures. It shows that both the SMSF portfolio and the average income-oriented retail superannuation portfolio could benefit from a broader allocation to global fixed income sectors.

PIMCO’s suite of funds in Australia offers access to a range of risk factors that are underrepresented in Australian retirement portfolios. In particular, we have demonstrated that an allocation to PIMCO income-oriented solutions can help reduce overall portfolio risk without necessarily compromising income and return objectives. This may help achieve retirement objectives by limiting the potential dispersion of investment outcomes.

Many investors understand the importance of risk factor diversification to mitigate tail risks and use a core bond allocation for this purpose. However, only a subset of investors uses the diversified opportunity set provided by the global bond market. A dynamic allocation to asset classes like government-related debt, mortgages, inflation-linked bonds, emerging markets debt, high yield and investment grade credit, could act as a good complement to government bonds and be a valuable building block for well-diversified income portfolios.

i To model the asset allocations, we used data from the Australian Tax Office (ATO) for SMSFs and an income model portfolio of a representative retail superannuation fund. Details of the assumed asset allocation are shown in Figure 7 in the appendix.
ii CVaR estimates the portfolio loss in a bad year (as defined with a probability one out of 20 years); please see appendix for details.
iii Please see Figure 6 for historical risk, return and correlation of PIMCO’s Income-oriented products.
iv Source: Bank for International Settlements (BIS) as of June 2016
v The expected return for Australian equities was adjusted upwards by 1.5 percentage points to capture expected franking credit benefits.
vi We have further constrained the allocation to each asset class by a range of 0% to 30% and have kept cash at a constant level of 5%.



These Composites were chosen because the strategy of the PIMCO Income and Capital Securities Funds is essentially identical to the strategy of the corresponding Composites. No guarantee is being made that the structure or the investments of the Funds will be the same as the investments underlying the Composites, or that similar returns to the Composite return will be achieved by the Fund.

Conditional Value at Risk (CVaR) estimates the risk of loss of an investment or portfolio over a given time period (one year in our analysis) under normal market conditions in terms of an average of loss after a specific percentile threshold of loss (95% in our analysis). Under the specific modeling assumptions used, the portfolio will incur an average loss in excess of the CVaR 5%, of the time. Different CVaR calculation methodologies may be used. CVaR models can help illustrate what future return or loss profiles might be. However, the effectiveness of a CVaR calculation is in fact constrained by its limited assumptions (for example, assumptions may involve, among other things, probability distributions, historical return modeling, factor selection, risk factor correlation, and simulation methodologies). It is important that investors understand the nature of these limitations when relying upon CVaR analyses.

Maximum drawdown is measured as the average of the distribution of maximum drawdowns across 15,000 simulated annual paths under normal market conditions. This number represents an expected peak-to-trough drawdown within a one-year time horizon.

For indexes and asset class models, return estimates are based on the product of risk factor exposures and projected risk factor premia which rely on historical data, valuation metrics and qualitative inputs from senior PIMCO investment professionals.

PIMCO employs highly granular holdings-based models to generate risk factor exposures. In our analysis, we may display aggregated risk factor data for ease of interpretation, but the granularity of the underlying models is maintained. For Alternatives/Illiquids and in selected cases where holdings information is unavailable or unreliable, PIMCO may use returns-based regression models to generate risk factor exposures.

The Sharpe Ratio measures the risk-adjusted performance. The risk-free rate is subtracted from the rate of return for a portfolio and the result is divided by the standard deviation of the portfolio returns.

A tail event is a portfolio outcome that is unpredictable and highly unlikely under the assumption that returns follow a normal distribution.

Total carry refers to the assumed total return a portfolio would potentially achieve over a three-month period provided that par rates, the option adjusted spread (OAS) of each security held in the portfolio and currency exchange rates remain unchanged. This hypothetical example also assumes no defaults are held in the account for the time period calculated. PIMCO makes no representation that any account will achieve similar results, and the statistical information provided as total carry in no way reflects the actual returns of any current PIMCO portfolio.

We employ a block bootstrap methodology to calculate volatilities. We start by computing historical factor returns that have underlain each asset class proxy from January 1997 through the present date. We then draw a set of 12-monthly returns within the dataset to come up with an annual return number. This process is repeated 25,000 times to have a return series with 25,000 annualized returns. The standard deviation of these annual returns is used to model the volatility for each factor. We then use the same return series for each factor to compute covariance between factors. Finally, the volatility of each asset class proxy is calculated as the sum of variances and covariance of factors that underlie that particular proxy. For each asset class, index, or strategy proxy, we look at either a point-in-time estimate or historical average of factor exposures in order to determine the total volatility. Please contact your PIMCO representative for more details on how specific proxy factor exposures are estimated.

The Author

Fabian Dienemann

Senior Account Associate

Manusha Samaraweera

Product Strategist

Related Funds



PIMCO Australia Pty Ltd
ABN 54 084 280 508
AFS Licence 246862
Level 19, 5 Martin Place
Sydney, NSW 2000

This publication is intended for general information of financial advisers and wholesale investors only.

This should not be passed on to retail investors.

Past performance is not a reliable indicator of future results. Interests in any PIMCO fund mentioned above are issued by PIMCO Australia Management Limited ABN 37 611 709 507, AFSL 487 505 of which PIMCO Australia Pty Ltd ABN 54 084 280 508, AFSL 246862 is the investment manager (together PIM