A closer look at the diversification potential of private assets
Private assets can enhance multi-asset portfolios. However, careful risk analysis is needed.
March 2024, From the Field
Key Insights
- A growing number of investors are seeking to enhance return potential and improve diversification by including allocations to private assets in their portfolios.
- We have developed a framework for evaluating private assets alongside publicly traded assets that we think better reflects their risks compared to reported data.
- Our analysis suggests that private assets can be return enhancers in a portfolio. However, their diversification and risk reduction potential can be overstated.
Written by
Rob Panariello, CFA, CAIA, FRM
Associate Director of Research, Multi-Asset
Som Priestley, CFA
Head of Multi‑Asset Solutions North America
Fahad Siddiqui, PhD, CFA, CQF, CAIA
Quantitative Investment Analyst
Private assets—including private equity, private real estate, and private credit—represent an increasingly large and growing portion of the global investment universe. As their popularity continues to rise, many clients are asking us for our views on the potential benefits of incorporating private assets in diversified portfolios.
Private assets often have unique characteristics that differentiate them from publicly traded investments, creating potential opportunities to improve the overall risk/return profile of a multi-asset portfolio. However, combining public and private assets in a portfolio can present a difficult challenge for portfolio construction—particularly when it comes to modeling the appropriate allocation weights for both categories.
To assist investors in this effort, T. Rowe Price has developed a comprehensive process for evaluating private assets alongside public investments, one that can be used consistently across portfolio construction projects. Using this framework, we analyzed the historical performance of three key private asset categories, taking into account the significant differences between the way private and public asset indexes are constructed, how their returns are calculated, and how frequently those returns are reported.1
Based on this analysis, we examined the impact of incorporating a 20% allocation to private assets in a hypothetical 60/40 U.S. public stock/bond portfolio. We found that while private assets would have improved the portfolio’s risk/return profile, the optimal allocations were extremely sensitive to the assumptions used—including the assumed ability of private asset managers to generate positive excess returns. Under these conditions, manager selection becomes an especially critical step in the portfolio construction process.
An expanding opportunity set
For many investors, the private asset universe has become too big to ignore. While traditional, publicly traded equity and fixed income assets represented USD 151.5 trillion in global market capitalization at the end of the third quarter of 2023, alternative assets totaled over USD 27 trillion, J.P. Morgan reports.2
What’s more, the private universe has expanded at a much faster pace, with an annualized growth for global market capitalization of 17.7% for private equity and 16% for private fixed income since 2005 versus just 5.1% for public equity and fixed income.2
Comparing private and public assets
Historically, many private asset categories offered attractive return potential, driven by factors such as illiquidity premia, deal-sourcing advantages, and lower market transparency. Reported volatilities in many private asset categories also were lower than for their public market counterparts, and their low reported equity correlations appeared to create attractive diversification opportunities.
However, unlike indexes for public assets—which typically only include investible securities and have transparent guidelines for index construction—private asset indexes represent an aggregation of active manager results (often self-reported), with meaningful dispersion between the highest and lowest performers. This dynamic can make it difficult to separate alpha from beta.
Additionally, reported returns on private assets also are usually cash flow-based (e.g., represent internal rates of return), require appraisals of the underlying investment values (not market prices), and typically are only reported quarterly.
Smoothed, appraisal-based valuations and a lack of mark-to-market pricing can produce reported performance results that are not comparable with public assets. Some investors welcome this smoothing effect as it reduces apparent maximum drawdowns and can mitigate negative behavioral reactions. However, from a multi-asset perspective, the smoothed results do not accurately represent the actual volatility and correlation characteristics of private investments. We find evidence for this in several places.
- Analysis over longer holding periods washes away the impact of smoothing and increases comparability. Private-equity volatility, for example, was comparable with U.S. large-cap equity over rolling one-year periods and higher over rolling three-year periods (Figure 1).
- Many private investments can be sold on the secondary market, and these transactions may provide market-based prices that are more reflective of their intrinsic value. This can be observed in the discounts to net asset value (NAV) required to sell private investments in secondary markets, particularly in periods of market stress.
- During the 2008 global financial crisis, some private-equity investors were forced to sell assets at up to a 60% discount to NAVs, which for the most part had not been marked down. While this may have been due in part to lower secondary market liquidity, it does suggest that the intrinsic value of these assets was lower than their reported value.
Observed private asset volatility consistently appears artificially low when compared with similar public assets. To directly compare the two, then, we need to adjust reported returns to estimate the “true” risk of private assets. We can do this by taking into account the artificial smoothness of appraisal-based prices. Without such adjustments, private assets will tend to dominate multi-asset portfolios in a traditional mean-variance optimization exercise.
Adjusting risk estimates on private assets
Given that private assets rely on appraisal-based valuations while comparable public assets typically are marked to market daily, price movements for public assets tend to be reflected in private assets but only with a time lag—an impact that can persist over several quarters and that also tends to smooth reported returns.
This tendency can create high levels of autocorrelation in private asset returns, meaning there is a strong positive relationship between the return in the current quarter and the return in the prior quarter and a likelihood of a similarly strong relationship in subsequent quarters (Figure 2).
While smoothed returns and lower reported risk can make private assets look highly attractive on first impression, we think it’s prudent to adjust for smoothness and autocorrelation to arrive at their “true” risk characteristics—i.e., estimates that are more comparable with public assets. One common technique adjusts each quarter’s reported return for a private asset to reflect a weighted average of the “true,” unobserved return in that quarter and the return in the prior quarter. 3
While this technique adjusts for much of the smoothing in reported volatility, we prefer an even more robust approach that unsmooths returns based on optimal lag lengths, rather than a single lagging period. There are several reasons for this.
- Because of the appraisal-based nature of many private asset valuations, the impact of autocorrelation can persist for more than just one quarter. Private real estate indexes, for example, are only required to update property values on an annual basis. This produces an optimal lag length of four quarters for that asset class (Figure 3).
- Other private asset classes also have their own intrinsic characteristics and therefore different lag structures, which we think should be taken into account.
- By unsmoothing private asset return data with our more targeted approach, we think we can obtain more accurate measures of true economic risk. Once we do this, we typically find that the volatility of private assets is roughly comparable with their public asset counterparts (Figure 4).
Measurement over longer periods can reveal higher volatility
(Fig. 1) Annualized volatility by rolling holding period
June 2005 through June 2023.
Sources: Cambridge Associates LLC, Standard & Poor’s (see Additional Disclosures). All data analysis by T. Rowe Price. See the appendix for a list of representative benchmarks for the asset classes shown.
Autocorrelation tended to smooth private asset returns
(Fig. 2) Average correlation between returns in consecutive quarters
June 2005 through June 2023.
Past performance is not a reliable indicator of future performance.
Sources: Cambridge Associates LLC, Standard & Poor’s, National Council of Real Estate Investment Fiduciaries (NCREIF), Wilshire Associates, Cliffwater LLC, Morningstar (see Additional Disclosures). All data analysis by T. Rowe Price. See the appendix for a list of representative benchmarks for the asset classes shown.
Autocorrelation effects may persist over multiple quarters
(Fig. 3) Optimal lag periods for different private assets*
June 2005 through June 2023.
Sources: Cambridge Associates LLC, NCREIF, Cliffwater LLC (see Additional Disclosures). All data analysis by T. Rowe Price. See the appendix for a list of representative benchmarks for the asset classes shown.
*Optimal lag period defined as the number of quarters beyond which the autocorrelation coefficient drops below a 95% confidence interval, using partial autocorrelation.
Unsmoothing revealed risk characteristics comparable with public assets
(Fig. 4) Reported and adjusted annualized volatilities and public equity correlations
| Asset Class | Reported Total Return | Reported Volatility | Adjusted Volatility | Reported Correlation to Public Stocks | Adjusted Correlation to Public Stocks | Reported CVaR (95%)† | Adjusted CVaR (95%)† |
| Private Equity | 14.6% | 9.5% | 16.3% | 0.8 | 0.8 | -10.3% | -20.0% |
| Private Real Estate | 7.7 | 5.3 | 17.8 | 0.1 | 0.4 | -7.0 | -28.1 |
| Private Credit* | 9.1 | 3.6 | 5.3* | 0.7 | 0.7 | -4.3 | -6.9 |
June 2005 through June 2023.
Past performance is not a reliable indicator of future performance.
Sources: Cambridge Associates LLC, NCREIF, Cliffwater LLC (see Additional Disclosures). All data analysis by T. Rowe Price. See the appendix for a list of representative benchmarks for the asset classes shown and additional detail on the methodology for the adjusted results.
*The low unsmoothed volatility for private credit could be explained by the fact that there is no mark-to-market for these assets and private credit funds can restructure loans rather than realize defaults. Even the adjusted volatility may underestimate the true economic risk, in our view. Therefore, we think investors may want to view private credit risk as comparable with the risk for leveraged loans, given the comparability of underlying assets.
† CVaR (conditional value at risk) is the weighted average of the “extreme” losses in the tail of a distribution of returns. 95% CVaR = the weighted loss in the lowest 5% of extreme tail events.
Impact of return unsmoothing
Although our analysis confirmed that incorporating private assets can improve diversification in multi-asset portfolios, those benefits were significantly lower when the reported risk characteristics were unsmoothed.4 To illustrate this effect, we looked at the impact of adding a 20% allocation to three private asset classes—private equity, private credit, and private real estate—to a hypothetical 60/40 U.S. public stock/bond portfolio. Based on the historical performances of the asset classes involved, we then estimated risk and return outcomes for the hypothetical combined public/private portfolios, using as inputs both reported private asset volatilities and our own unsmoothed adjustments.5
Figure 5 illustrates the impact on hypothetical portfolio performance. The line represents the optimal risk and return frontier for a set of hypothetical portfolios with differing public equity/public fixed income weights. The dots each show the results of adding a 20% allocation to each private asset class to the 60/40 hypothetical portfolio.
Unsmoothing risk significantly reduced diversification benefits
(Fig. 5) Performance of a hypothetical 20% private asset allocation based on reported and adjusted volatility
June 2005 through June 2023.
Sources: Bloomberg Finance L.P., Standard & Poor’s, Cambridge Associates LLC, Cliffwater LLC, NCREIF (see Additional Disclosures). All data analysis by T. Rowe Price. See the appendix for a list of representative benchmarks for the asset classes shown.
*Excess return relative to the yield on 90-day U.S. Treasury bills.
† Dashed line shows the efficient frontier for hypothetical portfolios with varying allocations to public stocks and bonds. The efficient frontier marks the highest return that can be achieved for a given level of volatility (or, conversely, the lowest level of volatility that can be achieved for a given return) with the combination of assets in a portfolio.
The information shows hypothetical results, which are shown for illustrative purposes only and are not indicative of realized past or future performance. Actual investment results may differ significantly. As the hypothetical portfolios are based on the performances of market indexes as described, performance does not incorporate fees, expenses, or any other costs associated with an actual investment. See the appendix for additional details on the study methodology and for important information regarding hypothetical performance.
Based on reported volatility, inclusion of private assets appeared to meaningfully reduce overall risk in our hypothetical portfolios. All three private asset classes moved the risk/return trade-off toward the upper left-hand corner of the chart—a clear improvement in portfolio efficiency, as measured by the Sharpe ratio.
However, when we replaced the reported volatilities with our unsmoothed estimates, the diversification benefits were reduced. This was largely due to the higher volatilities and equity correlations that unsmoothing revealed for both private equity and private real estate.
These key portfolio metrics are summarized in the table in Figure 6.
Hypothetical portfolio outcomes using reported and adjusted volatilities
(Fig. 6) Annualized risk and return results from adding a 20% allocation to private assets
| Hypothetical Portfolios | Excess Return | Reported Volatility | Adjusted Volatility | Reported Sharpe Ratio | Adjusted Sharpe Ratio | Reported CVaR (95%) | Adjusted CVaR (95%) |
Base 60/40 Public Stock/Bond |
5.20% | 9.77% | 9.77% | 0.53 | 0.53 | -11.70% | -11.70% |
| With 20% Private Equity | 6.00 | 8.12 | 9.49 | 0.74 | 0.63 | -9.70 | -11.70 |
| With 20% Private Real Estate | 5.40 | 7.99 | 9.70 | 0.68 | 0.56 | -9.60 | -13.10 |
| With 20% Private Credit | 5.70 | 8.33 | 9.48 | 0.69 | 0.60 | -10.00 | -10.50 |
June 2005 through June 2023.
Sources: Bloomberg Finance L.P., Standard & Poor’s, Cambridge Associates LLC, Cliffwater LLC, NCREIF (see Additional Disclosures). All data analysis by T. Rowe Price. See appendix for a list of representative benchmarks for the asset classes shown.
*Excess return relative to the yield on 90-day U.S. Treasury bills.
The information shows hypothetical results, which are shown for illustrative purposes only and are not indicative of realized past or future performance. Actual investment results may differ significantly. As the hypothetical portfolios are based on the performances of market indexes as described, performance does not incorporate fees, expenses, or any other costs associated with an actual investment. See the appendix for additional details on the study methodology and for important information regarding hypothetical performance.
Sensitivity analysis
As discussed above, we believe that incorporating private assets in multi-asset portfolios can enhance diversification and improve both absolute and risk-adjusted return potential—provided that the private asset managers employed are skilled enough to take advantage of that potential.
The required return that the manager of a private asset strategy would need to generate to justify inclusion in a multi-asset portfolio depends on the strategy’s expected risk (higher volatility implies a higher required return) and its correlation to equities (higher correlation also implies a higher required return).
Figure 7 illustrates our estimates of the returns that were needed for a private asset to improve risk-adjusted results for our hypothetical 60/40 portfolio over the historical period we examined. Not surprisingly, if the expected risk and/or equity correlation was relatively low for a particular private asset strategy, so was the return hurdle needed to justify its inclusion in the hypothetical portfolio, given the potential reduction in overall risk.
Hurdle rates for private assets were relatively low in our analysis
(Fig. 7) Excess return* required to improve performance in a hypothetical 60/40 portfolio
| Correlation With Public Stocks | Annualized Volatility† |
|||
|---|---|---|---|---|
| 8% | 12% | 16% | 20% | |
| 0.1 | -3.6% | -3.3% | -2.9% | -2.4% |
| 0.3 | -3.2 | -2.7 | -2.2 | -1.5 |
| 0.5 | -2.9 | -2.2 | -1.5 | -0.8 |
| 0.7 | -2.5 | -1.8 | -0.9 | -0.1 |
| 0.9 | -2.2 | -1.3 | -0.4 | 0.6 |
Correlation and volatility ranges based on data from June 2005 through June 2023.
For Illustrative purposes only. Does not represent actual results and actual results may have differed materially. Additionally, changing the assumptions could yield different results.
Source: T. Rowe Price. See the appendix for the representative benchmarks for the asset classes shown.
*Excess return relative to the yield on 90-day U.S. Treasury bills plus an assumed equity return premium of 4 percentage points for public stocks over public bonds. This premium is consistent with the returns for Treasury bills, U.S. large-cap stocks, and U.S. aggregate bonds reflected in T. Rowe Price’s own capital markets assumptions as of March 2024.
† From June 2005 to June 2023, U.S. public large-cap stocks had a standard deviation of 16.6%, so the private asset was viewed as less risky in the columns with 16% volatility or less, and riskier in the 20% volatility column.
For example, if we consider a private asset strategy with 12% expected annualized volatility and a 0.5 correlation to U.S. large-cap equities, the strategy only needed to clear a hurdle rate of return no more than 2.2 percentage points below the total equity return in order to improve risk-adjusted returns in the hypothetical portfolio.
Because even higher-risk private assets can offer at least some diversification benefits as long as their equity correlations are less than one, our analysis found that the rates of return required to justify inclusion in a hypothetical 60/40 portfolio were lower than what skilled managers historically have been able to deliver in the private asset space.6
However, we want to stress again that the forward-looking potential for private assets to enhance portfolio diversification is sensitive to the risk and return assumptions incorporated in an optimization analysis. Because the universe of private assets is extremely heterogeneous, the appropriate allocation also can depend heavily on the style and expected performance characteristics of the specific strategies under consideration—not least the active skill of the managers used.
Conclusions
Our analysis suggests that while the diversification benefits of private assets are real, they likely are overstated in asset allocation models that use reported volatility as an optimization input. These results may be artificially smoothed—relative to comparable public asset categories—by the methodologies used to calculate returns and the frequency of reporting.
Even taking into account the impact of return smoothing and autocorrelation, a portfolio optimization exercise narrowly focused on the risk/return trade-off still could include meaningful allocations to private assets—perhaps considerably larger than the 20% weight we used in the hypothetical examples above.
However, in our view, it still would be prudent to constrain such allocations, due to the challenge of identifying and selecting top-tier managers in the space, the potential liquidity considerations (smaller and/or less frequent cash flow requirements might justify higher private asset allocations), and the potential for large drawdowns in times of market stress compared with purely public asset portfolios.
We believe our analytical framework can help investors better approximate the quantitative impacts of allocating to private assets. However, how much to allocate—and to which private asset classes—are both highly specific questions. The answers will vary from investor to investor, depending on their investment objectives, funding sources, and access to top-tier managers.
We remain confident that private assets can enhance diversification in multi-asset portfolios. But investors need to be sure they understand the significant differences between the reported risk characteristics of private assets—based on smoothed returns—and the actual economic benefits that can be delivered by these allocations.
Appendix
Figure A1: Representative indexes
| Public Stocks (U.S. Large-Cap) | S&P 500 Index |
| Public Bonds (U.S.) | Bloomberg U.S. Aggregate Bond Index |
| Cash | 90-Day Treasury Bill |
| Leveraged Loans | Morningstar LSTA U.S. Leveraged Loan Index |
| REITs | Wilshire U.S. REITs Index |
| Private Equity | Cambridge Associates LLC U.S. Private Equity Index |
| Private Credit | Cliffwater Direct Lending Index |
| Private Real Estate | NCREIF Property Index |
Figure A2: Allocation weights in hypothetical portfolios
| Portfolio Weights | |||||
| Hypothetical Portfolios | Public Stock |
Public Bond |
Private Equity |
Private Real Estate | Private Credit |
| Base 60/40 Stock/Bond | 60% | 40% | – | – | – |
| With 20% Private Equity | 40 | 40 | 20% | – | – |
| With 20% Private Real Estate | 48 | 32 | – | 20% | – |
| With 20% Private Credit | 48 | 32 | – | – | 20% |
Autocorrelation methodology7
The econometric model used in our process for adjusting reported private asset volatility assumes that the observed (i.e., reported) returns can be modeled as a moving average8 of the recent history of actual9 but unobserved returns given by the following equation:
Where is the observed return, is the unobserved actual return, Q is number of lags and are weights reflecting the impact of past realized returns on current, observed returns.
Further constraints are applied, so that
Thus, a maximum likelihood estimation on observed returns gives an estimate of the and the appropriate number of lags (Q) is selected based on their statistical significance.10
Since the returns of any asset may be expressed as a linear combination of risk factor returns:
, this can be re-expressed
where N is the number of risk factors. These components are estimated using linear regression and, because the error terms are auto-correlated, Newey-West correction is applied to assess the statistical significance of the coefficients (βi).
Important information regarding hypothetical results
Hypothetical results: The information provided above reflects data for hypothetical portfolios based on the theoretical blending of the indicated benchmarks. It does not reflect the actual returns of any portfolio or strategy. For the applicable hypothetical portfolios, the assumption of constant benchmark weights has been made for modeling purposes and is unlikely to be realized. Results shown for blended portfolios are hypothetical, do not reflect actual investment results, and are not a guarantee of future results. Hypothetical results were developed with the benefit of hindsight and have inherent limitations. Hypothetical results do not reflect actual trading or the effect of material economic and market factors on the decision‑making process. Results do not include management fees, advisory fees, trading costs, and other related fees. Results have been adjusted to reflect the reinvestment of dividend and capital gains. Actual returns may differ significantly from the results shown above. It is not possible to invest in an index. Different time periods would yield different results.
From the Field
Forecasting European equity earnings with macro factors
A strong relationship exists between equity earnings, inflation, and unemployment.
1 For purposes of this project, we focused on private equity, private credit, and private real estate as asset classes. The private investment universe is broader and includes more subsets, but, typically, our clients are interested in building diversified private asset portfolios that are well represented by these three buckets.
2 Source: J.P. Morgan, “Alternatives Investments Outlook and Strategy,” October 13, 2023.
3 Among asset allocation analysts, this method is known as 1-lag autoregressive model, or AR(1), unsmoothing.
4 Our analysis was based on historical returns from June 2005 through June 2023. Since returns on private assets typically are reported quarterly, performance metrics for both public and private assets were calculated quarterly and then annualized to make them comparable.
5 The 20% private-equity allocation was assumed to be funded entirely from the public stock component of the 60/40 hypothetical portfolio. The 20% private credit and 20% private real estate allocations were assumed to be funded on a pro-rated basis from both the public stock and public bond components. See the appendix for the actual allocation weights in the hypothetical portfolios and a list of the benchmarks used to represent the various asset classes. Hypothetical portfolio weights were rebalanced quarterly over the period studied.
6 Academic and industry research both have found significant return advantages for private asset managers in top performance quartiles. See Korteweg, Sorensen, 2017, “Skill and luck in private equity performance,” Journal of Financial Economics, Volume 124, Issue 3), and McKinsey & Company, McKinsey Global Private Markets Review 2022, March 2022, Exhibit 6, page 12. On the Web at: McKinsey Global Private Markets Review 2022.
7 For more details, see Pedersen, N., Page, S., He, F., 2014, “Asset Allocation: Risk Models for Alternative Investments,” Financial Analysts Journal, 70(3), 2014.
8 The MA(Q) model assumes that the actual (unobserved) returns are independent and identically distributed random variables.
9 The returns that would have resulted from a transaction-based pricing process.
10 We use the 95% confidence interval to define the optimal lag length.
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