A surge in U.S. consumer prices has energized the debate about inflation and what investors should do about it.

Inflationistas believe massive fiscal and monetary stimulus is setting off a self-sustaining cycle of rising prices. Others, including the Federal Reserve and the Biden Administration, say elevated inflation readings in recent months will prove transitory. They stem chiefly from temporary factors such as supply bottlenecks and a spike in post-pandemic consumer demand.

Our July Research paper, “Assessing Inflation: Theories, Policies and Portfolios,” argues that the debate over inflation generally suffers from a lack of definition – and, therefore, comprehension. Measures of inflation can vary widely and some, such as the consumer price index, tend to overstate inflation. Theories of inflation also have evolved, having undergone two major inflection points since the 1970s.

Currently, we believe there are fatter inflation tails than the market has expected. But longer term, there is a high probability that inflation will be contained. However, for investors who wish to hedge against inflation risk, we demonstrate how a portfolio approach that combines multiple strategies may be effective in a variety of inflation scenarios.

A Portfolio Approach For Inflation-Hedging

The potential benefits of a portfolio approach are easy to see.

Consider a simple example with three commonly used inflation-hedging assets: Treasury Inflation-Protected Securities (TIPS), commodities and real estate investment trusts (REITs). Figure 1 plots inflation beta versus the Sharpe ratio for optimal portfolios with different inflation beta targets, as well as the three individual assets. The results show that optimal portfolios have the potential to deliver better return and/or hedging benefits than individual asset classes.

Figure 1: Three assets are better than one

Figure 2 details our assumptions about the characteristics of these asset classes and how they can be combined in diversified portfolios with greater estimated returns and lower volatility, on a leveraged and unleveraged basis. The two portfolios have positive inflation betas and achieve higher estimated returns and Sharpe ratios than individual asset classes, except for REITs, which have a negative inflation beta.

Figure 2: Portfolios may improve estimated Sharpe ratios and/or inflation-hedging properties

Macroeconomic Scenarios And Asset Tilts

Optimizing the overall portfolio also requires factoring in how individual asset classes may perform in various macroeconomic scenarios. Figure 3 shows four macro scenarios and our probability estimates for each. These reflect our current views on macroeconomic scenarios and their probabilities, as well as the allocations in the following analysis, all of which are subject to change.

Figure 3: Macroeconomic scenarios matter

Putting It All Together

The bar chart in Figure 4 shows asset tilts with respect to the market portfolio under each scenario, as well as the probability-weighted average of the four scenarios.

As expected, inflation-hedging assets such as global inflation-linked bonds and commodities have higher expected returns in inflationary scenarios. However, a higher return does not necessarily translate to a higher optimal weight in a given scenario. What matters most is relative returns across assets under each scenario. For example, in the stagflation scenario, the portfolio tilts toward various inflation-hedging assets such as inflation-linked bonds, commodities and private natural resources.

Figure 4: Expected returns and asset tilts under various scenarios

Overall, the results show that inflation-hedging assets can play a constructive role in an overall portfolio, even when the total probability of higher-than-expected inflation is low at 40%. Furthermore, we should note that these results reflect our current views on macroeconomic scenarios, and allocations are subject to change. Please refer to the full Research paper for a complete analysis and information on methodologies and calculations.


Proxies For Risk Modeling


1 TIPS are proxied by the Bloomberg Barclays US Treasury Inflation Notes Total Return Index; REITs are proxied by the FTSE Nareit Equity REITS Total Return Index; and commodities are proxied by the Bloomberg Commodity Index.
2 Inflation beta is calculated by regressing asset’s excess returns onto inflation surprise and growth surprise. An inflation beta of x can be interpreted as if realized inflation is 1% higher than expected; all else equal, the assets excess return will be x% higher.
3 The optimal portfolio achieves the highest return with inflation beta equal to the target. We allow for a maximum of 30% leverage and volatility is no larger than 18%.
4 We first calculate implied returns, which are a set of returns in which market-capitalization weights are mean-variance optimal; we then calculate scenario-specific returns and the optimal portfolio under each scenario.
The Author

Jamil Baz

Head of Client Solutions and Analytics

Josh Davis

Global Head of Risk Management

Jerry Tsai

Client Solutions and Analytics



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The analysis contained in this paper is based on hypothetical modeling. Hypothetical performance results have many inherent limitations, some of which are described below.  No representation is being made that any account will or is likely to achieve profits or losses similar to those shown. In fact, there are frequently sharp differences between hypothetical performance results and the actual results subsequently achieved by any particular trading program or strategy. 

One of the limitations of hypothetical performance results is that they are generally prepared with the benefit of hindsight.  In addition, hypothetical trading or modeling does not involve financial risk, and no hypothetical example can completely account for the impact of financial risk in actual trading. For example, the ability to withstand losses or to adhere to a particular trading program in spite of trading losses, are material points which can also adversely affect actual trading results. There are numerous other factors related to the markets in general or to the implementation of any specific trading program which cannot be fully accounted for in the preparation of hypothetical performance results, all of which can adversely affect actual results. No guarantee is being made that the stated results will be achieved.

The allocation models presented here are based on what PIMCO believes to be generally accepted investment theory. They are for illustrative purposes only and may not be appropriate for all investors. The allocation models are not based on any particularized financial situation, or need, and are not intended to be, and should not be construed as, a forecast, research, investment advice or a recommendation for any specific PIMCO or other strategy, product or service. Individuals should consult with their own financial advisors to determine the most appropriate allocations for their financial situation, including their investment objectives, time frame, risk tolerance, savings and other investments.

Figures are provided for illustrative purposes and are not indicative of the past or future performance of any PIMCO product.

Return assumptions are for illustrative purposes only and are not a prediction or a projection of return. Return assumption is an estimate of what investments may earn on average over the long term. Actual returns may be higher or lower than those shown and may vary substantially over shorter time periods.

We employed a block bootstrap methodology to calculate volatilities. We start by computing historical factor returns that underlie 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, 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 will 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.

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Real Estate is a custom model is designed to mimic the risk and return characteristics of an investment in levered, private opportunistic real estate based on the corresponding indices from Preqin and Cambridge Associates. Note that historical volatility on illiquid assets is understated as they are not regularly marked to market. Private Equity is a custom model where risk factor exposures are estimated through a regression on the Cambridge Private Equity Index. Adjustments are made to equity risk and liquidity consistent with empirical research on private equity managers. Note that historical volatility on illiquid assets is understated as they are not regularly marked to market. Private Credit is a custom model that represents an investment in broadly diversified private credit assets. This includes levered and unlevered exposures to residential credit, consumer finance, specialty accounts receivables financing, commercial real estate debt and private corporate lending. Note that historical volatility on illiquid assets is understated as they are not regularly marked to market. Private Infrastructure is a custom model is designed to mimic the risk and return characteristics of an investment in private infrastructure. Note that historical volatility on illiquid assets is understated as they are not regularly marked to market. Custom models: Models are provided as a proxy for asset classes where a market index is not available and are not intended or generally made available for investment purposes.

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