Dynamic Risk Parity & Adaptive Asset Allocation

Dynamic Risk Parity & Adaptive Asset Allocation

Dynamic Risk Parity & Adaptive Asset Allocation

Global Risk Parity


The ReSolve Global Risk Parity (GRP) Strategy is designed to provide steady returns in most market environments, with moderate turnover.

The Strategy is constructed from a diverse universe of global asset classes so that the portfolio contains investments which can thrive in any economic environment. Asset classes are held in weights such that each asset contributes the same amount of risk to the portfolio. As asset relationships change over time, the Strategy responds with subtle shifts to maintain maximum diversification.

Why Risk Parity

Traditional portfolios are structurally flawed

The past quarter century has been characterized by benign inflation and sustained growth in the global economy. These qualities favored traditional portfolios of developed market stocks and bonds, such as the ubiquitous 60/40 ‘balanced’ portfolio. However, truly diversified portfolios must be prepared to weather periods of poor global growth, potentially accompanied by large swings in inflation, when both stocks and bonds may flounder. The 1970s offer a meaningful case study, as stagnating economic growth coupled with high and accelerating inflation produced negative real returns for stocks and bonds, per Figure 1.

Dynamic Risk Parity & Adaptive Asset Allocation

The fact is, at any moment investors are anticipating economic outcomes to evolve along two dimensions. That is, the economy is either expected to produce accelerating or decelerating inflation, coincident with accelerating or decelerating growth. Moreover, the economic drivers of asset class returns lead to predictable asset class behaviors in each inflation and growth regime. Figure 2. provides a model for asset class behaviors in different inflation and growth environments. Each quadrant contains assets that might be expected to thrive under the specified combination of inflation and growth. Assets close the center will respond in a mildly positive way to the regime, while returns to assets on the periphery are highly sensitive to that environment.

Dynamic Risk Parity & Adaptive Asset Allocation

Recall that accelerating inflation and decelerating growth, corresponding to the upper left quadrant in Figure 2., characterized the economic environment of the 1970s. In this type of ‘stagflationary’ regime, stocks and bonds should struggle. However, holding gold, commodities and, in contemporary markets, treasury inflation protected securities (TIPS), would have provided a ballast to offset the losses from stocks and bonds as can be seen in Figure 3. As such, a more diversified portfolio would have generated reasonable returns during this otherwise ‘lost’ decade.

Dynamic Risk Parity & Adaptive Asset Allocation

See full PDF below.

Adaptive Asset Allocation


Modern Portfolio Theory (MPT) is the most widely used method to construct portfolios which maximize expected returns at different levels of risk. Investors often use long-term average asset returns and risks to find optimal portfolios, but these long-term estimates are subject to large errors in the intermediate term. As small errors in estimates can lead to large errors in portfolios formed using MPT, investors add heuristic constraints to produce more intuitive and diversified portfolios than MPT would prescribe. Unfortunately, these heuristic adjustments often drive portfolios far away from optimal values. In this paper, we describe methods of portfolio optimization that rely on shorter term estimates of risk and return, and which change through time in response to observed changes in markets. In stepwise fashion we demonstrate how using shorter-term rolling estimates of risk, diversification, and returns delivers more resilient portfolios which thrive in good times – and bad.


For most of us, the ultimate goal of investing is to achieve a target wealth (or portfolio income) with the lowest possible risk. The vehicle we use to realize this ambition is our investment portfolio. But what mix of investments is most likely to help us realize our ambitions?

Modern Portfolio Theory (MPT) is a Nobel Prize winning mathematical model that relates the expected return and risk of a portfolio to the returns and risks of its individual constituents, after accounting for the effects of diversification. If thoughtfully applied, it can be a valuable tool in the construction of a reasonably efficient portfolio to meet the needs of most investors.

It is useful to think of MPT as a machine. When you feed the machine information about the assets being considered for a portfolio, it produces new information about portfolios constructed from those assets. Specifically, MPT takes in information about the expected return, risk, and correlation for each asset under consideration for investment. In return, it produces information about all of the portfolios that maximize expected returns at each level of portfolio risk. Portfolios which maximize expected return at a level of risk said to be ‘efficient’ portfolios, and the continuum of all portfolios which maximize return at each level of risk is called the ‘efficient frontier’.

Dynamic Risk Parity & Adaptive Asset Allocation

Note on the frontier in Figure 1. a square indicating the mix of stocks and bonds that delivered the lowest volatility over the period. This is the so-called ‘minimum variance portfolio’, resulting from a mix of 22% stocks and 78% bonds. Also observe a circle at the point of highest return per unit of risk, representing the maximum “Sharpe ratio” portfolio, to be explained below. A diamond indicates the return that was available at a 10% target volatility (~12%), which happened to correspond to a mix of 60% stocks and 40% bonds. Lastly, the crossed squares at the ends of the frontier indicate the risk and return from owning either 100% bonds (at the bottom) or 100% stocks (at the top). Of course, this frontier is backward looking so investors should not derive any information about how to construct portfolios for the future from this simple example.

See full PDF below.




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