In a previous article, I did a deep dive into risk parity, discussing different approaches, sketching out (some) of the math involved in solving the risk parity problem, and going over some of the extensive literature on the topic.
In this article, I’m going to show the results of backtests of some of the different risk parity approaches I discussed. I’m going to be working with the same multi-asset ETF universe I used in this post:
As a reminder, the universe contains nine ETFs, representing asset classes that are grouped into four buckets (Equity/Fixed Income/Real Estate/Commodities & Gold). The data are daily from January 2000 to April 2026.1 I rebalance all portfolios monthly, requiring three years of daily data to estimate inputs. The universe is therefore dynamic, i.e. I consider at each rebalance date only the ETFs for which data are available over the last three years.
I considered the following alternative risk parity strategies:2
Inverse Volatility: this is not really a risk parity strategy, in the sense that it does not try to equalize risk contributions. By using only volatilities, this strategy ignores correlations between assets.
Asset RP (Vol): asset-level risk parity using standard deviation as the risk measure.
Asset RP (CVaR): asset-level risk parity using CVaR as the risk measure.
Bucket RP (Vol): equal risk budgets across buckets, split equally across eligible assets within each bucket, using standard deviation as the risk measure.
Bucket RP (CVaR): the same bucket-budget idea, but using CVaR instead of standard deviation.
Realized Performance
The table below shows the performance of all the strategies. The column “Realized Volatility” reports the actual volatility of each strategy as implemented. The remaining columns report numbers for rescaled strategies with 10% volatility.
In terms of returns, the Inverse Volatility strategy had the worst performance, achieving a CAGR of 7.27%. The best performers were the bucket RP strategies, with similar CAGRs around 8.4% to 8.5%. The downside volatility of the strategies are quite similar, around 7%. While the Inverse Volatility strategy has the least negative skewness and lowest kurtosis, it also has the worst maximum drawdown, around -39%. The bucket RP strategies reduce the maximum drawdown, with Bucket RP (CVaR) at -32%, which is still substantial for a strategy often associated with risk control. In terms of Sharpe and Sortino ratios, the bucket RP strategies perform better than the asset-level RP strategies. The best performer overall is Bucket RP (CVaR), with a Sharpe of 0.70 and a Sortino of 0.98. The strategies have similar turnovers, close to 1.50%.
The performance comparison is useful, but it should not be read as a horse race. These strategies are not designed to forecast returns; they impose different definitions of risk balance.
Allocations and Risk Contributions
I look at allocations and risk contributions for each strategy at the bucket level. For risk contributions, I report results using both the standard deviation and the CVaR measures.
The chart below reports the average allocation (in %) for each strategy. We can see that there are significant differences in allocations. The most notable pattern is that allocations to Fixed Income are comparatively large, which is expected given the lower volatility of bonds compared to other assets. The Inverse Volatility allocates the smallest percentage to Fixed Income (42.4%), while Bucket RP (CVaR) allocates nearly 2/3 of the portfolio, on average. The allocations to Commodities & Gold are the most stable, around 15%-17% for all strategies.
Moving on to the analysis of the risk contributions, I would start by mentioning that, for strategies that set budgets at the asset level, the bucket-level risk contributions will partly reflect how many assets are used to represent each bucket, as well as the volatilities and correlations of those assets. Therefore, for the strategies that work at the asset level (Inverse Volatility and Asset RP), we should expect higher risk contributions for the equity bucket, which in the later part of the sample, has three ETFs (SPY, EFA, EEM), compared to the other buckets, which have only two ETFs each. Earlier in the sample, the dynamic availability of ETFs also affects these counts. On the other hand, we should expect equal risk contributions at the bucket level for each bucket RP strategy under its own risk measure.
The chart below shows risk contributions at the bucket level using the volatility risk measure. As expected, we see higher risk contributions for the equity bucket for the first three strategies. For the Inverse Volatility, almost half of the total volatility comes from the equity bucket, highlighting the fact that ignoring correlations can lead to very unbalanced contributions at the asset class level. Also in line with expectations, the Bucket RP (Vol) strategy has equal risk contributions, while the Bucket RP (CVaR) does not, since it uses a different risk measure.
The risk contributions under the CVaR risk measure are shown on the chart below. This time, it is the Bucket RP (CVaR) that shows equal risk contributions, while Bucket RP (Vol) does not. The asset-level strategies again show very unbalanced risk profiles at the bucket level, especially the Inverse Volatility strategy.
Final Thoughts
These results illustrate clearly the fact that risk parity portfolio construction depends strongly on a number of choices:
asset selection and grouping
choice of risk measure
choice of risk budget (either at the asset level or at some aggregation like asset class in this example)
These choices are not innocuous, because they carry implications for what the portfolio will end up looking like. A clear example is the fact that the Inverse Volatility portfolio can end up producing very unbalanced risk contributions when aggregated at the asset class level.
For the particular set of assets used in this exercise, my preference would be to use a bucket RP strategy, because it allows control of risk contributions at the asset class level, regardless of how many or which ETFs we use to represent each asset class. As for the choice of risk measure, the performance of the Bucket RP (Vol) and Bucket RP (CVaR) are very similar, and using the CVaR, although nice conceptually, does not seem in this case to produce materially different portfolios relative to using the simpler volatility risk measure.
Risk parity is often sold as an investment approach that can deliver higher returns with controlled risk, but this depends heavily on the sample period, the assets chosen, and the behavior of correlations across regimes. Correlations are time-varying, and regime shifts can substantially affect the composition and performance of risk parity strategies. More importantly, since risk parity strategies are not really designed around return objectives, they should not, in my view, be judged on such metrics. The strategies are designed to impose a particular structure on risk contributions and, on that criterion, they largely do what they are supposed to do. The question, therefore, is what the investor values. Simple risk parity strategies are appropriate when the investor is seeking balanced risk contributions. As a risk-based approach, risk parity does not and cannot make strong statements about the returns it produces.
I extended the history of some ETFs using their own indices when available. Most series start in 2000, the exceptions being VNQI (start date: 02/01/2001) and BNDX (start date: 04/01/2013).
The CVaR strategies use a confidence level β=0.95.