I’m going to start this post by saying that it makes no sense for anyone to pay management fees to get a return stream that is highly correlated to any existing asset class. Unfortunately, many actively managed funds fall in this category.
There’s two reasons for this. One, you can replicate this return stream by just investing in that asset class yourself, likely through low-cost ETFs. Two, even if the fees are low enough that you wouldn’t save that much money by just doing it yourself, adding a highly correlated return stream to something that already exists in your portfolio isn’t going to add anything beneficial to your risk adjusted returns.
With this in mind, I tried to reverse engineer AQR’s Risk Parity Fund using low-cost ETFs that are accessible to retail investors. The idea is to find a way for retail investors to replicate a risk parity strategy for their own portfolio. If you’re unfamiliar with risk parity, you can take a look at my post on how to implement a risk parity strategy.
Why AQR’s Risk Parity Fund Can Be Reverse Engineered
AQR is one of the few fund managers that offer their risk parity strategy as a mutual fund. This makes their daily returns and portfolio holdings easily accessible since mutual funds are held to a higher level of disclosure compared to hedge funds. In addition, AQR has published a number of articles which provide a little bit of insight into exactly how they implement their risk parity strategy.
Moreover, the core concepts of risk parity are well understood. It’s going to invest in a wide range of asset classes across all geographies. At it’s core, it could be described as a fully long, passive investment strategy. It’s passive in the sense that it’s not trying to implement any market views but rather trying to harvest the risk premium of multiple assets. This makes it ripe for replication. AQR does adjust asset weightings based on their risk forecasts and other proprietary measures though, so it’s not purely passive.
Alright, so why attempt to reverse engineer AQR’s Risk Parity Fund in the first place? The fund has an expense ratio of around 1% and the investment minimum for individual investors is $1 million. So this isn’t something that a normal person has access to, and it’s not even clear you would want to given the high fees. I also wanted to empirically determine their portfolio allocations to various asset classes.
AQR’s Portfolio Holdings
First, a look at AQR’s portfolio holdings. I provide the holdings in a table sorted by weight at the end of this blog post. I encourage you to take a look at the full list if you’re interested.
Some observations: AQR invests in a wide range of assets across all geographies, including equities, sovereign bonds, inflation linked bonds, credit spreads, commodities, and currencies. Fixed income have high weight and equities have low weight as is expected for risk parity strategies. Leverage is harnessed through futures, swaps, and forwards — again no big surprise there.
Exposure to credit spreads is through shorting credit default swap indexes. Exposure to inflation linked bonds is through buying the physical bonds because futures for inflation linked bonds do not exist. Equities (rank 6) and commodities (rank 23) only appear once in the top 25 holdings.
Replicating AQR’s Risk Parity Strategy
This is the methodology I used. First, I identified low-cost ETFs for each of the major market segments (I wrote about this before here) that are represented in AQR’s Risk Parity Fund.
Second, I ran a regression on the daily returns of AQR on the daily returns of the various ETFs, subject to the constraint of having a zero intercept and making the sum of the coefficients be close to one. The interpretation is that I wanted the regression to empirically determine the weights of the various ETFs for an unleveraged portfolio and to try to make it match the returns of the leveraged portfolio.
The correct way to do this would be to implement a learning algorithm where the cost function incorporates these constraints, but I just did it using regular linear regression and trial and error to get an acceptable solution informed by my intuition.
Here are the ETFs that I selected along with the model output. The coefficients under the “Estimate” column are the portfolio weights which sum to 100%.
Call: lm(formula = AQRNX ~ 0 + GLD + SCHP + SPY + TLO + EFA + VWO + EMB + GSG, data = data) Coefficients: Estimate Std. Error t value Pr(>|t|) GLD 0.034 0.007 4.903 1.05e-06 *** (Gold) SCHP 0.265 0.034 7.684 2.75e-14 *** (US TIPS) SPY 0.163 0.017 9.389 < 2e-16 *** (US Equities) TLO 0.122 0.015 7.936 4.03e-15 *** (US Sovereign Bonds) EFA 0.028 0.015 1.928 0.054 . (Developed Equities) VWO 0.076 0.011 6.731 2.39e-11 *** (Emerging Equities) EMB 0.221 0.020 11.078 < 2e-16 *** (Emerging Sovereign Bonds) GSG 0.092 0.007 13.207 < 2e-16 *** (Commodities)
This is what this synthetic risk parity strategy looks like. It’s done pretty well at replicating AQR’s strategy with a correlation of 96% and a R-squared of 69%. And the weighted average expense ratio for this portfolio is only 0.20%.
There are a few limitations that prevent this synthetic strategy from matching AQR’s strategy more closely. AQR’s strategy is obviously levered, with total exposure around 300% of their net asset value, while the synthetic strategy is unlevered. This explains the roughly 5% difference in cumulative return over this time period.
Usually risk parity funds are levered to an arbitrary level of volatility, so just think of this synthetic strategy as a less volatile version of AQR’s strategy. AQR targets an annualized volatility of 10%.
To try to get more volatility from the unlevered ETF portfolio, I selected an ETF for US sovereign bonds with a 25 year average maturity (TLO). AQR’s exposure to sovereign bonds for developed markets is the 10 year futures contract, but selecting bonds with longer maturity allows for more volatility and more return. It’s a way to simulate the leverage that’s in risk parity strategies. I think using the long maturity ETF explains why there is only a 12% weight for US sovereign bonds and a 26% weight for US TIPS.
The synthetic strategy also has no exposure to credit spreads. This is more of a limitation with the current universe of ETF offerings which are mostly US focused. None of the broad market corporate credit ETFs I tried produced good results in the regression. I think it might have something to do with the fact that AQR gets exposure to credit spreads by shorting credit default swaps — kind of hard to replicate that.
Overall, this model yields pretty reasonable weights and provides good replication. The code underlying this post can be viewed at my Github repository.
I have an email list where I occasionally send updates to readers on the trading systems that I’m developing. If you are interested, please enter your email below.
Below you can find the position weights for AQR’s Risk Parity Fund.
|Asset Class||Security Description||Weight|
|1||Fixed Income||U.S. 10 Yr Treasury Note Future||45.8%|
|4||Fixed Income||Euro Bund 10 Yr Bund Future||25.3%|
|5||Credit||iTraxx Europe Crossover||13.7%|
|6||Equity||E-Mini S&P 500 Index Future||13.6%|
|7||Fixed Income||Singapore Interest Rate Swap||9.3%|
|8||Fixed Income||South Korea Interest Rate Swap||9.1%|
|9||TIPS||TII 0.125 04/15/20||8.0%|
|10||TIPS||TII 0.125 04/15/19||7.3%|
|12||Fixed Income||South Africa Interest Rate Swap||5.1%|
|13||Currency||TRY vs USD Forward||4.6%|
|14||Fixed Income||Poland Interest Rate Swap||4.6%|
|15||Fixed Income||Hong Kong Interest Rate Swap||4.5%|
|16||TIPS||TII 0.375 07/15/25||4.2%|
|17||TIPS||TII 0.250 01/15/25||3.9%|
|18||Fixed Income||OSE Japan 10 Yr Bond Future||3.8%|
|19||Currency||KRW vs USD Forward||3.6%|
|21||Currency||BRL vs USD Forward||3.5%|
|22||Currency||MXN vs USD Forward||3.2%|
|24||Currency||PLN vs USD Forward||3.0%|
|25||TIPS||TII 0.125 07/15/24||2.9%|
|26||TIPS||UKTI 0.125 03/22/24||2.8%|
|27||Currency||HUF vs USD Forward||2.8%|
|28||TIPS||DBRI 0.100 04/15/26||2.7%|
|29||Commodity||Brent Oil Future||2.3%|
|30||Fixed Income||Hungary Interest Rate Swap||2.3%|
|31||Equity||HSCEI China Index Future||2.3%|
|32||Currency||INR vs USD Forward||2.3%|
|33||TIPS||UKTI 0.125 03/22/26||2.2%|
|35||Fixed Income||Czech Republic Interest Rate Swap||2.2%|
|36||Currency||ZAR vs USD Forward||2.1%|
|37||TIPS||DBRI 1.750 04/15/20||2.1%|
|38||Equity||OSE Japan Topix Index Future||2.0%|
|39||TIPS||FRTR 2.250 07/25/20||2.0%|
|40||Equity||FTSE100 Index Future||1.7%|
|41||Equity||DJ Euro Stoxx 50 Future||1.6%|
|43||Commodity||WTI Crude Future||1.5%|
|44||Equity||S&P Mid 400 E-Mini Index Future||1.4%|
|45||Fixed Income||Canada 10 Yr Bond Future||1.4%|
|48||Equity||Russell 2000 EMini||1.2%|
|49||Equity||KOSPI 200 Index Future||1.2%|
|50||TIPS||DBRI 0.100 04/15/23||1.2%|
|51||Equity||MSCI Taiwan Index Future||1.1%|
|52||Equity||DAX Index Future||1.0%|
|53||TIPS||FRTR 0.250 07/25/24||1.0%|
|55||TIPS||FRTR 0.100 03/01/25||0.9%|
|56||Commodity||Live Cattle Future||0.9%|
|57||Fixed Income||Australia 10 Yr Bond Future||0.9%|
|58||Equity||Bovespa Index Future||0.9%|
|59||Currency||CNH vs USD Forward||0.8%|
|61||TIPS||FRTR 1.100 07/25/22||0.8%|
|62||Commodity||Soy Oil Future||0.8%|
|63||Commodity||Gas Oil Future (100MT)||0.7%|
|64||TIPS||FRTR 0.100 07/25/21||0.6%|
|66||Equity||Hang Seng Index Future||0.6%|
|67||Commodity||Unleaded Gas RBOB Future||0.6%|
|68||Commodity||Cotton No. 2 Future||0.5%|
|69||Commodity||Heating Oil ULSD Future||0.5%|
|71||Equity||Swiss Market Index Future||0.5%|
|72||Equity||SGX CNX Nifty Index Future||0.5%|
|73||Commodity||Natural Gas Future||0.5%|
|74||Equity||SPI 200 Index Future||0.5%|
|76||Commodity||Lean Hog Future||0.4%|
|79||Equity||S&P/TSE 60 Index Future||0.3%|
|81||Equity||South Africa Top 40 Index Future||0.3%|
|82||Equity||CAC40 Index Future||0.2%|
|83||Commodity||Soy Meal Future||0.1%|
|84||Commodity||Feeder Cattle Future||0.1%|
|85||Equity||IBEX 35 Index Future||0.0%|
|87||Commodity||Wheat Future (KCB)||0.0%|
|88||Currency||EUR vs USD Forward||-12.8%|