Funds of ETFs – A New Risk Management Conundrum

The steady rise of passive investment strategies, through allocation to exchanged-traded funds (ETFs) in particular, has long been documented in financial literature. It does not simply represent an economic challenge to traditional active investment fund providers. For many asset management firms, the popularity of passive indexing among end-investors is a double disruption factor. Beyond sustained fee pressure, it moves these firms' own value added from a balanced combination of "asset selection" and "asset allocation" processes into pure "asset allocation" territories. While bottom-up investment specialists redirect their asset selection skills towards more "alternative" strategies and Alpha products, the world of pure Beta offering keeps expanding. Passive ETFs (a.k.a. index trackers) are now available across a wide range of markets and asset classes. They cover developed and emerging economies, bonds and equities, commodities and more. For asset allocators, this ETF universe expansion might be a blessing in disguise, provided they can overcome selected operational and risk management burdens.

Figure 1 - Funds of ETFs - A complex data transformation exercise

Processing more granular data

Whether it is in an "outsourced CIO" capacity, a model portfolio team in wealth management or as part of a robo-advisor platform offering, asset allocators can now easily blend individual ETFs to reflect their market views. They can adjust their fund holdings across asset classes, with flexibility and low transaction costs. From an operational viewpoint (see Fig. 1 above), it means the data feeds and volumes they need to process with full "look-through" increase significantly over pure index-based processes. For example, in the fixed income segment alone, popular ETFs tracking US corporate bond market indices can hold many thousands of positions, representing hundreds of single-name issuers in tens of industry sectors. The same applies to regional and global equities. As advocated in a previous blog post, ETF investors like asset allocators, model portfolio owners or fund-of-funds managers must have access to full "look-through" (LT) details of their selected funds. They need data transparency for each building block of their allocation strategies to best assess risk/return trade-offs of the overall investment. This granular information also helps these business stakeholders manage their market exposures. They can also implement possible exclusion criteria that are part of their investment offerings (sector restrictions, ESG score thresholds). For small to medium-sized teams of functional experts, the move from index level to full "look-through" data content is operationally difficult to achieve. The more these upstream data operations can be outsourced, the better.

Figure 2 - From an ETF universe to ad-hoc optimized fund of ETFs

Enhancing the risk management process

The prevalence of "top down" decision-making in a fund-of-ETFs investment process means it is key for managers to compute aggregated exposures across sectors, countries, currency blocks and asset types. This "look-through" information across ETF holdings helps provide critical insights into directional biases, style factor tilts and issuer exposures across the debt seniority ladder. It should answer questions on group exposures, for example, to the so-called "Magnificent 7" (group of mega-cap technology stocks) across all equity and bond holdings combined.

From a risk/return perspective, the fund-of-funds managers need access to key indicators on demand. These variables include expected returns, ex-ante volatility, Value-at-Risk (VaR) and expected tail loss measures (ETL) at various quantiles and/or time horizons, at different nodes of their investment tree.

The move to "look-through" across ETF components raises the processing bar from hundreds to thousands of input data points. This is not the only operational hurdle to overcome. Risk simulation requirements for these granular fund-of-funds datasets can lead to the generation of millions of new numerical outputs. This analytics transformation layer must service in-depth risk management investigations and portfolio construction. It means helping users measure how each allocation block (ETF in this case) contributes to expected returns, to volatility, stress-test results, or stochastic tail scenario outcomes. The teams need to also quantify diversifications across ETF blocks, and test risk mitigation plans through forex overlays, ETF switches or outright divestures. Finally, this second layer of analytics shall allow business users to act upon ex-ante risk/return reports. It means exploring "what-if" position amendments under current or stressed market conditions. For teams with ad-hoc expertise, this process might also include advanced optimization under various constraint types.

Expanding portfolio construction capabilities

Funds-of-ETFs are not new to the world of finance. The breadth of the ETF universe is a more recent market evolution. The moves of model portfolio or advisory teams towards clear "top-down" investment processes reflect this new investment landscape. As presented in this blog, the data volumes these functional expert teams need to manage with efficiency remain an operational burden to many. Does it make funds-of-ETFs a new risk management conundrum per se? For most managers, probably not. It does make-in all cases-these business lines prime candidates to embrace external managed services across their operations. This includes data connections and the access to secure Cloud-based infrastructures for large-scale risk/return simulations. For small to medium-sized asset managers caught in the business battle for scalable customization of investment products, the leverage of these external services is key. It will be up to the end-investors to judge whether their blend of Beta products ends up delivering some Alpha on top as a combination of proven allocation skills and differentiated operational workflow.

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