At the 2024 SRP Americas Conference, a new session format was introduced. Titled “Product Lab”, the panel featured contributions from Ed Condon of Structured Insights, Michaelangelo Dooley of NewEdge Wealth and analytics provided by Tim Mortimer of FVC.

Three typical use cases were considered. The first involves a broker looking to place a mainstream product with a large group of clients. This would normally be a relatively light touch sale but one that needs to be executed in compliance with FINRA’s suitability and best execution rules. The second is a Registered Investment Adviser (RIA) recommending an interesting investment proposition to a sophisticated or High Net Worth client experienced in structured products. The third use case is the increasingly popular channel of a manager overseeing a Separately Managed Account (SMA) or other active managed portfolios.

Examining key use cases for structured products

Structuring and product design processes usually start with the fundamentals, such as investment horizon or product maturity, risk level and underlying asset. These three properties form the basis of any asset allocation decision, similar to those for direct equity investments. In the case of equity and other open-ended investments, the investor's investment horizon is key. Since structured products are created from one or more derivative or option components, the product generally requires its maturity defined at the outset. An investor can simply reinvest the proceeds of a structured product into a follow-on choice if their investment horizon is longer.

The underlying choice is often the next decision in new product design. An underlying is typically selected from a benchmark index, proprietary index or other Quantitative Investment Strategy (QIS). Alternatives include stocks or baskets of stocks and other asset classes.

Underlyings will be chosen for asset allocation reasons, but the choice of underlying also be considered in conjunction with the product payoff. For example, low-volatility underlyings usually work best with capital-protected payoffs. Typical payoff structures include the popular Auto-call, Leveraged Return, Twin Win and Protected Note. The investor’s market expectation may influence both the payoff type and potentially underlying asset.

Most structured notes are linked to an investment bank counterparty. While the choice remains important, it no longer dominates as much as it did in the years following the Global Financial Crisis of 2008. Counterparty rating, financial strength, country of domicile, and brand recognisability all need to be taken into account. However, the simplest solution to counterparty risk is generally to achieve adequate diversification.

Evaluating product metrics

Six example CUSIPs from the US market are presented below with their product details.

These CUSIPs have different characteristics but are typical of US structured products. They combine elements of auto-calls, leveraged returns and digital payouts.

Two products are single-point auto-calls (sometimes known as “catapult”), while one is a more conventional snowball auto-call. The barrier levels range between 60% and 80%, although product #6 is capital protected and the barrier only serves to terminate the product. Analytics for each product come from independent analysis. The first assessment was to calculate the standard risk rating, as used in Europe (“PRIIPs”). Since product #6 is capital protected, it is expected to score low (2 out of 7), and the other capital-at-risk products are expected to score a more typical 4 or 5.

Simulations of product payoffs were conducted using the FVC stress test engine, which calculates potential returns under three scenarios: “Bull”, “Bear” and “Neutral”. These are performed using Monte Carlo simulations, with the names indicating the growth rate of the underlying asset (positive, negative and near zero respectively).

Beating cash is an important test for any structured product and the probability of doing so will generally be the highest for the bull scenario, as most structured products perform better when the market rises. Product #1 shows the highest chance of this happening because in a strong growth scenario with multiple auto-call opportunities, a successful outcome is highly likely. This is supported by market experience over the past ten or more years, as shown by backtesting calculations in the bottom row of the table.

The product with the highest probability of loss in a bear scenario is product #5. This product has the joint-highest barrier and direct capital loss below that level, with no early auto-call opportunities to reduce the likelihood of loss.

The product with the highest Average Equivalent Return (AER) is product #3, due to its strong upside leverage. While this average includes negative outcomes, its overall upside potential gives it an advantage over the rest of the product set.

This survey of product selection and analysis considerations demonstrates some of the choices that need to be made when creating and distributing new structured products.

Tags: Structured Edge
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