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Tokenized Risk Assessment:
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Contacts:
Christine Cai, FRM, Director of Business
Christian Lantzsch, Director of Research
Sefton Kincaid, CFA, Managing Partner
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This document provides a detailed overview of Cicada’s risk assessment methodology for tokenized assets, including, private placements, tokenized direct lending, and other blockchain-based products that take direct credit risk. This document should serve as a foundation for users of Cicada’s research to glean additional context on Cicada’s risk scoring methodology.
When rating tokenized yield bearing products on blockchain-based platforms, Cicada analyzes the underlying instruments, collateral, and historical performance data. We also examine issuer and operational flows to understand counterparty risks along the chain of events to tokenize various financial assets. Finally, we analyze the borrower entity’s capital and legal structures, transaction documents, and legal opinions to assess the effectiveness of credit and structural enhancements that could support higher credit ratings or lower ratings in the event of heightened structural dependencies.
All of Cicada’s risk ratings work employs judgement. Our risk framework is designed to capture a multitude of credit and counterparty risks. While our methodologies are rigorously tested, the unique nature of tokenized asset structuring, supporting collateral, and varying legal systems introduces idiosyncratic risks. In our judgment, these factors may reduce the effectiveness of certain risk mitigations, and we account for this in our ratings process to ensure a more accurate reflection of the risks involved. Consequently, there may be times when certain components or criteria are deemphasized or deemed irrelevant. We invite those with further questions related to underlying ratings to reach out to members of the Cicada team.
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The approach laid out in the following methodology applies to new ratings as well as existing ratings we assess on an ongoing basis for our partners. New ratings involve an assessment of both historical performance and forward-looking risk factors while existing ratings are periodically updated based on underlying business, legal, market, or regulatory trends.
This methodology does not include an exhaustive description of all risk factors that our analysts may consider but standardizes the input of key information into a benchmarking process whereby users of our research will have a better understanding of the relative risks across various tokenized product offerings.
Cicada begins with a standardized risk framework (as depicted below in Figure 1) predicated on three pillars of risk: corporate credit/issuer risk, structural risk, and credit enhancement. We combine the assessment of these risks into a generalized ratings output that can be standardized across various tokenized debt product offerings.
We employ a scorecard system to depict our assessment of bottom-up risk analysis to allow clients of our research a snapshot into key strengths and/or weaknesses. Our methodology utilizes a 1-5 numerical rating scale, with investible ratings falling between 3.0 to 5.0, which can be mapped to traditional probability of default-based risk ratings.
We have witnessed investment analysis and compliance as the most pressing needs for our ratings work, but ratings are designed to be utilized by various user types across any organization integrating digital assets.
Figure 1: Tokenized Risk Assessment Taxonomy
Information richness is paramount in any risk assessment and our guiding principle in providing a rating. As such, we focus on downside protection (also known as Expected Loss) analysis across our Credit and Stablecoin Methodologies that we would expect to reasonably map to long-term rates of expected losses witnessed in traditional assets.
Cicada Credit Ratings (“CCR”) are the first pillar of our TRA Rating Methodology. Once the Risk Team feels comfortable with the adequacy of information provided via legal documentation, financial reporting, on-chain data, executive interviews, and other attestations, the underlying assets are rated using the Cicada’s Credit Rating System based on an aggregate scale of 1-5, with 5 considered a lowest-risk counterparty, i.e. AAA-equivalent. The CCR method breaks out scoring into four buckets as detailed in the Appendix. Adjustments to the CCR score are also made in context of the underlying collateral. Please Note: A CCR below 3.0 (equivalent to <CCC rated) is an instant ratings rejection. An Issuer will have to address certain material weaknesses resulting in a TRA Rating of <3.0 for Cicada to publish a rating. Among various options, CCRs can be improved at this stage through some of the following, though non-exhaustive, items:
Figure 2: CCR Framework
Corporates and funds possess different legal organizational structures which introduce complexity in estimating an Issuer’s expected balance sheet positioning in a downside scenario. We do not assume a simple consolidation of all entities under a corporate umbrella, but rather analyze an entity’s obligations by legal entity, subject to having sufficient information.
CCRs by definition are focused on isolating Issuer-related (a.k.a. “HoldCo”) risks, but prudent structuring can ring-fence these risks, resulting in our Credit Enhancement Factor (“CEF”). We account for risk isolation and credit enhancement by performing a parallel bottom-up ratings analysis on the underlying legal entity (a.k.a., “OpCo”).
SPV CCR = Cicada Credit Rating (“CCR”) + Credit Enhancement Factor (“CEF”)
CCRs are similar to Moody’s Corporate Family Ratings, that reflect the relative likelihood of a default on a corporate’s obligations. Given structural considerations with respect to legal entity or fund setup, we need to account for these changes.
Our analysis of the tokenization process takes into account the operational flow of inter-company and partnership dependencies. For example, a vertically integrated worksteam would reduce dependencies on outside parties, but at the expense of greater native operational execution. Where appropriate, we may account for these structural nuances by haircutting tokenization workflows to account for enhanced operational complexity. We also may simply conclude that the operating model is not appropriate relative to peers and therefore outside of our scope.
These adjustments require substantial judgement of the following non-exhaustive list of details:
To account for these risks, we apply a ratings notching system of 1 to 4 notches. We benchmark the tokenization process using our Structural Risk Factor (“SRF”) Weightings to create a baseline means of comparison across issuers. From here, we bucket an issuer’s tokenization process into the below notches. The growth of tokenization should reduce the need for a pure notching system over time as tokenization standards improve, but given the disparate nature of current tokenization processes and limited secondary liquidity unlocking value for issuers, we feel it’s prudent to haircut for these risks for the foreseeable future:
See Appendix for: Definitions of SFR Components.