From the papers to a quarterly indicator set.
The Radar turns ten years of research on loan contracting, nonbank lending and bank–nonbank interconnection into seven indicators that a supervisor could replicate. It runs on its own site, privatecreditradar.eu, under my name; the research team sits at the Centre for European Transformation at Frankfurt School. Public data only. Aggregates only.
Quarterly, aligned to the BDC filing calendar.
US BDC book, 2026 Q2
Standing indicator set, public deck, series file, methodology note.
European book
European borrowers in US BDC schedules, listed European and UK vehicles, news-based events.
2026 Q3 vintage
Special topic and sector tags (data-centre and AI-compute exposure).
2019–2022
Constant panel of filers present every quarter, for the full cycle.
Stress is visible in the filings before it is visible in defaults.
Private credit absorbs stress through payment-in-kind, amendments and sponsor support long before a payment default. The Radar is built to see that stage. A borrower-quarter counts as a credit event under one of three tiers; the headline shadow default rate is Tier 1 plus Tier 2.
| # | Indicator | What it measures | Built on |
|---|---|---|---|
| 1 | Loans marked below 90 and below 80 | Share of positions and of fair value marked at a discount | Valuation of leveraged loans; cross-lender mark dispersion |
| 2 | Non-accrual rate at cost and at fair value | Hard defaults recognised by the lender (Tier 1) | SEC schedules of investments, inline XBRL |
| 3 | Payment-in-kind | PIK income as a share of investment income; share of positions paying in kind | When Flexibility Becomes Forbearance (Rintamäki, Steffen) |
| 4 | Shadow default rate | Tier 1 hard defaults plus Tier 2 distress-driven amendments, PIK switches and covenant resets | CovenantAI (Saunders, Steffen, Verhoff); 8-K event capture |
| 5 | Cross-lender mark dispersion | The same facility marked by several lenders; dispersion and the lowest mark as early signals | Borrower matching across BDCs with a documented name-matching rule |
| 6 | Fund liquidity | Unmet tender requests, unfunded commitments to stressed borrowers, unsecured maturity wall | Funding Fragility in Direct Lending (Mistopoulou, Steffen) |
| 7 | Origination and terms at entry | Pricing on new loans, spread trends, direct lending versus syndicated issuance | The Cyclicality of Direct Lending (Hinzen, Mondini, Rintamäki, Steffen) |
Thresholds (90, 5 points, CCC) are parameters, not fixed; every change is documented in the methodology changelog with a sensitivity table.
Why a personal research site carries a market monitor.
Measurement first
Loan-level data on private credit does not exist outside the filings. The Radar is the measurement layer the research needs, published so that others can check it.
Independence
The site is mine, not the school's. Fixed-hour releases, embargoes, right-of-reply windows and same-day corrections are my decisions, and the numbers are not a supervisory view.
Europe as the target
US BDCs are the benchmark because they are the only segment with public marks. The European book states its coverage plainly: which borrowers, which lenders, what share of the market it plausibly sees.
Public results for everyone. Depth for partners of the Centre.
The standing indicator set, the series file and the methodology note are published every quarter and free to use with attribution. Firms that want depth tables, briefings and closed-door sessions for their boards and investment committees support the research team at the Centre for European Transformation through a partner programme contracted with Frankfurt School. The Radar does not sell data.
Write to me directly to discuss a partnership, a board session or a research collaboration.
Prof. Dr. Sascha Steffen · Frankfurt School of Finance & Management · Adickesallee 32–34, 60322 Frankfurt am Main
- Public and commercially licensed data only. No supervisory data, ever.
- Every number carries its source and vintage: filing date and pull date.
- Aggregates are published; micro-level data from non-public sources never is.
- Names from private data appear only where publicly verifiable.
- Nothing is phrased as, or attributable to, an ESRB, ECB or supervisory view.
Prof. Dr. Sascha Steffen, author and editor
Kevin Guldager, research assistant, Centre for European Transformation
Eike Rabe, research assistant, Centre for European Transformation
Giorgi Katchiuri, research assistant, Centre for European Transformation
Open positions: pre-doc and post-doc, from January 2027