Announcing merton 1.0

merton 1.0 is the first stable release of a Python package built specifically to make the Merton (1974) structural credit-risk model — and its industry-standard descendants — production-quality and easy to use.

We started from the observation that the Python ecosystem had no maintained, vectorized, well-calibrated implementation of the most important model in structural credit risk. finverse and riskoptima shipped basic single-firm math; creditriskengine was opaque about performance; modelrisk was alpha; QuantLib had no native Merton module at all. None of them bundled the extensions, portfolio engine, backtest harness, or Excel integration that practitioners actually need.

merton ships those — and then some.

What’s in the box

  • Single-firm calibration — five methods: Vassalou-Xing iterative MLE (default), Duan transformed-data MLE with survivorship correction, Jones-Mason-Rosenfeld, Bharath-Shumway naive, and KMV-iterative (Crosbie-Bohn). Plus an emcee-backed Bayesian MCMC calibrator under merton[mcmc].

  • Vectorized math primitives — distance_to_default, prob_of_default, implied_credit_spread, term_structure_pd. All closed-form, all NumPy-friendly, all backend-dispatchable.

  • Equity Greeks — Δ, Γ, Vega, Θ, ρ closed-form; matching JAX-autodiff implementations under merton[jax] validate them automatically.

  • Structural extensions — Black-Cox first-passage, Geske compound options, Longstaff-Schwartz stochastic rates, CreditGrades (RiskMetrics 2002), Leland-Toft endogenous default, Zhou jump-diffusion.

  • Portfolio engine — Vasicek single-factor with closed-form Basel IRB capital; Gaussian, t, and Clayton copulas; Monte Carlo loss distribution with VaR / ES / economic capital.

  • Climate stress — NGFS Phase V (2024) headline scenarios (net_zero_2050, delayed_transition, current_policies, fragmented_world); composable Scenario framework with carbon-price paths, sectoral PD multipliers, and chronic physical-risk parameters.

  • Backtest harness — AUC, Brier, KS, accuracy ratio, Hosmer-Lemeshow, calibration curves, rolling-window and walk-forward validation.

  • Excel integration — =MERTON_DD(equity, σE, debt, rf, T) in Excel Web, Mac (M365), and Windows via xlwings Server; classic xlwings UDF fallback for Windows desktop.

  • Observability — opt-in OpenTelemetry tracing (merton.obs.enable(...)) that pipes spans to any OTLP backend (Datadog, Honeycomb, Tempo, Jaeger, …).

  • Modern Python — full type hints, Pydantic settings, structured logging, Apache-2.0, cross-platform wheels for Python 3.11–3.14 (including free-threaded 3.13t/3.14t).

Performance

  • Cold import merton: ~500 ms on a 2024 M-series MacBook.

  • Single-firm fit: < 5 ms (typically ~1 ms once Numba is warmed).

  • 10 000-firm × 2 520-day panel: < 60 s on 8 cores via the Numba backend.

  • Portfolio Monte Carlo 5 000 firms × 100 000 sims: < 30 s with the CuPy GPU backend, < 5 min on CPU.

API stability

merton.__all__ is the public-API surface as of 1.0 and follows Semantic Versioning from this release onwards:

  • Minor releases (1.x) add new functions/extras, never remove documented behaviour.

  • Patch releases (1.x.y) fix bugs and improve performance.

  • Major release (2.x) is the only place deprecation removals can land. Deprecations are emitted through merton._deprecation with the removal target stamped in.

See API stability for the full contract.

Installation

pip install merton          # CPU-only NumPy + Numba JIT
pip install "merton[excel]" # Excel integration
pip install "merton[gpu]"   # CUDA GPU acceleration
pip install "merton[mcmc]"  # Bayesian calibration
pip install "merton[obs]"   # OpenTelemetry tracing
pip install "merton[all]"   # everything except [bloomberg] / [mlx]

On macOS / Linux / Windows; Python 3.11+ (including the free-threaded 3.13t / 3.14t builds). A conda-forge feedstock PR is open; once merged, conda install -c conda-forge merton will be the conda path.

What’s next

The 1.x roadmap focuses on:

  • Hybrid structural/reduced-form model (Duffie-Saita-Wang).

  • Polars-native zero-copy where pandas currently appears.

  • io, diagnostics, and viz namespaces graduate from their current homes (FirmPanel, MertonResult.summary, merton.reports).

  • CCAR and EBA packaged scenarios alongside the NGFS set.

  • Optional PyO3 / nanobind for the hottest kernels if benchmarks warrant the maintenance cost.

Credits and citing

merton is built on the shoulders of foundational papers — Merton 1974, Black-Cox 1976, Geske 1977, Leland-Toft 1996, Zhou 1997, Vassalou-Xing 2004, Bharath-Shumway 2008, Crosbie-Bohn 2003, the CreditGrades 2002 technical document, and Longstaff-Schwartz 1995. The package’s own citation metadata lives in CITATION.cff and a Zenodo DOI will be assigned on the 1.0 tag.

We’d love feedback. Open an issue or a discussion on the repo, or email the maintainers.

Happy modelling.