> For the complete documentation index, see [llms.txt](https://docs.suno.finance/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.suno.finance/financial-model/sensitivity-analysis.md).

# Sensitivity analysis

A NAV is a point inside a space of uncertainty. Every valuation therefore ships with a tornado analysis: the engine re-runs with one lever perturbed at a time, and the resulting deltas are published next to the base case. The tornado freezes into every published version, each row recording the exact perturbation it applied, so a valuation permanently declares the uncertainty known when it was issued.

The analysis answers three questions: which input moves the NAV most per unit of change (where model risk lives), what the NAV would be under stated alternative conditions, and what severe-but-plausible cases imply for protocol buffers and mechanism design.

#### Directionality

Each lever belongs to one of three types, which determines its justification and whether it runs in one direction or two.

Quantiles test both tails of an observable distribution, at empirically calibrated magnitudes that may come out asymmetric. Parameters test both ends of a defensible disagreement range, symmetric by construction. Scenarios test a discrete event in its single real direction, since a regime change has no meaningful mirror image.

#### Calibration of the quantile levers

**Production.** Monthly specific production across the portfolio (1,394 project-months, 30 projects, validated invoices) shows a pooled relative dispersion of `σ_month ≈ 20%`. Annualizing under bounds for the serial correlation of weather, `σ/√12` if independent and `σ/√6` under moderate autocorrelation, gives `σ_annual ≈ 5.7` to `8.1%`; the model uses 6%. The P90/P10 pair follows as `∓ 1.28σ ≈ ∓ 8%`, recalibrated annually as telemetry accumulates.

**Exchange rate.** FX quantiles are calibrated per portfolio currency against its official exchange-rate series. For the current portfolio (COP), rolling 12-month changes over the full available history (1991–2026, 405 windows) have quantiles `Q_10 = -12.1%`, `Q_50 = +4.8%`, `Q_90 = +24.4%`. The model's PPP path already carries the secular depreciation drift, which sits near the median, so the sensitivity shock is the quantile centered on the median: devaluation +20%, appreciation −17%. The asymmetry is in the data, not assumed: devaluations jump, with crisis episodes reaching +37% in a year, while appreciations crawl.

#### The lever set

| Lever                                 | Type      | Magnitude                                                 | Basis                                                                                     |
| ------------------------------------- | --------- | --------------------------------------------------------- | ----------------------------------------------------------------------------------------- |
| Production P90 / P10                  | quantile  | −8% / +8%                                                 | Own-portfolio telemetry, `1.28σ`                                                          |
| FX devaluation P90 / appreciation P10 | quantile  | +20% / −17%                                               | Official exchange-rate series of the portfolio currency (COP: 1991–2026), median-centered |
| Discount rate                         | parameter | ±100 bps                                                  | Width of the defensible range on the rate build-up                                        |
| OPEX                                  | parameter | ±20%                                                      | Interim range, to be replaced by backtesting statistics                                   |
| Import tax on replacements            | scenario  | full local VAT + duty (19% + 5% in the current portfolio) | Loss of the renewable-energy import exemption                                             |
| Certificate price                     | scenario  | → 0                                                       | Collapse of the voluntary certificate market                                              |
| Combined adverse / favorable          | scenario  | production ∓8%, rate ±100 bps, OPEX ±20%                  | The three continuous levers stressed jointly, both directions                             |

#### Combined scenarios and the NAV range

The combined rows are full engine runs with all three perturbations applied at once, not sums of individual deltas. Interactions matter: a leaner cash flow bears the inflated OPEX proportionally harder, while a higher rate discounts flows that are already smaller. For a representative project, the individual adverse deltas of −11.2%, −7.4% and −7.8% sum to −26.3%, but the joint run yields −24.8%. Only the full run can say which way the interactions cut.

The pair brackets the NAV in a declared range; the same representative project, with a base NAV of 1.096, brackets to \[0.824, 1.416]. The adverse bound informs buffer sizing, the favorable bound completes the picture for both sides of issuance and redemption. Discrete events stay out of both combinations, since stacking regime changes onto a bad year stops being a stress test, and FX is reported alone because its correlation with the other levers is ambiguous: devaluation episodes in the portfolio's markets tend to coincide with local inflation that the PPA re-indexes.

Symmetric inputs still produce asymmetric outputs. Present value is convex in the discount rate, so −100 bps gains more (+8.5% in the representative case) than +100 bps loses (−7.4%). Publishing both sides makes the convexity visible.

#### What this analysis is not

The tornado is not a confidence interval on the NAV. A joint interval would need a correlation structure across all inputs that no available data supports. Per-lever quantiles, calibrated on observable data and individually checkable, say more than a simulated distribution built on correlations nobody can verify.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.suno.finance/financial-model/sensitivity-analysis.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
