Conditional VaR Calculator
The formula
What the conditional var tells you
The 5×5 matrix the risk industry actually uses, for conditional var: gross likelihood × impact, discounted by how well your controls actually work.
Calculation: gross = likelihood × impact (1–25); net = gross × (1 − controls). Enter your inputs and every substitution is shown below the result.
Reading the result
Scores rank attention; they do not price losses. Anything 15+ gets a treatment plan with a named owner.
Rules of thumb: Most organisations carry 3–5 reds; more usually means likelihood inflation, not danger.
Limitations and best practice
Control ratings drift optimistic — audit one control’s evidence and re-mark the rest against it.
This calculator gives an estimate based on standard formulas; it is not professional financial, medical, legal or engineering advice. Verify anything you plan to act on with a qualified professional and your own records.
Frequently asked questions
Why discount by controls?
Do these scores compare across teams?
Source & methodology
ISO 31000-aligned qualitative risk matrices.
Conditional VaR explained: formula, worked example and honest limits
You are here because you need a specific number, and you want to see how it was produced before you rely on it. Searchers land on a Conditional VaR page for three reasons — to plan, to compare options, and to check that somebody else’s arithmetic is honest. This tool serves all three, and everything after the calculator explains the model instead of hiding it.
Most confusion about Conditional VaR comes from mixing up the input you control with the input you merely observe. The formula treats them identically, but your decisions should not: one deserves measurement, the other deserves a defensible estimate.
It helps to separate the mechanics from the interpretation. The mechanics are fixed: gross = likelihood × impact (1–25); net = gross × (1 − controls). Anyone, anywhere, entering the same inputs will get the same 9.0 / 25. The interpretation is where experience counts: whether 9.0 / 25 is good, normal or alarming depends on comparisons covered below, and that is the part most articles about Conditional VaR never explain.
What makes a calculation page trustworthy is that its copy is derived from its own arithmetic: here net risk score = 9.0 / 25, recomputed every time a field changes, and the sensitivity section ranks the inputs that move the result — Likelihood (1 = rare … 5 = almost certain) first, at ≈10.0 % of the headline for a ten-percent error either way in that input. Read the sections in order and you will be able to reproduce every figure by hand.
Inside the Conditional VaR calculation: formula and every input
The engine implements a single stated model: gross = likelihood × impact (1–25); net = gross × (1 − controls). There is no hidden coefficient and no login-gated precision — the formula printed is the formula executed, which is also what makes the result citable.
Likelihood (1 = rare … 5 = almost certain) (example value 3) is a quantity rather than a category — precision here is cheap and worth having. The example value is there to demonstrate the mechanics, not to describe your situation — overwrite it.
Impact (1 = negligible … 5 = severe) (example value 5) is a measured number in the unit named by the label; the formula does not convert for you. This input feeds the formula directly; a defensible figure here is worth more than false precision anywhere else.
Control effectiveness (0–100 %) (example value 40) is a measured number in the unit named by the label; the formula does not convert for you. Check the convention first: if the source says “6.5 % per year”, type 6.5 and let the formula do the rest.
The default Conditional VaR calculation, written out in full
The tool prints its own working; this is the same arithmetic on the example values, reproduced so you can follow it by hand. Starting figures: Likelihood (1 = rare … 5 = almost certain), Impact (1 = negligible … 5 = severe), Control effectiveness (0–100 %). Run to the end, the net risk score resolves to 9.0 / 25 — Amber — needs an owner and a date on the bands this tool uses.
- Gross = 3 × 5 = 15
- Net = 15 × (1 − 40 %) = 9.0
Gross score — 15 / 25. Read next to the headline, this is the figure that shows the shape of the trade-off.
Control credit — 6.0 pts. Read next to the headline, this is the figure that shows the shape of the trade-off.
Likelihood × impact band — High×High. Compare it against the main result of 9.0 / 25 before drawing conclusions.
Re-running with your own figures is instant; re-running by hand once is the best way to know the tool is telling you the truth.
How far Conditional VaR can reasonably move
Single-point answers invite false confidence, so the same engine was run across a band around the input that matters most here. Each row is a full recalculation of Conditional VaR, not an interpolation.
| Likelihood (1 = rare … 5 = almost certain) setting | Net risk score |
|---|---|
| 80 % of the example value | 7.2 / 25 |
| 90 % of the example value | 8.1 / 25 |
| example inputs | 9.0 / 25 |
| 110 % of the example value | 9.9 / 25 |
| 120 % of the example value | 10.8 / 25 |
That spread — 7.2 / 25 at the low end, 10.8 / 25 at the high — is what a careful read of Conditional VaR looks like: a band you can state out loud, with the reason for each edge. Reproduce it in the form above by editing Likelihood (1 = rare … 5 = almost certain) alone; the substitution steps will follow your figures exactly.
What actually moves your Conditional VaR
Nobody should have to guess which input matters. The engine was re-run once per field below — ten percent up, ten percent down, everything else frozen — and the results ranked by how far they moved the net risk score. It is the fastest honest answer to “what matters most in Conditional VaR?” — and it is specific to these defaults, because sensitivity moves with the starting point.
- Likelihood (1 = rare … 5 = almost certain): ten percent up gives 9.9 / 25, ten percent down gives 8.1 / 25 — ≈10.0 % of the net risk score either way
- Nudge Impact (1 = negligible … 5 = severe) by ±10 % and Conditional VaR lands between 8.1 / 25 and 9.0 / 25 (sensitivity ≈10.0 %)
- Control effectiveness +10 % → Conditional VaR = 8.4 / 25 · −10 % → 9.6 / 25 (the net risk score shifts ≈6.7 %)
Likelihood (1 = rare … 5 = almost certain) is the dominant lever here: ten percent either way moves the result by about 10.0 %, from 8.1 / 25 to 9.9 / 25. Measure that input from a document rather than from memory, and Impact (1 = negligible … 5 = severe) is effectively tied with it (≈10.0 % for the same nudge), so verify the pair together. The remaining fields can sit at safe defaults without materially changing the answer — knowing which is which is what separates a careful calculation from an anxious one.
What a Conditional VaR of 9.0 / 25 means in practice
Nobody acts on a bare figure — they act on a figure with a comparison attached. On the example inputs the band reads “Amber — needs an owner and a date”; your own figures place you on a different rung of the same ladder. How much of that band is luck? On Likelihood (1 = rare … 5 = almost certain) alone ten percent either way covers 8.1 / 25 to 9.9 / 25. The practical reading rules: read the band as a starting hypothesis, then test it: change the two dominant inputs by ten percent and see whether the verdict survives. Verdicts that survive stress are the ones worth acting on.
Set Conditional VaR beside its neighbours and it usually plays a specific role: this figure establishes the size of something, and the neighbouring tools establish whether that size is acceptable. Skipping the first step makes the second one arbitrary.
Common mistakes with Conditional VaR (and the fix)
Control ratings drift optimistic — audit one control’s evidence and re-mark the rest against it. This calculator gives an estimate based on standard formulas; it is not professional financial, medical, legal or engineering advice. Verify anything you plan to act on with a qualified professional and your own records.
Reusing a Conditional VaR from last season. In Investing & Markets the inputs are the thing that drifts; the formula does not.
Attention is also misallocated between fields. Having measured this engine, we can say where it belongs: Likelihood (1 = rare … 5 = almost certain) first, everything else second.
A final trap is presentation: reporting Conditional VaR to more decimal places than the inputs support. The tool prints what the arithmetic gives; you should quote only what your least precise field justifies.
Conditional VaR verification checklist
- Record the date and the inputs alongside the number so future-you knows what was assumed.
- Recompute once with the dominant input deliberately set low, and see whether the decision changes.
- Check Likelihood (1 = rare … 5 = almost certain) before anything else: it is the measured dominant input, ≈10.0 % of Conditional VaR per ten percent.
- Compare the result with one independent method or published benchmark.
- Verify each input against its original source document, not memory.
- Run the ±10 % stress on the top two levers and check that your conclusion survives both.
Five steps from raw inputs to a figure you can defend
Using a calculator well sounds trivial, but the difference between a decision-grade figure and a throwaway one lives entirely in the routine. The routine for Conditional VaR takes a minute to learn and applies to every input above.
Step 1 — collect, then type. Everything this tool asks for is listed above (likelihood (1 = rare … 5 = almost certain), impact (1 = negligible … 5 = severe), control effectiveness (0–100 %)). Pull each figure from something you can point at — paperwork, a dashboard, a label — before touching a field. Guesswork at the input stage cannot be repaired at the interpretation stage. If the verdict changes with a ten-percent nudge, report the nudge alongside the number.
Step 2 — baseline before exploration. Get one defensible Net risk score on screen before you start nudging numbers. Without a baseline, every variation looks interesting and none of them means anything. When in doubt, prefer the boring input: the interesting one is usually the one you misread.
Step 3 — isolate your variables. Change one field, observe, revert. The live sensitivity list does the ranking for the default inputs; verify it against your own, because dominance shifts when the starting point shifts. If the verdict changes with a ten-percent nudge, report the nudge alongside the number.
Step 4 — stress before you commit. A decision that survives a ten-percent squeeze on its two biggest inputs needs no luck to be sound. One that does not survive is a decision about hope, not about arithmetic. Re-running a number costs seconds; retracting a decision costs considerably more.
Step 5 — record the context, not just the number. Note today’s date and the inputs alongside the 9.0 / 25 output. Six months from now the context is what turns this figure into a trend. A figure with a date beside it becomes evidence; without one it stays an anecdote.
Where Conditional VaR matters in practice
Numbers earn their keep in conversations, and Conditional VaR is no exception. In Investing & Markets discussions around Portfolio Management, this figure decides whether an option stays on the table: it converts an opinionated debate into an arithmetic one, and arithmetic is the only referee both sides accept. A figure with a date beside it becomes evidence; without one it stays an anecdote.
Used properly this is a five-minute filter that saves weeks. Compute Conditional VaR, reject what fails, and only then bring in the expensive analysis for the survivors. When two sources disagree, it is almost never the arithmetic that differs.
There is a personal-administration use people overlook: periodic self-checking. Conditional VaR quietly drifts as life changes — rates move, bodies change, costs compound — and re-running it a few times a year catches drift while correction is still cheap. A figure nobody can reproduce is a figure nobody will act on twice.
Conditional VaR — frequently asked questions
What affects Conditional VaR the most?
Measured on this engine, Likelihood (1 = rare … 5 = almost certain) is the dominant lever: moving it ±10 % shifts the net risk score across 8.1 / 25 to 9.9 / 25 — about 10.0 % of the headline, with Impact (1 = negligible … 5 = severe) next at ≈10.0 %. The ranked list in the sensitivity section is computed live, not asserted from a template.
Do impact (1 = negligible … 5 = severe) and likelihood (1 = rare … 5 = almost certain) matter equally?
On these inputs, yes: the two measured within a rounding error of each other — ≈10.0 % and ≈10.0 % of the net risk score per ten-percent change. Treat them as one pair to verify together rather than ranking them.
What is a typical Conditional VaR?
On the worked example above Conditional VaR comes out at 9.0 / 25. Treat that as one point on a distribution rather than a norm: the bands, the companion outputs and the sensitivity ranking tell you how wide the plausible range really is.
How do I calculate Conditional VaR by hand?
Three steps: write the formula (gross = likelihood × impact (1–25); net = gross × (1 − controls)), insert your values, and simplify. The worked example section shows exactly that chain for the default inputs; replicating it once on paper teaches you more about Conditional VaR than any number of calculator runs.
What happens to Conditional VaR if likelihood (1 = rare … 5 = almost certain) changes by 10 %?
Try it in the form above and the printed substitution will follow you; the pre-computed answer for the defaults is 8.1 / 25 to 9.9 / 25 across the ten-percent probe, i.e. ≈10.0 % sensitivity on the net risk score.
Is the Conditional VaR result of 9.0 / 25 good or bad?
The tool answers “what”, not “whether”: 9.0 / 25 carries the verdict “Amber — needs an owner and a date” for the default case, which is a starting interpretation rather than a verdict on your situation. Re-run with your own figures and compare the bands.
Does Conditional VaR account for fees and taxes?
Only what you put in. Fees belong inside Likelihood (1 = rare … 5 = almost certain) where they shrink returns before compounding touches them; taxes are outside the scope of a pre-tax Conditional VaR and should be layered on top before decisions.
How often should I check Conditional VaR?
Whenever an input changes by more than a few percent, and otherwise on the rhythm of your decisions — before a purchase, a review, a renewal. Conditional VaR is a snapshot, not a subscription; stale snapshots are the most common silent error in Investing & Markets planning.
Why discount by controls?
A 20-score hazard half-mitigated is a 10, and 10s behave like a different risk class.
Do these scores compare across teams?
Only if the likelihood and impact scales are calibrated to the same definitions — the matrix is shared, the calibration is organisational.
Can this tool see the numbers I type?
It cannot. There is no submission step: the calculation happens in your browser and the result disappears when you close the page. Treat the fields above as a scratchpad rather than a form.
The vocabulary of Conditional VaR
- Gross score
- 15 / 25 on the example inputs — a companion figure printed beside the headline of Conditional VaR so that no result is read in isolation.
- Control credit
- 6.0 pts on the example inputs — a companion figure printed beside the headline of Conditional VaR so that no result is read in isolation.
- Likelihood × impact band
- High×High for the Conditional VaR worked case; it sits next to the main result because it carries context the headline leaves out.
- Net risk score
- The primary output of this tool: 9.0 / 25 for the default inputs, computed exactly from gross = likelihood × impact (1–25); net = gross × (1 − controls).
- Worked example
- The calculation written out in full so that nothing on this page has to be taken on trust.
- Verdict band
- The plain-language label — here “Amber — needs an owner and a date” — mapping the computed figure onto experience-based ranges for Investing & Markets.
- Sensitivity (±10 %)
- How far the Conditional VaR moves when a single input is nudged ten percent either way; the ranked list on this page is computed live from this engine.
Methodology, sources and limits
The model is published arithmetic implemented exactly as written: gross = likelihood × impact (1–25); net = gross × (1 − controls). Source of record: ISO 31000-aligned qualitative risk matrices. Specialist software layers extra coefficients on top of the same algebra; none are hidden here, and that transparency is what makes a disagreement traceable to a specific input instead of a black box.
Nothing here replaces a qualified professional, and nothing here is approximate either — the arithmetic is exact, and the honesty is in the inputs you supply. Nothing is smoothed, weighted or adjusted behind the scenes; where judgement enters, this page says so and names the input responsible.
Next steps after the calculation
Save the number together with its inputs and today’s date; a Conditional VaR figure without context is unusable the moment an input changes.
Whoever you are (people who need one trustworthy number before their next decision), the next step is the same: replace the example inputs with your own, note the date, and re-run whenever a figure moves. In Investing & Markets, a dated figure is a trend, and an undated one is a rumour.