How We Validate Our Monte Carlo Engine

October 30, 2025 • 6 min read

For Technical Readers: Complete mathematical specifications, test suite, and validation protocols are available in our Monte Carlo Methodology and Financial Modeling Methodology documents.

When you're using Monte Carlo simulations to model your $1.2B private fund portfolio and make $50M commitment decisions, you need confidence the numbers are right.

Here's how we validate our Monte Carlo engine.

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Why Validation Matters

Scenario: You run Monte Carlo on your portfolio. It shows:

• P50 (median): Final portfolio value $180M
• P90 (upside): $245M
• P10 (downside): $125M

Question: Can you trust these numbers to make decisions?

If the engine is wrong:

• You might over-commit (liquidity crisis)
• You might under-commit (missed opportunities)
• Your IC trusts bad projections

Validation ensures: The statistical properties are correct and results are reliable.

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What We Validate (6 Key Areas)

1. Mean Matching

Question: Does the average outcome match expectations?

Test: Run 10,000 simulations. Sample mean should equal parameter mean (within statistical bounds).

Result: Error <0.05% (well within acceptable range)

Why it matters: If mean is off, all your projections are systematically biased.

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2. Variance Matching

Question: Is the spread of outcomes realistic?

Test: Sample variance should match theoretical variance.

Result: Error <3% (within statistical tolerance)

Why it matters: If variance is wrong, your P10/P90 bands are meaningless (either too narrow or too wide).

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3. Correlation Preservation

Question: Do market movements correlate correctly?

Test: When equity markets drop, PE/VC returns should also drop (empirically: β ≈ 1.2-1.4).

Result: Correlation maintained within ±0.05

Why it matters: If correlation is broken, you underestimate portfolio risk (diversification illusion).

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4. Convergence

Question: Do results stabilize with more iterations?

Test: Standard error should decrease as 1/√N.

Result: Converges as expected

Why it matters: You need to know: "Is 1,000 iterations enough? Or do I need 10,000?"

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5. Benchmark Comparison

Question: Do results match industry data?

Test: Compare our projections to Cambridge Associates and Preqin data.

Results:

• Exit timing: Within 5-15% of industry medians (PE Buyout: 4.9% error vs 5.8y benchmark)
• TVPI/IRR: Derived from calibrated exit timing + growth assumptions
• DPI timing: Matches vintage curves

Why it matters: If you're off vs. industry, your projections are unrealistic.

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6. Reproducibility

Question: Do you get same results twice?

Test: Same random seed → identical results.

Result: Bitwise identical outputs

Why it matters: For audit trail and debugging. You need to recreate past projections exactly.

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Validation Results Summary

Statistical Correctness:

• Mean error: <0.05%
• Variance error: <3%
• Correlation preservation: ±0.05
• Convergence: 1/√N as expected

Industry Benchmarks:

• Exit timing: Calibrated to Cambridge Associates/Preqin (5.8y median PE Buyout, 4.9% error)
• Growth assumptions: Derived from target TVPI and exit timing
• DPI patterns: Match industry vintage curves

Test Coverage:

• 700+ automated test cases across 87 test files
• All critical paths covered
• Runs on every code change

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How This Helps You

When you run Monte Carlo in Nagare:

You see:

• P10/P50/P90 TVPI projections
• Probability bands over time
• Downside risk assessment

You can trust:

• Statistical properties are correct (mean, variance, correlation)
• Benchmarked against industry data (Cambridge Associates, Preqin)
• 700+ automated test cases ensure accuracy
• Transparent methodology (published at /docs/monte-carlo)

You make decisions with confidence:

• "What's my downside risk if I commit $15M?" (P10 scenario)
• "What's a realistic outcome?" (P50 median)
• "What if everything goes well?" (P90 upside)

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Validation vs. Accuracy

Important distinction:

Validation = Statistically correct

• Mean matches
• Variance matches
• Correlation preserves

Accuracy = Matches reality

• Requires good input assumptions
• Markets might not follow normal distribution
• Black swans exist

We validate: Statistical correctness (engine works as designed)

You provide: Realistic assumptions (based on your market views)

Together: Reliable probabilistic forecasts

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Continuous Validation

How we maintain accuracy:

1. Automated testing: 700+ test cases run on every code change
2. Benchmark updates: Recalibrate against latest industry data annually
3. Expert review: External validation by financial mathematics specialists
4. User feedback: Family offices report if projections feel off vs. reality

Result: Engine stays accurate as markets evolve.

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The Bottom Line

Monte Carlo is powerful but can be wrong if:

• Implementation has bugs
• Statistical properties don't match theory
• Parameters aren't calibrated to reality

Our validation framework ensures:

• Implementation is correct (700+ automated test cases)
• Statistics match theory (mean, variance, correlation validated)
• Parameters match industry (benchmarked vs. Cambridge Associates, Preqin)

You get: Confidence to use Monte Carlo for $50M decisions.

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Want deeper technical details? Monte Carlo Methodology - Full mathematical specification and test documentation

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Learn More

Want deeper technical details?

• Monte Carlo Methodology - Full mathematical specification
• Financial Modeling System - Complete modeling framework

Want to see it in action?

• Schedule demo - We'll run Monte Carlo on your portfolio
• Start free - Try Monte Carlo yourself (Institutional tier)

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Related Reading:

• Building a Market-Linked Monte Carlo Engine
• Monte Carlo vs Deterministic: When to Use Each
• Reading Monte Carlo Distributions