Three Questions Monte Carlo Answers (That Your Spreadsheet Cannot)

October 23, 2025 • 10 min read

You're in an investment committee meeting. Three questions come up:

1. "Should we commit to this new fund?"
2. "What's our liquidity risk over the next 2 years?"
3. "Are we properly diversified?"

Your Excel model gives you one answer for each. Monte Carlo gives you confidence levels.

Here's why that matters.

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Question 1: Should We Commit?

The Spreadsheet Answer

Fund: TechVentures Growth IV
Commitment: $50M
Expected TVPI: 2.8x
Expected IRR: 16.2%
5-year NAV projection: $685M

Recommendation: ✅ Approve

The problem: What if exits are delayed? What if growth is slower? What if the market crashes?

Your spreadsheet shows you one scenario. Reality offers thousands.

The Monte Carlo Answer

Same fund, but we run 1,000 simulations with varying conditions:

Now you can see:

Expected TVPI: 2.8x
80% confidence interval: 2.1x - 3.6x
10% downside risk: Below 1.9x (still positive)
10% upside potential: Above 3.6x

Probability of TVPI > 2.0x: 88%
Probability of TVPI > 3.0x: 42%

The Real Decision

Question shifts from: "What will happen?" (unknowable)

To: "What's the range of outcomes, and can we handle the downside?"

Example decision logic:

If P10 > 1.5x:
    ✅ Good risk/reward (worst case still positive)
    → Approve

If P10 = 1.0-1.5x:
    ⚠️ Marginal (breakeven in bad scenarios)
    → Approve with reduced commitment or pass

If P10 < 1.0x:
    ❌ Significant loss risk
    → Pass

For TechVentures Growth IV:

• P10 = 1.9x ✅
• Even in worst 10% of scenarios, we make 1.9x return
• Decision: Approve full $50M commitment

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Question 2: What's Our Liquidity Risk?

The Scenario

You have:

• $500M portfolio across 12 funds
• $150M in liquid reserves
• Planning to commit $200M over next 2 years

The question: Will we have enough cash?

The Spreadsheet Answer

Expected capital calls (8 quarters): $180M
Expected distributions (8 quarters): $95M
Net cash need: $85M

Current reserves: $150M
Surplus: $65M

Conclusion: ✅ We're fine

The problem: This is one path. What if all your funds call capital at once? What if distributions dry up?

The Monte Carlo Answer

Run 1,000 simulations of next 8 quarters:

Results:

Expected net cash need: $85M (matches spreadsheet)

But...

Probability we'll need >$150M: 18%
Probability we'll need >$175M: 8%
Probability we'll need >$200M: 3%

95th percentile cash need: $165M

Translation:

• Your $150M reserve is insufficient for 18% of scenarios
• You have an 18% chance of liquidity crisis

The Real Decision

Risk tolerance assessment:

Decision framework:

If Risk-Averse (family office):
    → Maintain $185M (5% shortfall risk)
    → Cost: $5.55M/year
    → Worth it for peace of mind

If Risk-Tolerant (institutional LP):
    → Maintain $165M (10% shortfall risk)
    → Cost: $4.95M/year
    → Accept some risk for better returns

This family office chose: $180M reserves (8% risk)

Why Monte Carlo mattered: Without it, they would've stayed at $150M and faced 18% crisis risk.

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Question 3: Are We Diversified?

The Portfolio

Current: 8 VC funds
Question: Should we add 4 more VC funds for diversification?

The Intuition

"More funds = More diversification = Lower risk"

But how much lower?

The Spreadsheet Answer

8 funds average TVPI: 3.2x
12 funds average TVPI: 3.2x (same)

Conclusion: No material benefit?

The problem: Spreadsheets don't show volatility reduction.

The Monte Carlo Answer

Run portfolio-level simulations:

With 8 funds:

Portfolio TVPI: 3.2x (mean)
Portfolio std dev: 0.52x
10% downside: 2.4x

With 12 funds (adding 4 more):

Portfolio TVPI: 3.2x (mean - unchanged)
Portfolio std dev: 0.41x (21% reduction!)
10% downside: 2.6x (improvement!)

Visualization:

Purple band (narrower) = 12 funds Blue band (wider) = 8 funds

The Real Decision

Adding 4 funds:
- Same expected return (3.2x)
- 21% lower volatility
- Better downside protection (2.6x vs 2.4x)

Cost:
- $40M total commitment
- 4 more GP relationships
- Additional monitoring overhead

Benefit:
- $200M less downside risk (at P10)
- Better risk-adjusted returns
- More stable portfolio

Decision: ✅ Add 4 funds

Why Monte Carlo mattered: Showed quantifiable volatility reduction that justified the additional complexity.

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The Pattern: From "What?" to "What If?"

Spreadsheet Thinking

"What will NAV be?"
"What will IRR be?"
"What will cash need be?"

Assumes certainty. Gives single answers.

Monte Carlo Thinking

"What's the range of NAV outcomes?"
"What's the probability of IRR > 15%?"
"What's the 90% confidence cash need?"

Embraces uncertainty. Gives probability distributions.

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Real Portfolio Example

The Setup

Portfolio:

• $650M across 15 private funds
• Planning $120M new commitment (TechVentures Growth IV)
• Current reserves: $180M

Three Questions, Three Answers

1. Should we commit $120M?

Monte Carlo P10 downside: 1.9x TVPI ✅ Approve (good risk/reward even in bad scenarios)

2. Will we have liquidity?

95th percentile cash need (next 8 quarters): $195M ⚠️ Current$180M reserves insufficient → Increase to $200M

3. Does this improve diversification?

Adding this fund:

• Portfolio std dev: 0.48x → 0.44x (9% reduction)
• P10 portfolio TVPI: 2.1x → 2.2x (better downside) ✅ Yes, improves diversification

The Decision

Approved with conditions:

1. ✅ Commit to TechVentures Growth IV ($120M)
2. ✅ Increase reserves to $200M (from$180M)
3. ✅ Defer one other $30M commitment to maintain liquidity

Total analysis time: 45 minutes (vs. 3 days with spreadsheets)

Confidence level: High (based on probabilistic analysis, not gut feel)

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Common Objections

"Monte Carlo is too complicated"

Reality: The output is simpler than deterministic.

Deterministic: "We built three scenarios with different assumptions about growth, exits, and fees..." (Requires explaining why you chose these specific scenarios)

Monte Carlo: "We ran 1,000 simulations. Here's the distribution." (Shows actual range of possibilities)

"We don't have time for this"

Reality: Monte Carlo is faster than building multiple scenarios.

Building 3 scenarios manually: 2-3 hours Running Monte Carlo: 2 minutes

Plus you get 1,000 scenarios instead of 3.

"Our board won't understand it"

Reality: Boards already understand weather forecasts.

Don't say: "We ran stochastic simulations with parameter distributions..."

Do say: "Like a weather forecast, we're showing a range of outcomes:

• Expected: $685M (like 'most likely temperature')
• Range: $620M-$750M (like 'high/low temps')
• Downside: $590M (like '10% chance of rain')"

Boards get it immediately.

"It's overkill for our simple portfolio"

Reality: If you have >3 funds or >$100M, it's not overkill.

When spreadsheets are fine:

• 1-2 funds
• Small portfolio (under $50M)
• Very early stage

When Monte Carlo pays off:

• 3+ funds (correlation matters)
• Material commitments ($20M+)
• Liquidity planning needed

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How to Get Started

Step 1: Run One Simulation

Pick your largest fund. Run Monte Carlo.

Look for:

• Is P10 still acceptable? (downside check)
• Is range (P90-P10) huge? (uncertainty check)
• Does P50 match your intuition? (sanity check)

Time: 5 minutes

Step 2: Compare to Your Spreadsheet

Your spreadsheet says: TVPI = 2.5x Monte Carlo says: P50 = 2.5x, range 1.8x-3.8x

Questions:

• Did you know the range was that wide?
• Are you comfortable with 1.8x downside?
• Does this change your decision?

Step 3: Use It for Next Decision

Next time you're evaluating a commitment:

Skip building 3 scenarios. Run Monte Carlo instead. Make decision based on P10/P50/P90.

You'll notice:

• Faster analysis
• More confident decisions
• Better risk awareness

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Try It Yourself

We've built Monte Carlo into our platform. Here's what you get:

Input:

• Fund parameters (commitment, target returns, fees)
• Market assumptions (equity returns, volatility, correlations)
• Configuration (pacing, smoothing, iterations)

Output:

• Complete probability distribution (P10-P90)
• Time series projections (quarter-by-quarter)
• Downside risk analysis
• Portfolio-level correlation

Time: 2 minutes to run 1,000 simulations

Start free trial → No credit card required.

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The Bottom Line

Three questions Monte Carlo answers better than spreadsheets:

1. "Should we commit?" → Answers with confidence intervals, not point estimates

2. "What's our liquidity risk?" → Answers with probabilities, not single paths

3. "Are we diversified?" → Answers with volatility metrics, not averages

The shift: From "What will happen?" (unknowable) To "What's the range of possibilities?" (knowable)

The result: Better decisions. More confidence. Less surprise.

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Next in series:

• Reading the Distribution: What P10, P50, P90 Actually Mean → (Coming next week)
• Stress Testing: The 2008 Playbook → (Coming soon)
• Explaining Monte Carlo to Your Board → (Coming soon)

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Want to see how Monte Carlo would analyze your portfolio?

Book a demo → We'll run your actual portfolio through Monte Carlo and show you the insights (free, 30 minutes).

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Published: October 23, 2025 Category: Portfolio Management Tags: Monte Carlo, Decision Making, Risk Management, LP Best Practices

Part of our "Practical Monte Carlo" series for fund managers and LPs.