Monte Carlo vs. Deterministic: When to Use Which Model

September 1, 2025 • 11 min read Updated: January 24, 2025 - Monte Carlo engine fully implemented

Technical Note: Our Monte Carlo engine uses market-linked simulations calibrated to industry benchmarks. See complete methodology documentation for mathematical specifications and validation approach.

Your CFO wants a "realistic" portfolio projection. Should you:

A) Build a base case scenario? B) Build three scenarios (base, upside, downside)? C) Run 10,000 Monte Carlo simulations?

The answer: It depends on what question you're trying to answer.

The Two Approaches

Deterministic Modeling

What it is: You define specific assumptions and calculate one outcome.

Example:

• Assume 15% annual growth
• Assume exits in years 5-7
• Assume 2% management fees
• Calculate: NAV will be $47M in Q20

Pros:

• Easy to understand
• Fast to calculate
• Easy to explain to others
• Shows clear cause and effect

Cons:

• Gives false sense of precision
• Doesn't capture uncertainty
• Can't estimate probabilities
• Encourages anchoring on single number

Monte Carlo Simulation

What it is: You define probability distributions for key variables, then run thousands of simulations to see the range of possible outcomes.

Example:

• Growth: 10-20% (normal distribution, mean 15%, std dev 3%)
• Exit timing: Years 5-7 (uniform distribution)
• Run 10,000 simulations
• Result: NAV will be $42M-$52M (80% confidence interval)

Pros:

• Captures uncertainty
• Provides confidence intervals
• Shows full range of outcomes
• Better for risk management

Cons:

• More complex
• Slower to calculate
• Harder to explain
• Can give false sense of scientific precision

When to Use Deterministic

Deterministic models are great for:

1. Quick Analysis

Question: "What happens to our allocation if we commit $50M to this new fund?"

Why deterministic: You just need directional understanding, not precise probabilities.

What to model:

• Current portfolio: $500M
• New commitment: $50M
• Projected NAV impact: ~$550M (assuming full deployment)

Time: 30 seconds

2. Scenario Comparison

Question: "Should we shift from 30% VC to 25% VC and 5% more in credit?"

Why deterministic: You want to compare two specific strategies, not model all possibilities.

What to model:

• Scenario A: Current allocation
• Scenario B: Proposed reallocation
• Compare: IRR, TVPI, risk metrics

Time: 2 minutes

3. Communicating with Non-Technical Stakeholders

Question: "What will our portfolio look like in 5 years?"

Why deterministic: Your board doesn't want to hear about probability distributions and confidence intervals.

What to show:

• Base case: Expected outcome under reasonable assumptions
• Upside case: If things go well
• Downside case: If we face headwinds

Message: "We expect $600M, but could range from$500M to $700M."

4. Back-of-Envelope Checks

Question: "Does this deal make sense at all?"

Why deterministic: You're just checking if it's in the ballpark, not doing full diligence.

What to model:

• Quick IRR calculation
• Simple cashflow projection
• Rough allocation impact

Decision: "Yes, worth detailed analysis" or "No, not in our range."

When to Use Monte Carlo

Monte Carlo is better for:

1. Risk Management

Question: "What's the probability we'll need more than $200M in dry powder over the next 2 years?"

Why Monte Carlo: You need to understand the tail risks, not just the base case.

What to model:

• Capital call timing (uncertain)
• Distribution timing (even more uncertain)
• Net cash flow over 8 quarters
• Probability of cash shortfall

Result: "15% chance we'll need >$200M, 3$250M."

Action: "Let's maintain $225M in liquid reserves to be 97% confident."

2. Long-Term Strategic Planning

Question: "If we change our allocation strategy, what's the range of outcomes over 10 years?"

Why Monte Carlo: Too many variables to model deterministically over 10 years.

What to model:

• Market returns (vary by year)
• Manager performance (vary by fund)
• Exit timing (uncertain)
• Currency effects (volatile)
• New capital deployment (lumpy)

Result: "Expected outcome: $1.2B NAV. 80$900M-$1.6B."

Action: "Strategy is sound, but let's ensure we can weather the downside scenarios."

3. Stress Testing

Question: "What happens if we face another 2008-style crisis?"

Why Monte Carlo: Need to model compounding worst-case events across multiple variables.

What to model:

• Exit freeze (12-24 months)
• Valuation cuts (20-40%)
• Follow-on requirements (elevated)
• Currency volatility (increased)
• Credit tightening (reduced distributions)

Result: "In 5% worst-case scenarios, NAV drops to $550M and we need$180M in crisis reserves."

Action: "Let's ensure we have $200M+ in highly liquid reserves."

4. Portfolio Optimization

Question: "What's the optimal allocation to maximize risk-adjusted returns?"

Why Monte Carlo: Need to explore many possible allocations under uncertainty.

What to model:

• Multiple allocation strategies
• Varying market conditions
• Different manager performances
• Correlation effects

Result: "Optimal allocation: 35% VC, 30% buyout, 20% growth, 15% credit. Sharpe ratio: 1.8."

Action: "Shift allocation toward this target over next 18 months."

The Hybrid Approach: Best of Both

In practice, sophisticated portfolio managers use both:

Step 1: Deterministic Base Case

Start with a clean, simple deterministic model.

Assumptions:

• Expected returns by asset class
• Target deployment schedules
• Standard fee structures
• Current FX rates

Result: "Base case 5-year projection: NAV grows from $500M to$685M, IRR of 12.8%."

Step 2: Deterministic Scenarios

Build 2-3 alternative scenarios around the base case.

Upside Scenario:

• Returns: +300bps above base
• Exits: 6 months earlier
• FX: Favorable by 5%

Result: "Upside projection: NAV reaches $760M, IRR of 15.2%."

Downside Scenario:

• Returns: -300bps below base
• Exits: 12 months delayed
• FX: Unfavorable by 5%

Result: "Downside projection: NAV reaches $610M, IRR of 10.1%."

Step 3: Monte Carlo Risk Analysis

Run Monte Carlo to understand probability of outcomes beyond the scenarios.

Variable distributions:

• Returns: Normal (mean = base case, std dev = historical)
• Exit timing: Uniform (±12 months from base)
• FX: Historical volatility

Result: "80% confidence interval: $620M-$750M. 5% downside risk: Below $590M."

Step 4: Communicate with Layered Approach

To the board:

• "Base case: $685M"
• "Range: $610M to$760M"
• Simple language, deterministic scenarios

To the investment committee:

• "Expected: $685M"
• "80% confidence: $620M-$750M"
• "5% downside risk: <$590M"
• Include probability language

To the risk committee:

• Full Monte Carlo results
• Probability distributions
• Tail risk analysis
• Stress test scenarios

Everyone gets what they need at the right level of detail.

Common Mistakes

Mistake 1: Monte Carlo Everything

"We should run 10,000 simulations for every decision!"

Problem: Overkill for simple questions. You spend 30 minutes modeling what could be answered in 30 seconds.

When this happens: Junior analyst excited about new Monte Carlo tool.

Solution: Use deterministic for quick analysis, Monte Carlo for complex risk assessment.

Mistake 2: Over-Precise Deterministic

"Our 5-year NAV projection is $684,327,492."

Problem: False precision. You don't know within ±$50M, much less to the dollar.

When this happens: Excel model with too many decimal places.

Solution: Round to meaningful precision. "$685M" is more honest than "$684.3M".

Mistake 3: Black Box Monte Carlo

"The model says there's a 23.7% probability of..."

Problem: If you can't explain the model simply, stakeholders won't trust it.

When this happens: Analyst runs complex simulation without understanding underlying assumptions.

Solution: Always be able to explain Monte Carlo results with simple scenarios.

Mistake 4: Ignoring Model Assumptions

"Monte Carlo is sophisticated, so it must be more accurate."

Problem: Garbage in, garbage out. Bad assumptions produce bad results, whether deterministic or stochastic.

When this happens: Blind faith in models.

Solution: Validate assumptions with historical data and expert judgment.

Mistake 5: Single Scenario Anchoring

"The base case is $685M, so that's what we'll get."

Problem: Reality is uncertain. Presenting single number encourages false confidence.

When this happens: Management demands "THE number" for budgeting.

Solution: Always present ranges, even for deterministic models: "Base case $685M, realistic range$620M-$750M."

Practical Implementation

What You Need for Deterministic Modeling

Minimum:

• Excel with basic formulas
• Historical fund data
• Return assumptions

Time to build: 1-2 days for comprehensive model

Time to run: Seconds to minutes

Difficulty: Easy to moderate

What You Need for Monte Carlo

Minimum:

• Statistical software (R, Python) OR
• Specialized tool (like Nagare)
• Historical volatility data
• Distribution assumptions

Time to build: 1-2 weeks for robust Monte Carlo engine

Time to run: Minutes to hours (depending on complexity)

Difficulty: Moderate to difficult

How Nagare Handles Both

Deterministic:

• Quick scenario modeling (10 seconds)
• Base/upside/downside templates
• Real-time calculation updates

Monte Carlo:

• Pre-built simulation engine
• 10,000 iterations in under 2 minutes
• Confidence intervals automatically calculated
• Visual probability distributions

The Interface:

• Toggle between modes seamlessly
• Start with deterministic for exploration
• Switch to Monte Carlo for risk analysis
• Export results for presentations

Decision Framework

Use this flowchart:

Question: Do I need to make a quick decision?

• Yes: Deterministic → Simple base case
• No: Continue

Question: Am I presenting to non-technical audience?

• Yes: Deterministic → Base + upside/downside scenarios
• No: Continue

Question: Is risk management critical to this decision?

• Yes: Monte Carlo → Full probability analysis
• No: Continue

Question: Do I need precise probability estimates?

• Yes: Monte Carlo → Statistical confidence intervals
• No: Deterministic → Scenario analysis is sufficient

Real-World Example

Situation: Family office considering doubling allocation to emerging market VC.

Question 1: "If we double EM VC from 10% to 20%, how does that change our portfolio?"

Approach: Deterministic Time: 30 seconds Result: "NAV projection increases from $500M to$525M, but volatility increases." Decision: "Interesting, let's dig deeper."

Question 2: "What are the upside/downside cases?"

Approach: Deterministic scenarios Time: 5 minutes Result:

• Base: $525M
• Upside: $580M (EM VC pays off big)
• Downside: $470M (EM VC underperforms) Decision: "Range is wide. How confident should we be?"

Question 3: "What's the probability distribution of outcomes?"

Approach: Monte Carlo Time: 10 minutes Result:

• Expected: $525M
• 80% confidence: $480M-$570M
• 10% downside risk: <$460M
• Probability of outperforming base allocation: 48% Decision: "Roughly coin-flip whether this beats current allocation, but with more downside risk. Let's reduce target to 15% instead of 20%."

Final Check with 15% EM VC allocation:

Approach: Monte Carlo Result:

• Expected: $515M
• 80% confidence: $490M-$540M
• 10% downside risk: <$480M
• Probability of outperforming base allocation: 55% Decision: "Better risk/reward. Approved."

Total analysis time: 20 minutes

Without Monte Carlo, they would've either:

1. Made decision on gut feel (risky)
2. Spent 2 weeks doing manual scenario analysis (slow)

The Bottom Line

Deterministic modeling:

• Fast, simple, intuitive
• Great for exploration and communication
• Use for 80% of day-to-day analysis

Monte Carlo simulation:

• Rigorous, comprehensive, probabilistic
• Great for risk management and complex decisions
• Use for 20% of analysis where precision matters

Best practice: Start deterministic, escalate to Monte Carlo when needed.

Don't: Default to Monte Carlo for everything (overkill).

Don't: Use deterministic for major strategic decisions without understanding uncertainty (risky).

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