Volatility Controlled Indexes – ChatGPT Discussion

By Bill Boersma

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Bill: How have volatility-controlled indexes performed compared to standard S&P 500 indexes 

How Volatility-Controlled Indexes Have Performed Compared to the Standard S&P 500

Overview

Volatility-controlled indexes (also called target-volatility indexes or risk-control indexes) are increasingly used inside structured products and indexed universal life (IUL) policies. These indexes attempt to maintain a relatively stable level of volatility—commonly around 5%, 6%, or 10%—by dynamically adjusting exposure between equities and low-risk assets such as Treasury bills or cash equivalents.

In contrast, the S&P 500 is a traditional equity index that maintains full exposure to the stock market regardless of volatility conditions. The difference in structure leads to materially different long-term performance characteristics.

Structural Differences

Standard S&P 500

• Maintains 100% exposure to U.S. equities at all times.
• Experiences the full magnitude of both market gains and losses.
• Long-term returns include both price appreciation and dividends (although many insurance illustrations remove dividends).

Volatility-Controlled Index

• Targets a specific volatility level (typically 5–10%).
• Uses a dynamic allocation model:
  • When market volatility rises, the index reduces equity exposure and shifts toward cash or Treasuries.
  • When volatility falls, the index increases equity exposure.
• Often includes a daily rebalancing mechanism to maintain the volatility target.

Historical Performance Patterns

Across most historical periods, volatility-controlled indexes have underperformed the standard S&P 500 in absolute returns. This occurs for several structural reasons.

1. Reduced Equity Exposure

During periods of elevated market volatility—such as financial crises or rapid market corrections—the volatility-controlled index typically reduces equity exposure significantly. In extreme cases, exposure may drop to 20–40% equities, with the remainder allocated to cash.

If markets rebound quickly after volatility spikes (as occurred after the 2009 financial crisis and the 2020 COVID crash), the index participates less fully in the recovery, creating a performance drag.

2. Volatility Drag from Daily Rebalancing

Because the index adjusts exposure daily, it may repeatedly buy high and sell low in volatile markets. This rebalancing effect can create a structural volatility drag, reducing cumulative returns relative to a fully invested equity index.

3. Cash Allocation Reduces Long-Term Compounding

Periods where a volatility-controlled index allocates heavily to cash reduce long-term compounding. While this lowers drawdowns, it also limits upside participation during extended bull markets.

Over long time horizons—particularly since the 1980s—equities have produced strong returns. Any strategy that periodically shifts to cash will generally compound more slowly than a fully invested equity portfolio.

Typical Return Comparisons

Although performance varies by index design, general research findings show:

Many volatility-controlled indexes have historically produced returns roughly 1–4 percentage points lower per year than the S&P 500 over long measurement periods.

Why They Are Used in Insurance Products

Despite lower historical returns, volatility-controlled indexes are widely used in insurance-linked products such as IUL for several practical reasons:

1. More stable option pricing

Insurance carriers hedge crediting strategies using options. Stable volatility helps control option costs.

2. Higher illustrated participation rates

Because option prices are lower, carriers can offer higher participation rates or uncapped crediting structures.

3. Reduced cap volatility

Traditional indexes like the S&P 500 can lead to fluctuating caps when option prices change.

Key Takeaway

Volatility-controlled indexes generally provide smoother return patterns and lower drawdowns, but this stability typically comes at the cost of lower long-term returns compared to the standard S&P 500. The structural mechanisms that limit downside risk—reduced equity exposure and cash allocations during volatile periods—also reduce the ability to fully participate in strong equity market recoveries.

For this reason, volatility-controlled indexes are often best understood not as equity substitutes, but as risk-managed strategies designed to trade some upside potential for lower volatility and more predictable option pricing.

Bill: It seems that volatility-controlled indexes have dramatically lagged the markets. Realistically, over time, how could or should volatility-controlled indexes compare to the S&P 500, for example?

Short answer: a volatility-controlled index (VCI) should not be expected to match the S&P 500 over full market cycles—and if it ever does for long stretches, something unusual is going on.

Below is the realistic framing most people miss.

What a volatility-controlled index is designed to do

A volatility-controlled index dynamically adjusts exposure to equities (and sometimes bonds or cash) to target a fixed volatility level (often ~5%–7%).

Mechanically:

• When volatility is low → equity exposure rises (sometimes above 100%)
• When volatility is high → equity exposure is reduced (sometimes dramatically)
• The goal is stable risk, not maximum return

This makes it fundamentally different from a fully invested equity index like the S&P 500, which tolerates whatever volatility the market delivers.

The unavoidable math: volatility targeting creates return drag

Volatility targeting introduces two structural headwinds:

1. Systematic de-risking after volatility spikes

VCIs typically:

• Cut equity exposure after volatility rises
• Re-enter after volatility falls

This means they often:

• Sell low
• Buy back higher

That timing drag is not a bug — it is intrinsic to the strategy.

2. Lower expected geometric return

Even if average arithmetic returns were equal (they aren’t), lower equity exposure during volatile periods mathematically reduces long-term compound growth.

Put simply:

You cannot permanently reduce volatility without giving up expected return.

That’s finance, not insurance marketing.

Realistic long-term expectations vs the S&P 500

Here’s what should be expected over full market cycles:

Expectation:

A VCI should lag the S&P 500 by 2–5 percentage points annually over long periods.

If it doesn’t, check:

• Backtest assumptions
• Leverage mechanics
• Index launch date bias
• Embedded caps or participation resets (especially in insurance products)

Why recent lag feels “dramatic”

The last decade has been especially hostile to VCIs:

1. Sharp, fast volatility spikes (2018, 2020, 2022)
2. V-shaped recoveries
3. Concentrated mega-cap rallies
4. High realized volatility even during rising markets

VCIs repeatedly:

• De-risked during spikes
• Missed violent rebounds
• Re-entered after much of the upside was gone

This isn’t mismanagement — it’s exactly how they are built.

Where VCIs can make sense

Volatility-controlled indexes can be appropriate when:

• The crediting mechanism requires stability (e.g., option-budgeted strategies)
• The objective is risk smoothing, not return maximization
• They are part of a larger allocation (not the sole growth engine)
• Used honestly — without implied equity-like expectations

They are risk-management tools, not equity substitutes.

The insurance-specific reality (important)

Inside products like IULs or indexed annuities, VCIs are often used because:

• Lower volatility → cheaper options
• Cheaper options → higher caps or participation rates
• Smoother crediting → better year-to-year optics

That can make policy illustrations look competitive — even when the underlying index is structurally return-limited.

Bottom line

A volatility-controlled index should never be expected to keep up with the S&P 500 over time.

The honest benchmark is not:

“Did it match the S&P 500?”

But rather:

“Did it deliver lower volatility, smaller drawdowns, and predictable behavior — at the cost of return?”

If you’d like, I can:

• Quantify expected return drag given a 5% vs 15% volatility target
• Compare VCIs to balanced portfolios instead of equities
• Deconstruct why some insurance illustrations overstate their realism

Bill: Yes to all three

Great — I’ll tackle all three cleanly, quantitatively, and without insurance-spin. I’ll keep this grounded in capital-markets math, not illustrations.

1️⃣ Quantifying the expected return drag from volatility targeting

Start with basic capital-markets math

Long-term geometric return (what investors actually experience) is roughly:

{Geometric Return} \approx \{Arithmetic Return} – tfrac{1}{2}sigma^2

But volatility-controlled indexes reduce returns in two ways:

A. Lower average equity exposure

A 5%–6% volatility target usually implies 30–60% effective equity exposure over time.

If the long-run arithmetic return of equities is ~9–10%:

This source information was added to provide context for the content generated by ChatGPT. It was not displayed in the original chat interface and has been included to help readers better understand the origin and sourcing of the information. Sources: Long-term expected return and volatility assumptions are derived from historical capital market data and volatility-targeting research. The S&P 500 has historically exhibited annualized volatility in the mid-teens with long-term nominal arithmetic returns near 9%–10%. Volatility-controlled indexes (“VCIs”) generally reduce equity exposure and allocate substantial portions to cash or low-volatility fixed income instruments in order to maintain a targeted volatility level, resulting in lower expected returns as volatility targets decline. Estimated equity exposure ranges for 7% and 5% volatility-targeted strategies are based on common volatility-scaling methodologies and historical relationships between portfolio volatility and equity allocation. See, for example, Robert J. Shiller, Irrational Exuberance (3d ed. 2015); Aswath Damodaran, Equity Risk Premiums (ERP): Determinants, Estimation and Implications (annual updates); and research regarding volatility targeting and risk-controlled portfolios by MSCI, S&P Dow Jones Indices, and Research Affiliates. Figures are approximate and illustrative and will vary based on the specific volatility-control methodology, rebalance frequency, financing assumptions, and prevailing market conditions.

B. Timing drag from volatility feedback loops

VCIs mechanically:

• De-risk after volatility rises
• Re-risk after volatility subsides

This produces a persistent trend-capture penalty during:

• V-shaped recoveries
• Fast rallies
• Concentrated bull markets

Empirically, this costs another 0.5%–1.5% annually.

🔢 Realistic long-term expectation

Compared to the S&P 500:

• 5% vol target: ~3–5% CAGR lag
• 7% vol target: ~2–4% CAGR lag

That’s not bad design — it’s unavoidable physics.

2️⃣ The correct benchmark: VCIs vs balanced portfolios (not equities)

Why comparing VCIs to equities is intellectually dishonest

A volatility-controlled index is not an equity strategy.

It’s closer to a rules-based balanced portfolio with volatility targeting.

The correct comparisons:

This source information was added to provide context for the content generated by ChatGPT. It was not displayed in the original chat interface and has been included to help readers better understand the origin and sourcing of the information. Supporting Sources:

• Historical U.S. equity and bond market return/volatility data: Aswath Damodaran Historical Returns Data (1928–2024)
• Example target-volatility index methodology and volatility-control mechanics: S&P 500 Daily Risk Control 5% Index Methodology Overview

Key insight

When benchmarked properly:

• VCIs often do fine
• Sometimes even outperform 60/40 portfolios on a volatility-adjusted basis

But:

They are return-inferior by design.

Which makes their use case risk control, not growth maximization.

3️⃣ Why insurance illustrations often overstate VCI realism

This is where things quietly go off the rails.

⚠️ Illustration distortion #1: 

Static option budgets

Illustrations often assume:

• Stable option pricing
• Stable volatility
• Stable index mechanics

Reality:

• Option costs rise during stress
• Volatility spikes persist
• Index exposure gets cut when returns matter most

⚠️ Distortion #2: 

Caps & participation decoupled from exposure

VCI crediting looks attractive because:

• Lower volatility → cheaper options
• Cheaper options → higher caps or par rates

But:

• High caps on low exposure ≠ equity-like growth
• A 100% participation rate on a 40% equity exposure is still 40%

⚠️ Distortion #3: 

Backfilled or cherry-picked history

Many VCIs:

• Launch after favorable regimes
• Use reconstructed data
• Do not include real-time rebalance friction

This exaggerates long-term expectations.

⚠️ Distortion #4: 

AG 49 optics

Under AG 49:

• Smooth crediting helps illustrated IRRs
• Volatility suppression looks “efficient”
• Timing drag is invisible

Illustrations reward optical smoothness, not economic truth.

🔑 Bottom-line synthesis

Over time, a volatility-controlled index should:

✔ Deliver smoother returns

✔ Reduce drawdowns

✔ Lower option costs

❌ Lag equities materially

❌ Underperform strong bull markets

❌ Be unsuitable as a primary growth engine

Honest framing

A volatility-controlled index is a risk-management chassis, not an equity engine.

When presented that way, it makes sense.

When presented as an S&P 500 surrogate, it is mathematically indefensible.

Bill: So, it sounds like VCIs are expected to lag the securities market and IUL policies are expected to lag VUL policies under a similar premise, thus we should not be surprised that VCIs in an IUL policy are doing exceptionally poorly. 

Yes — that conclusion is correct and stated cleanly it sounds almost obvious once the mechanics are lined up.

What you’re seeing is not an anomaly. It’s the stacking of two separate, expected return-dampening mechanisms.

The compounding stack of “expected underperformance”

You’re not dealing with one source of drag. You’re dealing with stacked constraints.

Layer 1 — VCIs vs securities markets

A volatility-controlled index is expected to lag a fully invested equity index like the S&P 500 by 2–5% annually over full cycles.

That’s not market timing error — that’s the price of volatility suppression.

Layer 2 — IUL vs VUL (structural, not implementation)

Even with perfect product construction:

So yes:

IUL is expected to lag VUL under similar equity assumptions.

That’s not a knock — it’s inherent to the chassis.

Layer 3 — Insurance option economics

Inside IULs:

• Options are bought annually
• Volatility spikes → option costs rise
• Rising costs → lower caps or lower participation
• Lower exposure → lower realized returns

VCIs help insurers manage this, but:

Lower volatility ≠ higher returns

It simply makes the option budget workable.

Layer 4 — Path dependency during recent regimes

The last several years featured:

• Sudden volatility shocks
• Fast rebounds
• Concentrated mega-cap leadership

This is the worst possible environment for:

• VCIs
• Annual-reset option strategies
• Capped upside products

Why “exceptionally poor” results are not evidence of failure

This is the key reframing.

What you’re observing is not:

• Poor index design
• Carrier incompetence
• A broken product

It’s:

Expected behavior under a hostile regime — amplified by layered constraints.

When you stack:

1. Volatility suppression
2. Option budget limits
3. Annual reset mechanics
4. Post-spike de-risking
5. Illustration bias

You should expect:

• Long stretches of disappointing relative performance
• Especially versus VULs and direct equities

The real issue isn’t performance — it’s expectation-setting

The problem is not that VCIs in IULs are lagging.

The problem is that they were often framed implicitly as:

• Equity-like
• S&P 500 adjacent
• “Same return, less risk”

That framing is mathematically false.

The honest hierarchy (over full cycles)

From highest expected return → lowest:

1. VUL (efficiently built, equity-heavy)
2. IUL (equity-linked, volatility-managed)
3. VCI-based IUL
4. Fixed / general account

If reality deviates materially from that ordering for long periods, something structural has changed.

But right now, the ordering is behaving exactly as theory predicts.

The stacked premise (made explicit)

Premise 1: VCIs vs the securities market

Volatility-controlled indexes are explicitly designed to reduce volatility, not maximize return.

As a result, over full cycles they are expected to lag a fully invested equity benchmark like the S&P 500 by multiple percentage points per year.

That lag is not accidental — it is the cost of volatility targeting.

Premise 2: IUL vs VUL

Independently of index choice:

• VUL = direct participation in securities markets (full volatility, full upside)
• IUL = option-based exposure with caps, budgets, and annual resets

Therefore:

IULs are expected to lag VULs under similar equity assumptions.

Again — not a flaw, just structure.

What happens when you combine them?

You get VCIs inside an IUL, which layers:

1. Volatility suppression at the index level
2. Option-budget constraints at the policy level
3. Annual reset mechanics
4. De-risking after volatility spikes
5. Missed participation in fast recoveries

So performance isn’t just reduced — it’s structurally compressed from both directions.

Why “exceptionally poor” results should not be surprising

The recent market environment has been especially hostile to this stack:

• Sudden volatility spikes
• Rapid V-shaped recoveries
• Narrow, momentum-driven equity leadership
• Persistently elevated realized volatility

That combination is:

• Bad for VCIs
• Bad for option-based crediting
• Worst of all for VCIs inside IULs

So yes:

VCIs in IULs doing exceptionally poorly relative to equities or VULs is the expected outcome — not an indictment of the product.

The real problem is not performance — it’s framing

The mistake historically wasn’t building VCIs into IULs.

The mistake was allowing them to be mentally benchmarked as:

• “S&P 500–like”
• “Equity returns with less risk”
• “Just smoother stocks”

They are none of those things.

They are risk-managed, option-compatible return streams, and when judged honestly, their recent behavior is exactly what theory predicts.

Bottom line (one sentence)

If VCIs are expected to lag securities markets, and IULs are expected to lag VULs, then VCIs inside IULs should lag both — and recent poor results are unsurprising once expectations are set correctly.

If you want, next we can:

• Quantify how much lag is “normal” vs concerning
• Translate this into trustee-safe language
• Build a one-page visual showing the stacked drag
• Compare VCI-IUL outcomes to a properly matched 60/40 or risk-parity benchmark

What should surprise people is how often these expectations were never clearly stated.

Bill: Model a VCI vs S&P 500 vs 60/40 using identical assumptions

Here’s a stylized, apples-to-apples model (not a backtest) that shows what “should” happen when you compare:

• S&P 500 (100% equity)
• 60/40 (stocks/bonds)
• VCI (7% vol target) implemented as scaled S&P exposure + cash to hit the vol target

Identical assumptions (same capital-market inputs)

All numbers are annual.

• Equities (S&P-like): return 10.0%, volatility 16%
• Bonds (Agg-like): return 4.5%, volatility 6%
• Stock/bond correlation: −0.10
• Cash: return 3.0%
• 60/40 weights: 60% equity / 40% bonds
• VCI rule: set equity exposure m =7% /16% = 0.4375 each year (balance goes to cash), so index vol ≈ 7%

This VCI structure matches the economic intent of most volatility-control indexes: dial equity exposure up/down to a vol target, with the rest effectively in cash-like assets.

Results (same assumptions, different structures)

Expected arithmetic return (simple average)

• S&P: 10.00%
• 60/40: 7.80%
• VCI (7%): 0.4375 times10% + 0.5625 times 3% = 6.06%

Expected volatility

• S&P: 16.0%
• 60/40: ~9.66%
• VCI (7%): 7.0% (by design)

A clean way to compare compounding (geometric return proxy)

Using the common approximation:

g approx u – ½ sigma^2

This source information was added to provide context for the content generated by ChatGPT. It was not displayed in the original chat interface and has been included to help readers better understand the origin and sourcing of the information. Sources and Methodology: Geometric return proxy figures are derived using the commonly accepted approximation: g \approx \mu – \frac{\sigma^2}{2} where g is the estimated geometric return, \mu is the arithmetic return, and \sigma is annualized volatility. See, for example, Investments by Zvi Bodie, Alex Kane, and Alan J. Marcus, along with research and commentary from Aswath Damodaran regarding volatility drag and compounding.

The S&P 500 assumptions (10% arithmetic return; ~16% volatility) are broadly consistent with long-term historical market data published by S&P Dow Jones Indices and datasets compiled by Robert Shiller. The 60/40 portfolio assumptions (~7.8% return; ~9%–10% volatility) are consistent with historical balanced portfolio studies from Vanguard and BlackRock. The 7% volatility-controlled index (“VCI”) assumptions reflect typical volatility-targeting methodologies that reduce equity exposure to maintain a target volatility level, as discussed in research from MSCI and S&P Dow Jones Indices.

Thirty-year growth figures are approximate compound-growth illustrations calculated as: (1+g)^{30}  All figures are approximate and illustrative only.

Interpretation: under identical assumptions, a 7% vol-targeted equity strategy is expected to compound materially below a 60/40, and far below full equity—because it spends a lot of time not fully exposed to equity risk premia.

“Bad year” intuition (simple normal approximation)

Probability of a negative year (rough, assuming normal returns):

• S&P: ~26.6%
• 60/40: ~21.0%
• VCI (7%): ~19.3%

5th percentile (“1-in-20”) annual return estimate:

• S&P: ~−16.3%
• 60/40: ~−8.1%
• VCI (7%): ~−5.5%

So, the VCI is doing what it’s supposed to do on the risk side—but you pay for that in expected compounded return.

This source information was added to provide context for the content generated by ChatGPT. It was not displayed in the original chat interface and has been included to help readers better understand the origin and sourcing of the information. Negative-year probabilities and 5th percentile return estimates are derived using a normal-distribution approximation based on expected arithmetic returns and annualized volatility assumptions. Probability of loss calculated as P(R<0)=\Phi(-\mu/\sigma). Fifth percentile return estimated as \mu-1.645\sigma. Assumptions for S&P 500, 60/40 portfolio, and volatility-controlled index (“VCI”) are based on long-term historical U.S. capital market data (1928–2024) from Aswath Damodaran, NYU Stern School of Business, and standard target-volatility index construction methodologies that dynamically reduce equity exposure to maintain a specified volatility target.

Two important caveats (especially relevant to IUL context)

1. This model is generous to VCIs because it assumes clean scaling and no extra frictions. Real VCIs often face additional drag from:

• realized-vol “whipsaw” (de-risk after spikes, re-risk later)
• rebalance rules and constraints
• fees embedded in index methodology

2. An IUL crediting strategy is another layer on top of the index return stream (annual options, caps/pars/spreads, resets). So, if the VCI stream is already structurally lower, the IUL wrapper can compress it further.

Bill: Do it for 5%

Perfect — here’s the same apples-to-apples model, but now with a 5% volatility target, which is much closer to what many insurance-friendly VCIs actually run.

I’ll keep every other assumption identical so the only thing changing is the volatility target.

Identical capital-market assumptions (unchanged)

• Equities (S&P-like): Return 10.0%, Volatility 16%
• Bonds (Agg-like): Return 4.5%, Volatility 6%
• Stock/Bond Correlation: −0.10
• Cash: 3.0%
• 60/40: 60% equity / 40% bonds
• VCI construction:

Scale equity exposure to hit target vol, remainder in cash

Reference equity benchmark: S&P 500

Step 1: What a 5% volatility target means mechanically

Targeting 5% volatility using equity with 16% vol implies:

Equity exposure m = 5% / 16% = 31.25%

So, on average:

• 31% equities
• 69% cash-like exposure

That alone should set expectations.

Step 2: Expected arithmetic return (μ)

This source information was added to provide context for the content generated by ChatGPT. It was not displayed in the original chat interface and has been included to help readers better understand the origin and sourcing of the information.  Arithmetic return assumptions derived from long-term U.S. historical market returns (1928–2024) published by Aswath Damodaran, NYU Stern School of Business. VCI expected return assumes proportional volatility scaling from a 16% S&P 500 volatility baseline to a 5% target-volatility structure, with residual allocation modeled as cash equivalents earning approximately 3% annually.

Step 3: Expected volatility (σ)

This source information was added to provide context for the content generated by ChatGPT. It was not displayed in the original chat interface and has been included to help readers better understand the origin and sourcing of the information.  Volatility assumptions are based on long-term historical U.S. market data and standard target-volatility index methodology. S&P 500 volatility is assumed at approximately 16% annually; a traditional 60/40 stock-bond portfolio at approximately 9–10%; and a 5% volatility-controlled index at approximately 5% annualized volatility by design through dynamic allocation adjustments between equity exposure and lower-volatility collateral assets.

Step 4: Compounding comparison (geometric return proxy)

Using the standard approximation:

g approx u – ½ sigma^2

This is the key result

A 5% VCI compounds at roughly half the rate of equities and ~60% of a 60/40, before any insurance wrapper.

That gap is not a bug — it’s the price of forcing volatility down to 5%.

Step 5: Downside behavior (intuition check)

Approximate probability of a negative year:

• S&P 500: ~26.6%
• 60/40: ~21.0%
• VCI (5%): ~15.6%

5th-percentile (“1-in-20”) annual outcome:

• S&P 500: ~−16%
• 60/40: ~−8%
• VCI (5%): ~−3%

So the VCI does deliver what it promises:

• Very small drawdowns
• Very smooth returns

But that smoothness is bought with massive long-term opportunity cost.

Why this matters enormously for IULs

Now layer this onto an IUL chassis:

1. Start with a ~4.5% expected geometric return from the VCI
2. Apply annual option pricing
3. Add caps / participation / spreads
4. Reset annually (no recovery carryover)

It is therefore completely unsurprising to see:

• Long runs of 1–4% credited returns
• Policy IRRs that trail even conservative balanced portfolios
• Wide disappointment vs VUL or direct equity exposure

In fact:

If a 5% VCI inside an IUL didn’t lag badly, that would be the surprising outcome.

One-sentence takeaway (slide-worthy)

A 5% volatility-controlled index is economically closer to a conservative allocation than an equity strategy, and when wrapped in an IUL, the resulting return profile should be expected to lag equities, VULs, and even balanced portfolios by a wide margin.

Bill Boersma is a CLU, AEP and LIC and the founder and owner of OC Consulting Group. More information can be found at www.OC-LIC.com, www.BillBoersmaOnLifeInsurance.info or email at bill@oc-lic.com or call at 616-456-1000.

Securities offered through Valmark Securities, Inc. Member FINRA, SIPC. Investment Advisory Services offered through Valmark Advisers, Inc. a SEC Registered Investment Advisor.

OC Consulting Group and its affiliates are separate entities from Valmark Securities, Inc. and Valmark Advisers, Inc.

Indices are unmanaged and do not incur fees, one cannot directly invest in an index. Past performance does not guarantee future results. Guarantees are based on the claims-paying ability of the issuing company.