Why Fear Is Mispriced — and How to Profit From It
A simple guide to harvesting returns from market uncertainty
For the vast majority of investors, market volatility represents one of the most psychologically challenging aspects of managing a portfolio. It is the force behind sudden, unsettling swings in asset prices. The kind that trigger anxious portfolio check-ins, impulsive decision-making and, for many, genuinely restless nights.
Rather than viewing it as a threat to be endured, volatility can be treated as something closer to a natural resource: something that can be measured, priced and harvested. To understand why this is possible, and how it works in practice, we need to examine the foundational concept that makes it all viable: the Volatility Risk Premium (VRP).
Understanding the Volatility Risk Premium
At its most fundamental level, the Volatility Risk Premium can be thought of as a systematically recurring “fear tax” that risk-averse market participants pay in exchange for a sense of financial security. To appreciate why this premium exists, it helps to understand the two distinct measurements of volatility that sit at the heart of the concept.
The first is implied volatility: the market’s forward-looking expectation of how much an asset’s price will fluctuate over a given period. This expectation is derived mathematically from the prices of options contracts.
The second is realized volatility: the actual, historical measure of how much an asset’s price did fluctuate over that same period, calculated after the fact using observed price data. This is the ground truth, the storm as it actually materialized, rather than the storm that was feared.
The Volatility Risk Premium is the gap between these two figures. Decades of empirical research across global equity, commodity and fixed income markets have consistently demonstrated that implied volatility tends to exceed realized volatility over time. In other words, the market’s collective expectation of future turbulence is, on average, systematically and reliably higher than the turbulence that actually unfolds.
The existence of the VRP is not accidental, nor is it a market inefficiency that is likely to be arbitraged away anytime soon. Rather, it is deeply rooted in both human psychology (loss and risk aversion) and structural market dynamics (Institutional Mandates and Fiduciary Obligations), which together create a persistent source of demand for volatility protection.
It is essential to understand that the Volatility Risk Premium is not a “free lunch”. The reason the premium exists at all (the reason investors are willing to pay it) is precisely because the risk being transferred is real and meaningful. Volatility sellers are exposed to what you can call: “picking up nickels in front of a steamroller”. Most of the time, the strategy generates steady, modest returns. But during periods of genuine market stress, realized volatility can spike far above even elevated levels of implied volatility, leading to sharp and painful losses. This is represented in the following figure.
This asymmetric return profile, frequent small gains punctuated by occasional severe drawdowns, is the fundamental reason why the premium persists. Many market participants simply cannot tolerate the episodic losses, either psychologically or institutionally and thus prefer to remain on the buying side of the trade.
Testing the Theory
While the concept of the Volatility Risk Premium (VRP) offers a powerful theoretical edge, successfully capturing it requires more than simply “being short” volatility at all times. A permanent short position in volatility is a dangerous proposition, as it exposes the trader to “tail risk”—those rare but catastrophic market spikes that can wipe out years of steady gains in a matter of days.
To transform this edge into a sustainable investment strategy, the goal must be dynamic participation. This means developing a systematic timing mechanism designed to harvest the premium when it is abundant and, crucially, to step aside when the premium disappears or turns negative.
For retail traders, the most accessible way to implement these sophisticated strategies is through Exchange-Traded Products (ETPs) that track volatility indices. In this analysis, we focus on two primary instruments:
VIXY: Provides 1x long exposure to the S&P 500 VIX Short-Term Futures Index
UVXY: Provides 1.5x leveraged exposure to the same index.
By shorting these instruments we can capture the VRP. The methodology used here is heavily influenced by the work of Zarattini, Mele, and Aziz, as well as Robot Wealth.
The cornerstone of a dynamic strategy is a timing mechanism. The logic is defensive: we want to avoid shorting volatility during periods where the market is actually underestimating future turbulence. If realized volatility is likely to be higher than implied volatility, the VRP is negative, so the “insurance seller” is positioned for a loss.
To estimate this in real-time, we use several approaches.
1. The Backward-Looking Signal: Historical Volatility Clustering
Volatility is not random; it exhibits autocorrelation and volatility clustering. Simply put, high volatility today is likely to be followed by high volatility tomorrow. Because of this, even a simple historical measure can serve as a potent forecast for the immediate future.
To capture this, we use the annualized standard deviation of the last 10 daily returns of the SPY (S&P 500 ETF) as a proxy for realized volatility. By subtracting this from the current VIX (implied volatility), we arrive at the estimated VRP (eVRP).
If VIX > 10-day Realized Volatility: The market is paying a premium; the environment is favorable for harvesting.
If VIX < 10-day Realized Volatility: The market is underpricing risk; the strategy should move to the sidelines.
2. The Forward-Looking Signal: VIX Term Structure (IVTS)
To supplement the historical data, we look to the Implied Volatility Term Structure (IVTS). This involves comparing the spot VIX (near-term expectations) to the VIX3M (expectations three months out). I use the median of the last 10 days, like in my post about the HeroRATs strategy.
When the IVTS is below 1.0, this signals that investors expect the near term to be calmer than the medium term. This “normal” upward-sloping curve is the ideal environment for volatility harvesting, as it suggests an absence of immediate panic and a high probability that the VRP will be realized. Conversely, when the ratio exceeds 1.0, it indicates immediate market stress and the strategy retreats to safety.
Capital Allocation and Position Sizing
Even with high-probability signals, volatility remains an aggressive asset class. Therefore, the strategy employs a strict risk-management overlay to ensure longevity.
The 20% Ceiling
Total exposure to these volatility strategies is capped at a fixed allocation of 20% of the total portfolio. The remaining 80% is held in cash.
Adaptive Sizing: The VIX/100 and VIX/75 Rules
To further refine the strategy, we use dynamic position sizing as suggested by Zarattini, Mele and Aziz. Rather than always deploying the full 20%, we scale the position size based on the current level of the VIX:
The VIX/100 Rule: If the VIX is at 20, the strategy allocates 20% of its designated capital.
The VIX/75 Rule: A more aggressive variant that allows for higher participation.
These rules are based on the observation that when the VIX is high, the potential VRP is often larger, but the risk of further spikes remains. By scaling into positions relative to the level of VIX, the strategy seeks to optimize the balance between risk and reward.
This creates the following 8 variations of the strategy (DYNVRP):
By combining these variables, leverage (VIXY vs. UVXY), sizing rules (VIX/100 vs. VIX/75) and the eVRP/IVTS filters. we arrive at eight distinct strategy variations. The goal is to see if a more sophisticated variation (more parameters) is superior to simple ones and if it’s better to use an unlevered or leveraged ETF.
The Results
The sample period is from 2019 to 2025 because UVXY had significant changes after “Volmageddon” in 2018.
The most immediate observation is that the UVXY variations consistently outperform their VIXY counterparts across CAGR and Sharpe. Interestingly, the addition of the second parameter (IVTS) actually led to a decrease in (risk-adjusted) performance compared to the simple eVRP model. While adding the IVTS filter can initially dampen returns, combining these filters with dynamic position sizing (especially VIX/75) more than compensates for it. Dynamic sizing is especially effective for UVXY.
While the backtest results presented above are compelling, it is crucial to apply a “reality filter” to these figures. The figures in the table are gross returns, meaning they do not yet account for transaction costs, slippage or—most importantly—the costs associated with short selling. Shorting volatility ETPs like VIXY or UVXY is not free, it involves borrow fees that can fluctuate wildly. During periods of high demand, these instruments may become hard to borrow, leading to fees that can significantly erode the volatility risk premium you are trying to harvest. Because these costs vary based on your broker, account size and the specific market environment.
Because of this variability, it is difficult to provide a universal answer to the question of whether a strategy will remain profitable after costs are considered. What works well for one trader may look very different for another. For these reasons, it is essential that each trader runs their own cost assumptions and evaluates how the strategy performs under conditions that realistically reflect their personal setup. This post is focused on “Hat 1 - Edge Research” (see here for explanation), so there will be only an illustration of these strategies after costs.
I have chosen to narrow the focus specifically to DYNVRP 1, 5 and 6. While other variations in the table showed higher raw returns, selecting these two variations is a strategic decision based on finding the balance between theoretical performance and practical execution.
The data consistently demonstrates that UVXY is a more efficient instrument for this specific harvesting strategy than the unleveraged VIXY. You may notice that DYNVRP 8 actually produced the highest total performance. However, its success relies on dynamic position sizing, which presents a significant operational challenge in the real world. Dynamic sizing requires the trader to constantly adjust the position as the VIX moves. This leads to a high frequency of transactions which generates costs. With further work this could be changed but is out of the scope of this post.
When we factor in realistic borrow costs, the strategy’s Sharpe Ratio drops by more than 33%. This is a significant performance haircut. Despite this substantial drag on performance, both variations remain attractive investment candidates.
After evaluating the various iterations of the DYNVRP strategy, I have decided to focus on the eVRP variations. The data suggests that eVRP is the most powerful driver of the strategy’s success, while being the most simplest.
Unfortunately for (non professional) EU citizens like me, you cannot trade UVXY or VIXY directly. You have to use CFDs. But even shorting UVXY via CFD is hard to impossible because it’s shares can be unborrowable. You are left with VIXY and DYNVRP 1 which is easy to borrow but definitely inferior to an implementation with UVXY but still a good and simple strategy.
Further Work
The analysis indicates that implementing a dynamic position-sizing approach substantially enhances the strategy’s effectiveness, at least before transaction costs are taken into account. Moreover, if the model can be refined to reduce trading frequency, without sacrificing signal quality, it’s a very important parameter to add to this or other strategies.
Key Takeaways
The Volatility Risk Premium Is a Structural, Persistent Phenomenon: The gap between implied volatility and realized volatility is not a random anomaly or a temporary market inefficiency. It is deeply rooted in human psychology and institutional behavior. This creates a reliable premium that disciplined investors can systematically capture.
Harvesting the VRP Is Not a “Free Lunch”: While the premium is persistent, it comes with a very real and asymmetric risk profile. Volatility sellers enjoy frequent, modest gains during calm market periods, but face the threat of sudden, severe losses when markets experience genuine stress and realized volatility spikes dramatically above implied volatility. This “picking up nickels in front of a steamroller” dynamic is precisely why the premium exists in the first place, as many investors simply cannot stomach those episodic but painful drawdowns.
Transaction Costs and Borrow Fees are Real-World Headwind: The gross backtest results, while highly compelling, present an overly optimistic picture. When realistic costs are factored in, particularly volatile borrow fees associated with short-selling volatility ETPs, the strategy’s Sharpe Ratio drops significantly. This underscores a critical practical lesson: a theoretically sound strategy can be significantly eroded by implementation costs, and any serious practitioner must account for these expenses before committing capital.
Simplicity often Outperforms Complexity in Strategy Design: One of the most counterintuitive findings of this analysis is that adding more parameters does not necessarily improve performance.
Conclusion
The pursuit of harvesting the Volatility Risk Premium is, at its core, an exercise in disciplined patience. You have to accept that the strategy will, from time to time, deliver uncomfortable losses.
What this analysis has demonstrated is that a dynamic approach to volatility harvesting, one that actively filters for favorable conditions rather than blindly selling volatility at all times, can transform a dangerously asymmetric trade into a genuinely attractive investment strategy. The eVRP signal, proves to be a remarkably powerful gatekeeper. It keeps you in the game when the odds are in your favor and, crucially, moves you to the sidelines before the steamroller arrives.
The findings also serve as a timely reminder that sophistication is not synonymous with superiority. Adding layers of complexity does not automatically translate into better risk-adjusted returns, particularly once the friction of real-world implementation is taken into account. In strategy design, as in so many areas of trading, simplicity frequently wins in the long run.
Perhaps most importantly, this is not a strategy for everyone. The episodic drawdowns are real, the borrow costs are meaningful, and the discipline required to systematically follow signals rather than emotions can be demanding. But for those who can embrace its inherent risks with clear eyes and a structured framework, the Volatility Risk Premium offers something increasingly rare in modern markets: a structural and persistent opportunity.
The volatility is out there. The question is whether you choose to fear it, or harvest it.
In the next weeks I will publish more posts on other volatility related strategies.
Disclaimer
The above article constitutes my or the authors’ personal views and is for entertainment purposes only. It is not to be construed as financial advice in any shape or form. Please do your own research and seek your own advice from a qualified financial advisor. I / The authors may from time to time hold positions in the aforementioned securities consistent with the views and opinions expressed in this article. The information provided in this article is not making promises, or guarantees regarding the accuracy of information supplied, nor that you guarantee for the completeness of the information here. The information in this article is opinion-based and that these opinions do not reflect the ideas, ideologies, or points of view of any organization the authors may be potentially affiliated with. The authors reserve the right to change the content of this blog or the above article. The performance represented is historical and that past performance is not a reliable indicator of future results and investors may not recover the full amount invested.
Question. You said that vol has autocorrelation and volatility clustering. Aren't those two points essentially the same thing?