Hedging Tail Threat with Strong VIXY Fashions – QuantPedia

Excessive market occasions, as soon as perceived as statistical outliers, have develop into a central concern for buyers. The persistence of sharp drawdowns and volatility spikes demonstrates that the price of ignoring tail dangers just isn’t tolerable for long-term portfolio resilience. Whereas diversification can mitigate strange fluctuations, it usually fails when markets transfer in unison beneath stress. This makes express safety towards extreme draw back occasions not simply fascinating however vital. Tail hedging addresses this want by offering a structured protection towards essentially the most damaging situations, guaranteeing that portfolios stay strong when conventional danger administration instruments fall quick. Utilizing VIXY ETF, we’ll current and check a spread of hedging methods designed to guard portfolios beneath stress. By making use of strong testing frameworks, we intention to guage how totally different implementations of VIXY ETF-based tail hedges carry out throughout a wide range of market environments, highlighting each their strengths and inherent trade-offs.

VIX, VXV & VIXY

In our evaluation, we’ll use the ProShares VIX Brief-Time period Futures ETF (VIXY) as the first hedging instrument, mixed with the SPDR S&P 500 ETF (SPY) because the core fairness publicity. This pairing permits us to discover how volatility-linked property can mitigate drawdowns in a standard fairness portfolio. To information the allocation of VIXY throughout the portfolio, we’ll incorporate alerts derived from the VIX and VXV indices. Whereas these indices can’t be traded instantly, their informational worth makes them helpful modeling variables for figuring out when and to what extent volatility publicity must be utilized. Importantly, solely SPY and VIXY will type the investable portfolio, with VIX and VXV serving strictly as inputs to allocation fashions somewhat than direct holdings.

The CBOE Volatility Index (VIX) is essentially the most well known measure of anticipated fairness market volatility. Derived from S&P 500 choices, the VIX displays the market’s consensus on near-term uncertainty and is sometimes called the “worry gauge.” Sharp will increase within the VIX usually coincide with market stress, making it a pure reference level for tail danger hedging. Nonetheless, as a non-tradable index, buyers can not instantly purchase or promote the VIX itself, which limits its use to signaling somewhat than execution.

The CBOE 3-Month Volatility Index (VXV) extends the idea of the VIX by measuring implied volatility over a three-month horizon. This longer tenor makes VXV much less delicate to short-lived spikes however extra reflective of sustained market uncertainty. In consequence, the connection between VIX and VXV is commonly used as a gauge of market stress regimes, with a rising VIX relative to VXV signaling elevated short-term worry. For tail hedging, VXV gives beneficial context by anchoring short-term volatility inside a broader temporal framework.

The ProShares VIX Brief-Time period Futures ETF (VIXY) presents buyers a liquid, tradable automobile to realize publicity to VIX futures. By holding a rolling place in front-month and second-month futures, VIXY seeks to trace short-term modifications in anticipated volatility. Its responsiveness to market shocks makes it a sensible instrument for implementing tail hedging methods. Nonetheless, buyers should account for structural challenges reminiscent of roll prices in contango environments, which might erode worth over time. Regardless of these limitations, VIXY stays one of the crucial accessible instruments for translating volatility expectations into actionable hedges. This asset was launched in 2011, so to have an extended information historical past, we reconstructed it earlier historical past since 2004 till 2011 utilizing VIX futures.

Benchmark technique

A easy but highly effective sign for timing VIXY publicity arises from the connection between the short-term VIX and the medium-term VXV. Beneath regular market circumstances, the VIX, which displays 30-day implied volatility, tends to be decrease than VXV, the 90-day measure. This displays the market’s expectation that rapid uncertainty is normally smaller than medium-term uncertainty, a typical characteristic of secure markets.

When market stress emerges, the same old relationship can invert: the VIX rises above VXV, signaling that short-term worry exceeds medium-term expectations. For VIXY, which tracks short-term VIX futures, this inversion is especially significant. It identifies intervals by which the ETF is prone to reply sharply to spikes in volatility, making it an environment friendly hedge exactly when fairness markets face the best danger. Through the use of this sign, buyers can keep away from the prices of holding VIXY repeatedly and as a substitute activate publicity solely when it’s most probably to be efficient.

Following this technique, we’ll allocate as much as 20% of the portfolio dynamically to VIXY, with the rest, 80%, held in SPY. The VIXY allocation is conditional: it’s both absolutely invested in keeping with the sign or held in money when the sign doesn’t set off. Importantly, the allocation updates happen with a one-day lag. It is because we depend on VIX and VXV values from the earlier market shut, and trades are executed on the shut of the next day. This timing ensures that the technique stays implementable in apply whereas precisely reflecting the knowledge supplied by the volatility alerts.

Desk 1: Efficiency metrics of 100% SPY and 20% VIX-VXV sign for VIXY and 80% SPY technique.

PORTFOLIO
CAR p.a.
VOL p.a.
SHARPE

100% SPY
10.52%
18.71%
0.56

80% SPY, 20% VXV-VIX sign for VIXY
6.25%
16.29%
0.38

Whereas the VIXY-hedged portfolio demonstrates a discount in absolute danger, its decrease return and Sharpe ratio relative to the SPY benchmark recommend that, no less than over the interval thought of, the hedge comes at a value that isn’t absolutely offset by improved risk-adjusted efficiency. This highlights a key problem in tail hedging: whereas safety towards excessive occasions is effective, it may well scale back general effectivity if the sign is just too conservative or market circumstances don’t ceaselessly set off vital drawdowns. As such, the benchmark itself doesn’t seem sufficiently efficient in capturing the potential advantages of the hedge, underscoring the necessity for cautious technique design and strong testing. Due to this fact, there’s a clear have to discover improved approaches that steadiness draw back safety with general portfolio effectivity.

Earlier analysis

We have now already coated the query of selective hedging utilizing the triple leveraged ETFs by which we had been impressed by Carlo Zarattini, Antonio Mele, and Andrew Aziz beforehand printed article addressing this very subject, which gives a superb start line for our evaluation. Of their work, they introduced a number of tail hedging methods, masking a variety of approaches and implementation types. For the needs of this text, we’ll slender our focus particularly to long-only methods, inspecting how they carry out beneath totally different market circumstances. Past merely implementing these methods, our aim is to carefully check their robustness, assessing not solely their effectiveness in decreasing draw back danger but additionally their consistency and sensible viability over time. The article introduces two methods that type the premise of our dialogue.

All methods introduced contain an idea of anticipated volatility danger premium. The anticipated volatility premium is calculated because the distinction between the implied volatility of VIX or VXV at time t and the realized volatility of the underlying asset, on this case the S&P 500, over the corresponding horizon T. In different phrases, it measures how a lot the market’s expectation of future volatility exceeds the precise noticed variability of the index.

The second indicator employed in each methods is the smoothing of VIX or VXV values. This strategy includes evaluating the present stage of the index to a transferring common calculated over the previous a number of days (in case of this text, it’s 90 calendar days). By doing so, the technique captures short-term deviations from current developments, serving to to determine intervals when volatility is unusually excessive or low relative to its current historical past.

This strategy integrates comparatively properly with the benchmark, because it equally allocates 20% to a hedging technique.

This strategy represents what is often known as “sizing,” which includes allocating a bigger portion of capital to the hedging asset as market uncertainty will increase. In apply, given the fastened allocation proportions mentioned earlier, which means that during times of exceptionally excessive uncertainty, it could be essential to make use of leverage on a short-term foundation to take care of the specified publicity. The prices related to such leverage are ignored on this evaluation, as these positions are usually very short-lived and shortly adjusted as soon as market circumstances normalize.

Let’s get again to fundamentals

Within the earlier part, we launched two methods that would be the focus of our evaluation. To start, we’ll begin with the less complicated one: the model with out sizing. Right here, the hedge allocation is capped at 20% of the portfolio, however it’s activated solely when the circumstances of the sign are met, particularly when the anticipated volatility danger premium (eVRP) is lower than or equal to zero and the VIX exceeds its three-month transferring common (VIX3M). Exterior of those circumstances, the hedge stays in money. This framework represents a fixed-weight implementation of the technique and gives a helpful baseline for evaluating its effectiveness earlier than transferring on to the extra complicated model with dynamic sizing.

Allow us to now study whether or not this technique is really strong or whether or not its efficiency could merely be the results of probability. To deal with this query, we will fluctuate the parameters that outline the alerts. Particularly, we’ll experiment with the window size used to calculate the usual deviation of the S&P 500, in addition to the smoothing window utilized to VIX. By testing the technique throughout totally different parameter settings, we will consider the consistency of its outcomes and decide whether or not its effectiveness holds up past a single calibration.

Desk 2: Sensitivity of annualized yields to eVRP and transferring common window size, MA home windows in buying and selling days are 2/3 of calendar days, execution shifted by 1 day, calculated between 01.04.2004 and 31.07.2025.

eVRP
10 D
20 D
30 D
40 D
60 D
80 D
90 D
100 D
120 D

5
0.70%
1.68%
1.81%
1.23%
0.90%
1.23%
1.02%
0.94%
0.70%

10
0.70%
1.68%
1.81%
1.23%
0.90%
1.23%
1.02%
0.94%
0.70%

20
0.11%
0.88%
1.09%
0.57%
0.56%
1.10%
0.93%
0.91%
0.75%

30
-0.94%
0.09%
0.22%
-0.47%
-0.32%
0.37%
0.36%
0.28%
-0.05%

40
-0.52%
0.16%
0.18%
-0.12%
0.18%
0.93%
0.97%
0.93%
0.66%

60
-1.18%
-0.47%
-0.59%
-0.65%
-0.57%
-0.10%
-0.11%
-0.30%
-0.61%

Desk 3: Sensitivity of annualized volatility to eVRP and transferring common window size, MA home windows in buying and selling days are 2/3 of calendar days, execution shifted by 1 day, calculated between 01.04.2004 and 31.07.2025.

eVRP
10 D
20 D
30 D
40 D
60 D
80 D
90 D
100 D
120 D

5
3.49%
4.30%
4.56%
4.81%
5.02%
5.16%
5.15%
5.18%
5.19%

10
3.49%
4.30%
4.56%
4.81%
5.02%
5.16%
5.15%
5.18%
5.19%

20
2.38%
2.71%
2.88%
3.27%
3.78%
4.00%
4.08%
4.18%
4.27%

30
2.03%
2.09%
2.12%
2.30%
2.88%
3.36%
3.51%
3.59%
3.66%

40
2.07%
1.59%
1.61%
1.54%
2.17%
2.80%
3.00%
3.10%
3.18%

60
2.77%
1.85%
1.61%
1.52%
1.30%
1.51%
1.79%
1.92%
2.19%

Desk 4: Sensitivity of Sharpe to eVRP and transferring common window size, MA home windows in buying and selling days are 2/3 of calendar days, execution shifted by 1 day, calculated between 01.04.2004 and 31.07.2025.

eVRP
10 D
20 D
30 D
40 D
60 D
80 D
90 D
100 D
120 D

5
0.20
0.39
0.40
0.26
0.18
0.24
0.20
0.18
0.14

10
0.20
0.39
0.40
0.26
0.18
0.24
0.20
0.18
0.14

20
0.05
0.32
0.38
0.17
0.15
0.28
0.23
0.22
0.18

30
-0.46
0.04
0.10
-0.20
-0.11
0.11
0.10
0.08
-0.02

40
-0.25
0.10
0.11
-0.08
0.08
0.33
0.32
0.30
0.21

60
-0.43
-0.25
-0.37
-0.43
-0.44
-0.06
-0.06
-0.15
-0.28

The leads to the earlier desk recommend that the 10-day transferring common window gives a constant and strong estimate. Sharpe ratios, return and volatility values stay secure throughout totally different testing horizons, whereas avoiding the pronounced damaging outcomes which will emerge for longer home windows. Selecting a 10-day window subsequently represents a balanced compromise: it’s quick sufficient to seize related short-term market dynamics, however lengthy sufficient to filter out extra noise that might in any other case dominate at even shorter intervals. Within the following sections, we’ll subsequently focus solely on the 5 and 10-day window and study its conduct beneath various market circumstances and with respect to complementary efficiency measures.

You will need to notice that all through our evaluation, the technique is applied with a one-day execution lag. In apply, each the eVRP sign and the transferring common are evaluated with a delay of 1 buying and selling day. This displays the operational constraint that it isn’t possible to open or shut positions instantaneously on the post-market stage as soon as the sign has been generated.

By making use of this lag constantly, we make sure that the outcomes replicate a sensible buying and selling framework somewhat than an idealized setup that might be troublesome to duplicate in apply. Whereas this adjustment barely reduces the theoretical effectivity of the alerts, it gives a extra strong and implementable measure of technique efficiency.

For completeness, we additionally evaluated all methods with out making use of the one-day execution lag. The outcomes point out that the strongest alerts had been captured equally properly, no matter whether or not the lag was current. The primary variations had been restricted to marginal variations in general efficiency and danger measures.

This discovering reinforces the robustness of the alerts themselves: the selection between implementing or omitting the execution lag primarily impacts the sensible execution profile somewhat than the underlying informational content material. In different phrases, the lag introduces solely a modest trade-off between theoretical effectivity and lifelike implementability, with out altering the elemental conclusions in regards to the methods’ effectiveness.

Is our mannequin overfitted?

It’s value noting that our strategy is likely to be barely aggressive in what we think about a “sign.” We concentrate on very particular indicators, and there’s a danger of overfitting if we attempt to extract an excessive amount of from market information. For instance, makes an attempt to easy the VIX curve could transcend cheap evaluation and verge on over-optimization.

So, if we simply keep away from this situation and focus solely on eVRP < 0, we will get hold of a brand new benchmark.

Equally, we will ask whether or not eVRP may be thought of utilizing VXV as a foundation. Allow us to discover this chance and create a brand new benchmark based mostly on it.

Desk 5: Efficiency metrics of benchmark and modified article methods.

PORTFOLIO
CAR p.a.
VOL p.a.
SHARPE

20% VIX-VXV sign for VIXY
-2.13%
6.43%
–

20% VIX 5D eVRP sign for VIXY
1.79%
5.86%
0.32

20% VIX 10D eVRP sign for VIXY
1.70%
5.71%
0.30

20% VXV 5D eVRP sign for VIXY

-1.80%
5.46%
–

20% VXV 10D eVRP sign for VIXY
-2.12%
5.29%
–

We will see that selecting the sign based mostly on 5-day or 10-day eVRP works properly, however solely once we use VIX because the underlying measure—not VXV. On the similar time, it turns into clear that our benchmark performs somewhat poorly, which additional highlights the significance of rigorously defining the reference level. One other essential statement is that article methods carry out higher when it comes to returns, however when it comes to danger adjusted returns, modified methods have higher Sharpe ratio.

Sizing as the important thing to improved efficiency

Within the earlier part, our findings concerning the exclusion of smoothing within the VIX eVRP sign had been considerably inconclusive. We’ll nonetheless hold methods based mostly on simply the  VIX eVRP sign in consideration, as we didn’t mix them with SPY in 80/20 portfolio but. Nonetheless, the unique article we adopted proposed an extra refinement: adjusting place sizing in proportion to the extent of VIX.

In these methods, we hold the identical sign as earlier than, however the portfolio weight is not fastened. As an alternative, it’s decided by the present stage of the VIX. For instance, if the VIX is at 28, the allocation to VIXY within the portfolio at the moment can be 28%. Naturally, which means that when mixed with, say, an 80% allocation to SPY, the portfolio weight can exceed 100%, requiring the usage of leverage. On this evaluation, we ignore the prices of leverage, as these positions are meant to be short-term.

Desk 6: Efficiency metrics of modified article methods utilizing sizing.

PORTFOLIO
CAR p.a.
VOL p.a.
SHARPE

5D SD & 30D MA for VIX technique

6.55%
11.91%
0.55

5D SD & 90D MA for VIX technique
7.18%
12.85%
0.56

10D SD & 30D MA for VIX technique
6.55%
11.91%
0.30

10D SD & 90D MA for VIX technique

7.18%
12.85%
0.56

What we will observe is that the conduct beneath each window decisions, whether or not 5-day or 10-day, is actually an identical. As soon as once more, we will see that these methods are comparatively strong, so from this level onward we’ll work solely with the 10-day standard-deviation model.

The outcomes we obtained exhibit considerably improved traits in contrast not solely to the benchmark methods but additionally to the variants with out dynamic sizing and to people who excluded smoothing. In different phrases, incorporating VIX-based proportional sizing gives a transparent enhancement in efficiency metrics, demonstrating each higher risk-adjusted returns and extra constant conduct throughout totally different market circumstances. This means that adjusting place weights in keeping with VIX ranges captures significant tail-risk alerts that the less complicated approaches fail to take advantage of.

Time to combine

Thus far, now we have examined every technique in isolation, focusing solely on its standalone conduct. We have now not but tried to implement them inside an precise portfolio context, the place SPY is already current, nor have we explored combining a number of methods concurrently. That is essential as a result of, though these methods are based mostly on the identical underlying sign, they usually interpret it in barely alternative ways. In consequence, merely them individually could overstate their effectiveness, whereas combining them may result in diversification advantages or reveal overlapping exposures that scale back incremental worth. Investigating how these methods work together inside a portfolio setting is subsequently a vital step to evaluate their real-world applicability and robustness.

As a primary mixture, allow us to study a composite technique based mostly on the 30-day and 90-day transferring averages of the VIX, and evaluate whether or not it achieves higher traits than every of the person methods on their very own.

 

Desk 7: Efficiency metrics of modified article methods utilizing sizing and their composition.

PORTFOLIO
CAR p.a.
VOL p.a.
SHARPE

10D SD & 30D MA for VIX technique
6.55%
11.91%
0.30

10D SD & 90D MA for VIX technique

7.18%
12.85%
0.56

Composition of 30D and 90D technique
6.90%
12.14%
0.57

After we mix the 30-day and 90-day VIX-based methods right into a single composite strategy, we observe a marginal enchancment in risk-adjusted efficiency in comparison with the person methods. Whereas every of the unique methods performs moderately properly by itself, the composite barely enhances the Sharpe ratio, suggesting that mixing totally different horizons can modestly easy returns and mildly enhance effectivity with out drastically altering the general danger profile.

As the ultimate step of this evaluation, we have to tackle mixtures of those methods with SPY. By allocating 80% of the portfolio to SPY and the remaining portion to the methods described above, typically utilizing leverage, we get hold of the next outcomes.

* In benchmark technique, between 01.04.2004 and 03.12.2007, 20% of portfolio was in money.

Desk 8: Efficiency metrics of benchmark and modified article methods utilizing sizing and their composition and modified article methods not utilizing sizing or smoothing.

PORTFOLIO
CAR p.a.
VOL p.a.
SHARPE

100% SPY
10.52%
18.71%
0.56

80% SPY, 20% VXV-VIX sign technique (benchmark)
6.25%
16.29%
0.38

80% SPY + SIZING 30D MA & 10D SD (eVRP) technique
16.47%
15.76%
1.04

80% SPY + SIZING 90D MA & 10D SD (eVRP) technique
17.38%
15.44%
1.13

80% SPY + SIZING 30D/90D MA & 10D (eVRP) technique
16.96%
15.41%
1.10

80% SPY + 5D (eVRP) technique
11.00%
13.72%
0.80

80% SPY + 10D (eVRP) technique
10.91%
13.59%
0.8

80% SPY + SIZING 5D (eVRP) technique
18.39%
15.52%
1.19

80% SPY + SIZING 10D (eVRP) technique
18.17%
15.32%
1.19

When combining SPY with the methods derived above, we will clearly see that not all approaches add worth. The straightforward benchmark mixture with the VXV–VIX sign truly dilutes efficiency relative to holding SPY alone, each when it comes to absolute and risk-adjusted returns.

In contrast, methods that incorporate dynamic sizing ship way more engaging outcomes. Whether or not sizing relies on the 30-day or 90-day transferring common, or a composite of the 2, the enhancements are evident. These approaches concurrently enhance returns and scale back danger in comparison with SPY by itself, leading to a considerable increase in effectivity.

Even the less complicated 5-day and 10-day eVRP variants present some profit, although their influence is much less pronounced. As soon as dynamic sizing is added to those shorter home windows, nonetheless, the efficiency turns into significantly compelling, combining larger returns with improved stability.

Conclusion

Our evaluation reveals that tail-hedging methods based mostly on eVRP alerts can present significant enhancements when rigorously designed and applied. Whereas naive benchmarks or unsized variants usually fail to outperform a easy SPY allocation, introducing place sizing linked to VIX ranges constantly enhances each returns and risk-adjusted outcomes. Among the many variations thought of, dynamically sized methods, whether or not based mostly on quick or medium-term home windows, stand out as the simplest enhances to a core SPY portfolio.

An essential end result of this evaluation is that the introduction of sizing allowed us to determine a number of comparatively environment friendly methods. What additionally turns into clear is that the hedge prompts solely not often throughout the portfolio. But when it does, and when the circumstances are correctly outlined, the outcomes will be fairly compelling. This highlights the worth of getting a well-calibrated hedging mechanism in place: it doesn’t burden the portfolio throughout regular market circumstances, however it may well meaningfully enhance outcomes when stress occasions happen.

Authors:

David Belobrad, Junior Quant Analyst, Quantpedia

Radovan Vojtko, Head of Analysis, Quantpedia

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