Leveraged ETFs (corresponding to SPXL – (Direxion Every day S&P 500 Bull 3X Shares) supply amplified publicity to the S&P 500, promising excessive returns however exposing traders to volatility drag attributable to each day rebalancing. This impact can considerably erode efficiency over longer horizons, notably in periods of elevated market volatility. Impressed by current analysis, The Volatility Edge, A Twin Strategy For VIX ETNs Buying and selling, targeted on volatility-linked ETNs, we suggest a volatility filter that adjusts ETF publicity primarily based on the connection between short-term realized volatility and implied volatility. By decreasing publicity in high-volatility durations and sustaining it in calmer markets, this method goals to harness leverage successfully whereas mitigating essentially the most damaging drawdowns.
Introduction
Lately, exchange-traded merchandise have grow to be more and more subtle, providing traders publicity not solely to broad fairness indices but additionally to extra complicated methods that amplify and even invert each day returns. Amongst these devices, leveraged ETFs stand out for his or her promise of magnified positive factors but additionally for the hidden dangers embedded of their design.
A outstanding instance will be present in SPXL (Direxion Every day S&P 500 Bull 3X Shares) and SPXU (Direxion Every day S&P 500 Bear 3X Shares), which search to ship 3 times the each day return of the S&P 500, within the lengthy and inverse instructions, respectively. To realize this leverage, the funds rebalance their by-product publicity every day. Whereas this mechanism ensures that the leverage goal is met every day, it additionally introduces a well-documented phenomenon often known as volatility drag: over prolonged horizons, compounding of each day returns causes realized efficiency to diverge considerably from the “theoretical” triple return of the index. Durations of excessive volatility are inclined to speed up this erosion, making such merchandise particularly fragile throughout turbulent markets.
Current analysis The Volatility Edge, A Twin Strategy For VIX ETNs Buying and selling by Zarattini, Mele, and Aziz (2025) suggests a possible path to mitigating this drawback. Their research on volatility-linked ETNs launched the concept of a volatility filter, which compares short-term realized volatility, estimated utilizing a rolling window of current returns, with the implied volatility mirrored within the VIX index. The VIX, usually known as the “concern index”, is a monetary indicator that measures the anticipated volatility of the U.S. inventory market over the subsequent 30 days primarily based on S&P 500 choice costs. It displays short-term market uncertainty or danger, with greater values indicating larger anticipated volatility and elevated investor concern, whereas decrease values recommend a calmer market with smaller worth fluctuations. Importantly, the VIX doesn’t predict the market’s course, solely how a lot costs are anticipated to maneuver. When implied volatility exceeds the short-term realized measure, the technique maintains publicity, when the filter alerts elevated realized danger, publicity is decreased or reduce completely. This straightforward mechanism proved efficient at avoiding a few of the most damaging drawdowns of their backtests.
This method gave us an concept that leveraged fairness ETFs like SPXL and SPXU may gain advantage from an identical filter. Moderately than sustaining publicity unconditionally, one may use the connection between short-term realized volatility and implied volatility as a information for investing. On this means, the technique goals to harness leverage throughout favorable developments whereas avoiding a few of the most damaging durations of volatility drag.
Methodology
On this research, the backtest covers the interval from January 17, 2013, to July 31, 2025. We used each day knowledge for SPXU and SPXL obtained from EODHD.com – the sponsor of our weblog. EODHD presents seamless entry to +30 years of historic costs and elementary knowledge for shares, ETFs, foreign exchange, and cryptocurrencies throughout 60+ exchanges, obtainable through API or no-code add-ons for Excel and Google Sheets. As a particular supply, our weblog readers can take pleasure in an unique 30% low cost on premium EODHD plans..
As a benchmark to evaluate the risk-adjusted returns for our buying and selling methods, we employed SPY’s whole each day knowledge returns from the identical supply. We contemplate SPY essentially the most applicable benchmark, provided that the analyzed ETFs present 3 times the each day return of the S&P 500.
VIX knowledge have been obtained from the FRED, protecting the identical interval.
As talked about within the Introduction, the analysis The Volatility Edge, A Twin Strategy For VIX ETNs Buying and selling impressed us to design the next technique. Since our objective is to match short-term realized volatility with implied volatility, it’s essential to outline each phrases. Quick-term realized volatility was calculated because the annualized normal deviation of SPY returns over current days, which is simple given the supply of the info. In contrast, implied volatility is represented by the VIX, which is out there with a one-day lag. We then use a median of current VIX values to easy SPY’s implied volatility.
As soon as each variables are obtained, their comparability determines whether or not or to not put money into leveraged ETFs corresponding to SPXL (or SPXU for the aim of hedging). This process is repeated every day. The ultimate step is to optimize the size of the time window for each metrics in an effort to obtain the perfect efficiency relative to the benchmark. We due to this fact start by inspecting the ETFs individually.
Fundamental efficiency traits in tables are introduced as follows: the notation perf represents the annual return of the technique, st dev stands for the annual normal deviation, max dd is the utmost drawdown, adjusted Sharpe r is calculated because the ratio of perf to st dev and adjusted Calmar r because the ratio of perf to max dd.
SPXL in Low-Volatility Enviroment
Let’s start with the ETF SPXL (Direxion Every day S&P 500 Bull 3X Shares). SPXL, as a triple-leveraged ETF on the S&P 500 index, performs greatest in periods of regular progress when each day market strikes are comparatively small and the pattern is evident. In such circumstances, leverage works in favor of the investor with out being offset by volatility drag, which in any other case erodes the fund’s worth in choppier markets.
It’s due to this fact essential to establish a easy indicator that helps distinguish favorable environments from turbulent ones. One method is to match short-term realized volatility with implied volatility as measured by the VIX index. When VIX is greater than realized volatility, it signifies that the market expects bigger strikes than are literally occurring—in different phrases, traders are “overpaying for insurance coverage.” For SPXL, this creates a supportive setting: realized swings stay contained, volatility drag is proscribed, and if the market is trending upward, the triple publicity amplifies these positive factors.
Due to this fact, we designed a technique for investing in SPXL primarily based on the connection between realized and implied volatility. Particularly, when the common VIX worth (implied volatility) exceeds SPY’s annualized X-day normal deviation (realized volatility), we take this as a sign to put money into SPXL. In any other case (realized volatility is greater then implied), we stay out of the market. Every place is held for in the future, after which the process is repeated.
Constructing on the paper that guided our pondering, the preliminary technique’s parameters are arrange as SPY’s 10-day annualized normal deviation, whereas the transferring common of the VIX is calculated over the latest 60 days.
Each the graph in Determine 3 and the leads to Desk 3 point out that the SPXL technique delivered considerably greater efficiency, with returns double these of the benchmark. Nevertheless, these positive factors have been accompanied by elevated normal deviation and bigger drawdowns, that are clearly seen within the graph. Whereas these unfavorable facets barely scale back the Sharpe ratio, the Calmar ratio nonetheless displays the effectivity of the method. However, this consequence didn’t absolutely align with our aims, and we due to this fact determined to check the robustness and modify the technique.
The technique was adjusted via two modifications. First, we fastened the 60-day window for the transferring common of VIX values and diversified solely the size of SPY’s annualized normal deviation, particularly utilizing the 5-, 10-, and 20-day measures.
The outcomes present that extending the variety of days used to calculate the usual deviation results in greater annualized returns, whereas drawdowns and volatility decline. This impact will be defined by the truth that shorter home windows (5 or 10 days) are extremely delicate to one-off swings in SPY, which ends up in extra frequent exits from the market, whereas an extended window supplies a smoother and extra steady estimate of realized volatility. Because of this, it’s extra widespread for the common VIX to exceed realized volatility, resulting in extra frequent entries into SPXL throughout upward-trending durations and thus greater returns, whereas the extra steady sign concurrently reduces volatility and drawdowns.
This results in greater Sharpe and Calmar ratios, reaching values exceeding 1 and 0.80, respectively, within the technique primarily based on the 20-day normal deviation. These outcomes outperform the benchmark and meet our expectations. Whereas the modification of realized volatility yielded favorable outcomes, the query stays whether or not adjusting the implied volatility will show equally profitable.
The second modification of the SPXL technique focuses on implied volatility, represented by the common VIX worth over the latest 60 days. On this case, the realized volatility is fastened at a 10-day window, whereas the averaging interval for VIX values varies throughout 10, 20, 30, 40, 60, 80, and 120 days.
Within the case of implied volatility represented by the VIX index, shorter averages produced higher outcomes, which will be defined by the completely different nature of this measure. Whereas realized volatility is backward-looking and requires longer durations to easy out random fluctuations, the VIX responds instantly to present occasions and investor sentiment. When a protracted averaging window is utilized, the sign turns into overly smoothed and loses timeliness. In contrast, shorter home windows, corresponding to 10 or 20 days, seize prevailing market expectations extra precisely and permit faster identification of conditions the place implied volatility exceeds realized volatility. This will increase the chance of well timed entries into SPXL throughout favorable durations, thereby enhancing the technique’s efficiency, whereas the extra noise launched by shorter home windows is much less detrimental on this context. The strongest outcomes have been achieved with the 10-day transferring common of the VIX, yielding a Sharpe ratio near 1 and a Calmar ratio of virtually 0.9. These outcomes are similar to the perfect outcomes obtained within the earlier modification, though the Calmar ratio is noticeably greater.
The outcomes point out that the effectiveness of the SPXL technique relies on a cautious stability between the horizons used for realized and implied volatility. Longer home windows for realized volatility scale back noise and stabilize the sign, whereas shorter home windows for implied volatility protect its responsiveness. Nevertheless, combining the longest realized-volatility window (20 days) with the shortest VIX window (10 days) doesn’t yield the perfect efficiency, suggesting that the 2 parameters work together and have to be collectively optimized. General, the technique performs greatest when realized volatility is measured over a average horizon (10 days) whereas implied volatility is averaged over a brief window (10–20 days), balancing stability and adaptableness.
Our method for SPXL proved efficient, efficiently figuring out durations when it’s favorable to speculate primarily based on the connection between realized and implied volatility. However what if the market strikes in the wrong way? In that case, SPXU, with its inverse and leveraged publicity, may probably profit, elevating the query of whether or not an identical volatility-based indicator will be utilized to information funding choices for SPXU.
SPXU in Low-Volatility Enviroment
On this half, we targeted on the SPXU. SPXU (ProShares UltraPro Quick S&P 500) is a triple-leveraged inverse ETF designed to ship 3 times the inverse each day return of the S&P 500 index. Which means that SPXU positive factors when the S&P 500 declines and loses when the index rises. As a consequence of its leverage, SPXU is extremely delicate to short-term market fluctuations and each day volatility. It performs greatest throughout sustained downward developments available in the market, whereas in uneven or bullish durations, volatility drag and compounding results can considerably erode returns.
SPXU, as a triple-leveraged inverse ETF, positive factors when the S&P 500 declines and loses when the index rises. When short-term realized volatility of SPY exceeds the common VIX, it signifies that the market is transferring greater than traders had anticipated, reflecting heightened turbulence and sudden swings. Such circumstances usually coincide with downward actions available in the market, creating a positive atmosphere for SPXU. In different phrases, excessive realized volatility (SPY’s normal deviation) relative to implied volatility (VIX transferring common) may sign short-term uncertainty and potential declines within the S&P 500, which an inverse leveraged ETF like SPXU can exploit.
Due to this fact, the technique for investing in SPXU is designed as the alternative of the SPXL method. Particularly, when the common VIX worth (implied volatility) is decrease than SPY’s normal deviation (realized volatility), this serves as a sign to put money into SPXU, in any other case, the place is prevented. Every place is held for in the future, after which the process is repeated. Once more, for this evaluation, SPY’s 10-day annualized normal deviation is used as the idea, whereas the transferring common of the VIX is calculated over the latest 60 days. Benchmark additionally stays unchanged.
As we are able to see from the outcomes, this technique didn’t ship extremely worthwhile outcomes, however in comparison with the efficiency of the SPXU ETF alone, it achieved enhancements throughout all measures. Due to this fact, on this case as nicely, we try to change the technique with respect to each implied and realized volatility.
We start once more by first fixing the window for implied volatility, calculating the VIX transferring common over 60 days, whereas various realized volatility, measured as SPY’s normal deviation, throughout 5, 10, and 20 days.
We will see that the modification led to an enchancment when the variety of days used to calculate the usual deviation was decreased, particularly to five days. Since this technique operates on the alternative precept to SPXL, the place longer home windows produced higher outcomes, it’s logical that right here shorter home windows work extra successfully. On this case, the modification improves funding in SPXU, nonetheless, it’s nonetheless not enough to outperform the benchmark.
Now allow us to check the case the place realized volatility, measured as SPY’s normal deviation, is fastened at 10 days, whereas the implied volatility is diversified by altering the window for the VIX transferring common, particularly throughout 10, 20, 30, 40, 60, 80, and 120 days.
We will see that not one of the modifications show to be efficient, because the ratios stay unfavorable in all instances, clearly indicating inefficiency. Whereas the efficiency of SPXU improves considerably via these changes, it nonetheless doesn’t come near surpassing the benchmark.
It’s laborious to beat the efficiency of SPY by simply utilizing the triple-leverage quick ETF within the robust bull market (as was the case for the interval of 2013 to 2025). All the variants had outcomes from -8% to +1%. Nevertheless, what we are able to anticipate is that these variants may have a unfavorable correlation with the SPY ETF itself, and alerts can due to this fact be used as entries to make use of a selective hedge. The worth of the hedge (-8% to +1%) shouldn’t be excessively excessive. Nevertheless, we don’t plan to pursue these selective hedging methods additional, as we’ll dedicate an unbiased article on the subject of hedging sooner or later.
Conclusion
Our evaluation demonstrates that volatility-based filters can enhance the efficiency of leveraged ETFs by figuring out favorable circumstances for publicity. For SPXL, evaluating realized and implied volatility proved to be an efficient method. The technique delivered returns considerably above the benchmark, notably when realized volatility was measured over longer horizons and implied volatility over shorter ones. This mixture allowed for steady but responsive alerts, decreasing noise whereas capturing well timed alternatives.
In distinction, making use of the identical framework to SPXU produced weaker outcomes, suggesting that bearish leveraged publicity is tougher to take advantage of systematically. Whereas the technique improved efficiency relative to holding SPXU outright, it didn’t produce persistently optimistic outcomes. Nonetheless, the efficiency of the SPXU systematic methods means that we could possibly use this method as a selective hedge sooner or later.
Writer: Sona Beluska, Junior Quant Analyst
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