Category: volatility arbitrage

The decomposition of risks and returns into overnight and intraday components is an emerging area of research. In this post, we examine how these components differ in terms of volatility clustering and the variance risk premium, and what this implies for forecasting, risk management, and strategy design.

Breaking Down the Volatility Risk Premium: Overnight vs. Intraday Returns

The decomposition of the volatility risk premium (VRP) into overnight and intraday components is an emerging area of research. Most studies indicate that the VRP serves as compensation for investors bearing overnight risks.

Reference [1] continues this line of research, with its main contribution being the decomposition of the variance risk premium into overnight and intraday components using a variance swap approach. The study also tests the predictive ability of these components and examines the seasonality (day-of-week effects) of the VRP.

Findings

-The paper decomposes the variance risk premium into overnight and intraday components across the US, Europe, and Asia.

-It finds that the variance risk premium is significantly negative during the overnight non-trading period.

-During the intraday trading period, the variance risk premium becomes positive and often insignificant.

-The results show that the overall negative variance risk premium documented in prior studies is largely driven by the overnight component.

-The study uses the P&L of a hypothetical variance swap to analyze these components.

-The intraday variance risk premium captures short-term risk and has predictive power over 1 to 3-month horizons.

-The overnight variance risk premium reflects longer-term risk and shows predictive ability over 6 to 12-month horizons.

-The findings highlight the importance of non-trading periods in explaining the behavior of the variance risk premium.

In summary, the study reaffirms that the variance risk premium is significantly negative during the non-trading overnight period, while it becomes positive and often insignificant during the intraday trading period.

An interesting finding is the day-of-week seasonality. For instance, going long volatility at the open and closing the position at the close tends to be profitable on most days, except Fridays.

Reference

[1] Papagelis, Lucas and Dotsis, George, The Variance Risk Premium Over Trading and Non-Trading Periods (2024). SSRN 4954623

Intraday and Overnight Volatility Clustering Effect

Volatility clustering is a phenomenon observed in financial markets where periods of high volatility tend to cluster together, followed by periods of low volatility. This pattern suggests that extreme price movements are not randomly distributed over time but rather occur in clusters or groups.

Volatility clustering has undergone extensive study within the daily timeframe. Reference [2] delves into volatility clustering within intraday and overnight timeframes. It specifically investigates clustering within each timeframe and between them.

Findings

-The paper studies volatility clustering in intraday and overnight returns across 15 global equity markets.

-It finds that volatility clustering is present in both intraday and overnight returns across multiple time scales, from daily to long-term horizons.

-The results show that volatility clustering is generally stronger in overnight returns than in intraday returns.

-Cross clustering between intraday and overnight volatility is relatively weak within each market, especially at shorter time scales.

-The findings are consistent across both developed and emerging markets, indicating a universal pattern.

-The study highlights the importance of considering both short-term and long-term risks in equity markets.

-The results suggest that volatility dynamics differ between trading and non-trading periods.

-The paper provides implications for trading and risk management strategies based on volatility clustering behavior.

In short, the paper shows that volatility clustering is a universal feature of both intraday and overnight returns across multiple time scales. It also finds that clustering is stronger overnight, while cross-effects between intraday and overnight volatility remain weak, with consistent patterns across global markets.

Reference

[2] Xiaojun Zhao, Na Zhang, Yali Zhang, Chao Xu, Pengjian Shang, Equity markets volatility clustering: A multiscale analysis of intraday and overnight returns, Journal of Empirical Finance 77 (2024) 101487

Closing Thoughts

Taken together, these studies show that volatility dynamics differ significantly between intraday and overnight periods, both in terms of risk pricing and clustering behavior. The variance risk premium is largely driven by the overnight component, while intraday and overnight volatility exhibit distinct clustering patterns with limited interaction. These findings highlight the importance of separating trading and non-trading periods in both forecasting and risk management, as each captures different horizons and sources of risk, offering more refined inputs for portfolio construction and strategy design.

Momentum has been studied extensively across equities, commodities, and other asset classes, with well-documented evidence of cross-sectional and time-series continuation effects. More recently, an emerging line of research has shifted attention to momentum in option returns, examining whether derivative markets exhibit their own systematic return patterns.

In this post, we review the latest evidence on option return momentum across both monthly and intraday horizons and assess the economic mechanisms that may explain these persistent dynamics.

Momentum in the Option Market

In the financial market, momentum is the tendency for assets to continue moving in the same direction. It is a reflection of the underlying strength or weakness of an asset’s price action and can be used to identify trends. Momentum is one of the most pervasive market phenomena and can be observed in nearly all stock markets around the world.

Does this anomaly exist in other asset classes?

Reference [1] studied momentum in the options market. It examined the returns of delta-neutral straddles on individual equities.

Findings

-The study finds strong evidence of momentum in option returns, as options that performed well over the previous 6 to 36 months tend to generate high returns in the subsequent month.

-Momentum is observed under both cross-sectional and time-series definitions of past performance.

-The strategy is profitable across all five-year subsamples and carries significantly lower risk than short straddle positions on the S&P 500 Index or individual stocks.

-There is no evidence of momentum crashes in option returns, although the sample length may limit detection of such events.

-The authors find limited evidence of short-term cross-sectional reversal, where options that outperform in one month may underperform in the following month.

-There is no evidence of long-run reversal in option returns, in contrast to equities, and momentum persists even at 2- to 3-year horizons.

-Option momentum differs from stock momentum because the results are based on delta-hedged positions and remain robust after controlling for stock momentum effects.

-Momentum profits are unaffected by controls for implied versus historical volatility and other option characteristics, and remain significant after factor risk adjustments.

-The study shows that high historical-return options significantly outperform low-return options across multiple horizons, including when using out-of-the-money options or delta-hedged returns.

In short, like in equities, options also exhibit momentum. The options momentum is mean-reverting in the short term and trending in the long term.

Reference

[1] Heston, Steven L. and Jones, Christopher S. and Khorram, Mehdi and Li, Shuaiqi and Mo, Haitao, Option Momentum (2022), SSRN 4113680

Momentum in the Option Market, Intraday Case

While the previous article examines momentum in option returns across monthly horizons, Reference [2] extends this line of research by focusing on intraday option return dynamics.

Findings

-The paper documents novel seasonal patterns in intraday returns of individual stock option straddles.

-Despite being delta-neutral, straddle returns exhibit the same persistent intraday seasonality as their underlying stocks.

-Returns in a given half-hour interval predict returns in the same interval on the following trading day.

-This continuation effect is strongest at the market open and close, referred to as morning and afternoon momentum.

-Morning momentum is attributed to investors’ underreaction to volatility shocks.

-Afternoon momentum is driven by persistent inventory management behavior by option market makers.

In summary, it was shown that a straddle’s return during a particular 30-minute trading interval today positively predicts its return during the same interval on subsequent days. Morning momentum reflects a continued under-reaction to overnight volatility news. Afternoon momentum, on the other hand, is attributed to persistent price pressure caused by inventory management from option market makers.

Reference

[2] Da, Zhi and Goyenko, Ruslan and Zhang, Chengyu, Intraday Option Return: A Tale of Two Momentum (2024), SSRN 5018430

Closing Thoughts

Taken together, these studies show that option markets exhibit systematic return patterns across both monthly and intraday horizons. Momentum persists over 6 to 36 months without the long-run reversals observed in equities, while intraday straddle returns display predictable continuation at the market open and close.

The evidence suggests that option return dynamics are driven by distinct forces, including behavioral underreaction, inventory management by market makers, and structural features of volatility trading. Collectively, these findings reinforce the view that options are not merely derivatives of stocks, but markets with their own persistent and economically meaningful return patterns.

Delta hedging is a fundamental topic in portfolio and risk management. In this post, we discuss which volatility measure should be used in the delta hedging process, while a future edition will examine the appropriate hedging frequency and time horizon.

Which Free Lunch Would You Like Today Sir?

Reference [1] is a classic article on delta hedging that addresses the following question: if an investor has an accurate estimate of future realized volatility that differs from current implied volatility, a position can be initiated to exploit this discrepancy and then dynamically hedged—but which volatility should be used as the input in the hedging process?

Hedging with Actual Volatility

Pros

-Hedging with actual volatility guarantees the final profit at expiration, equal to the difference between theoretical option values under actual and implied volatility.

-The final profit has zero variance, making it attractive from a long-term, global risk–reward perspective.

-Expected profit is often insensitive to small errors in the volatility used for hedging, providing some robustness to estimation error.

Cons

-Mark-to-market P&L during the life of the option can fluctuate significantly, which is problematic for short-term risk management.

-Interim P&L depends on the true drift of the underlying asset, introducing uncertainty before expiration.

-In practice, traders are rarely confident in their estimate of actual volatility, weakening the appeal of this approach.

Hedging with Implied Volatility

Pros

-Mark-to-market P&L evolves smoothly with no random fluctuations, which is advantageous for daily risk monitoring.

-The trader only needs to be directionally correct about volatility (i.e., actual > implied or vice versa), not to estimate actual volatility precisely.

-Implied volatility is directly observable from the market, simplifying implementation.

Cons

-The final profit is path-dependent and therefore uncertain at inception.

-While profits are always positive in expectation, their magnitude cannot be known in advance.

-Profitability depends on the realized price path, particularly whether the underlying remains near regions of high gamma.

Reference

[1] R Ahmad, P Wilmott, Which Free Lunch Would You Like Today Sir?, Wilmott, 2005

Delta Hedging with Implied vs. Historical Volatility

Similar to the previous paper, Reference [2] examines the effectiveness of hedging using implied versus realized volatility. The study is based on empirical analysis using index ETF options, specifically the Nasdaq-100 ETF (QQQ).

Findings

-The study examines the role of volatility estimation in delta-neutral hedging, with a focus on short-term options trading and risk management.

-It empirically compares implied volatility (IV) and historical volatility (HV) using Nasdaq-100 ETF (QQQ) options over several months of daily data.

-The analysis evaluates hedging performance, return stability, transaction costs, hedging errors, and sensitivity under varying market volatility conditions.

-Results show that IV-based hedging delivers more stable returns, lower return volatility, and better risk mitigation, making it more suitable for conservative and risk-averse investors.

– IV-based strategies benefit from forward-looking, market-implied inputs, which improve delta accuracy, reduce rebalancing frequency, and lower transaction costs.

-HV-based hedging can generate higher potential returns but exhibits greater variability, larger hedging errors, and higher portfolio risk, particularly during volatile markets.

-Sensitivity tests confirm that IV adapts more effectively to changing market conditions than HV.

-The study highlights a clear trade-off between stability and return potential, emphasizing that volatility measure selection should depend on market conditions and risk preferences.

The findings provide practical guidance for traders and risk managers and contribute to the literature on optimal volatility modeling under real-world constraints. Though the paper has some limitations, notably the small sample size, this research direction is worth pursuing, particularly in establishing a delta band and determining the optimal hedging frequency.

Reference

[2] Yimao Zhao, Implied Volatility vs. Historical Volatility: Evaluating the Effectiveness of Delta-Neutral Hedging Strategies, Proceedings of the 2025 5th International Conference on Enterprise Management and Economic Development (ICEMED 2025)

Closing Thoughts

Taken together, these two studies highlight that the choice of volatility input is an important decision in delta hedging, rather than a technical detail. Both papers show that implied volatility, with its forward-looking and market-based nature, generally delivers more stable hedging performance, lower tracking errors, and better risk control, particularly in short-term and actively rebalanced strategies.

Historical or realized volatility, while simpler and sometimes effective in calmer market regimes, tends to lag during volatility shifts and leads to larger hedging errors. The broader implication for practitioners is that effective delta hedging requires aligning the volatility measure with market conditions, risk tolerance, and trading horizon, rather than relying on a one-size-fits-all approach.

There are several popular options strategies frequently discussed in the trading and investing literature, as well as on social media. In a previous post, we examined the effectiveness of the covered call strategy, which has gained wide adoption among retail investors. In this edition, we extend our critical evaluation to another widely used approach—the options collar, a strategy employed by both retail traders and institutional investors.

Assessing the Effectiveness of Zero-Cost Collar in Different Markets

The zero-cost collar strategy is an options trading strategy that involves the simultaneous purchase of a put option and the sale of a call option. The options are usually of the same maturity, and the transaction results in a zero or small credit to the trader’s account.

This strategy is often used by investors who are bullish on a stock but want to protect themselves against a potential drop in the price. By buying the put option, they have the right to sell the stock at a predetermined price (the strike price). If the stock price falls below the strike price, they can sell the stock and offset any losses.

The sale of the call option helps to offset the cost of the put option and results in a zero or small credit to the trader’s account. This strategy is sometimes referred to as a “zero cost” collar because the net cost of the trade is zero.

Reference [1] examined the effectiveness of the zero-cost collar strategy in the developed and developing markets.

Findings

-The paper’s objective is to provide investors with a continuously implemented trading strategy that can effectively handle turbulent market periods such as the Dotcom bubble, the 2008–2009 financial crisis, and the COVID-19 pandemic.

-The study analyzes stock indices from six countries across both developed and developing economies to assess how extreme market events affect performance.

-Zero-cost collars are proposed as a costless option-based protection strategy, created by equating the cost of the long and short option components.

-Prior literature has not evaluated zero-cost collars across different rebalancing frequencies or tested their outcomes in both turbulent and stable markets.

-Results show that zero-cost collars generate strong returns when market volatility is moderate, and the underlying indices perform well, especially when the put strike is set at a higher level.

-The strategy performs respectably during severe downturns as well as during trending or declining markets.

– Its effectiveness depends on market conditions and the choice of strike levels.

Overall, the paper contributes a practical trading strategy that helps investors manage turbulent market conditions through the continuous application of zero-cost collars.

Reference

[1] Lj Basson, Suné Ferreira-Schenk and Zandri Dickason-Koekemoer, The performance of zero-cost option derivative strategies during turbulent market conditions in developing and developed countries, Cogent Economics & Finance, Volume 10, 2022 – Issue 1

How the Airlines Hedge Fuel Costs

The recent rise in the cost of airline tickets can be attributed in part to the escalating fuel prices, which significantly affect operating expenses for airlines. To counter the adverse impact of fuel price volatility, airlines often adopt a strategic approach known as fuel hedging. This practice involves entering into financial contracts to secure future fuel purchases at predetermined prices, mitigating the vulnerability to sudden spikes in fuel costs. Fuel hedging provides airlines with a degree of price certainty, offering a measure of stability in budgeting and operational planning while allowing them to better manage the economic challenges posed by fluctuating fuel prices.

Amongst the US airlines, Southwest Airlines distinguishes itself for its efficient execution of the hedging strategy. It has maintained a record of profitability since 1973, an accomplishment that sets it apart in the US airline sector. Expert observers attribute Southwest’s sustained financial success to its proficient utilization of derivatives for the purposes of hedging. An analysis of Southwest Airlines’ financial statements across multiple years reveals a distinct trend: the share of jet fuel expenses is consistently lower compared to the industry norm. This achievement can be directly attributed to the precise implementation of their jet fuel hedging strategy, a practice that effectively shields the airline from fluctuations in fuel prices.

Reference [2] examined the fuel hedging strategy of Southwest Airlines in detail.

Findings

-The paper analyzes why hedging jet fuel is critical for airlines, given the high volatility of oil prices, and highlights how Southwest Airlines’ long-term low-cost strategy is closely tied to its effective fuel-hedging program.

-It examines Southwest’s financial background and stock performance as a foundation for evaluating its hedging approach.

-The study identifies four key hedging strategies used by Southwest Airlines: call options, collar structures, call spreads, and put spreads.

-By combining these four strategies, Southwest effectively mitigated jet-fuel price risk without engaging in speculative derivative positions.

-A comparison with other airlines shows that Southwest’s disciplined, non-speculative approach contributed significantly to its hedging success and cost stability.

-The paper also evaluates how COVID-19 and oil-price movements related to production-cut agreements adversely affected Southwest, leading to over-hedging and substantial losses in 2020.

In short, the paper discussed the intricacies of Southwest Airlines’ hedging strategies and demonstrated their value-added effect. It is apparent that the deployment of hedging by Southwest Airlines yields advantages. However, it is important to underscore the necessity of a well-designed hedging program, one that avoids the potential pitfall of over-hedging.

Reference

[2] Xiao Han, Hedging Strategy Analysis of Southwest Airlines, 2023, Proceedings of the 6th International Conference on Economic Management and Green Development

Closing Thoughts

Taken together, the two studies illustrate how collar-based strategies can play an important role in managing volatile market conditions, whether for broad equity portfolios or for highly fuel-sensitive industries such as airlines. The first study shows that zero-cost collars can deliver respectable risk-adjusted outcomes during major market disruptions, particularly when volatility is moderate, and strike selection is calibrated appropriately. The second study highlights how Southwest Airlines effectively applied options structures, including collars, to hedge jet-fuel exposure, while also underscoring the risks of over-hedging during periods such as COVID-19. Collectively, the evidence reinforces that collar strategies can be valuable risk-management tools, but their effectiveness depends critically on market regime, implementation frequency, and disciplined design.

Volatility and volatility of volatility are highly correlated and share many similar characteristics. However, there are subtle but important differences between them. In this post, we will examine some of these differences and explore an application of volatility of volatility in portfolio management.

Improving Portfolio Management with Volatility of Volatility

Managing portfolios using volatility has proven effective. Reference [1] builds on this research by proposing the use of volatility of volatility for portfolio management. The rationale behind using volatility of volatility is that it represents uncertainty.

Unlike risk, which refers to situations where future returns are unknown but follow a known distribution, uncertainty means that both the outcome and the distribution are unknown. Stocks may exhibit uncertainty when volatility or other return distribution characteristics vary unpredictably over time.

Practically, the author used a stock’s daily high and low prices to derive its volatility of volatility.

Findings

-The study investigates how volatility-managed investment strategies perform under different levels of uncertainty across stocks and over time.

-A new measure of volatility-of-volatility (vol-of-vol) is introduced as a proxy for uncertainty about risk, capturing a unique dimension distinct from traditional volatility.

-Results show that abnormal returns from volatility management are concentrated in stocks with low uncertainty and during periods of low aggregate uncertainty.

-The effectiveness of sentiment-based explanations for volatility-managed returns is conditional on the level of uncertainty.

-Cross-sectional differences in uncertainty help explain why volatility-managed factor portfolios perform unevenly across stocks and time.

-Theoretical analysis extends a biased belief model, showing that higher vol-of-vol reduces volatility predictability and belief persistence, weakening the benefits of volatility timing.

-The study hypothesizes that volatility management is most effective for low-uncertainty stocks and in low-uncertainty market environments.

-Empirical tests use realized vol-of-vol derived from intraday high and low prices as the measure of uncertainty.

-Consistent with prior literature, uncertainty is positively related to future returns and contains unique predictive information not explained by other stock characteristics.

-Volatility management significantly improves risk-adjusted performance in low-uncertainty stocks and during low aggregate uncertainty periods, while uncertainty also helps explain performance variation across asset pricing factor portfolios.

In short, using the volatility of volatility as a filter proves to be effective, particularly for low-uncertainty stocks.

We find it insightful that the author distinguishes between risk and uncertainty and utilizes the volatility of volatility to represent uncertainty.

Reference

[1] Harris, Richard D. F. and Li, Nan and Taylor, Nicholas, The Impact of Uncertainty on Volatility-Managed Investment Strategies (2024), SSRN 4951893

Beyond volatility of volatility

This section is written by Alpha in Academia

The Volatility of Volatility Index (VVIX) is a composite measure, driven by both short-term market panic and long-term risk expectations.

For years, the VVIX, often dubbed the “fear of fear” index, was treated primarily as a measure of the volatility of volatility (VOV), but new research reveals it contains a second, equally critical component: Long-Run Variance (LRV).

Using a sophisticated model and leveraging a novel technique involving risk-neutral cumulant data extracted from VIX options, researchers decomposed the VVIX dynamics. Their analysis reveals that the factors driving the index change dramatically depending on market conditions. Specifically, the short-term panic measure, VOV, significantly contributes only during acute periods of financial distress, which aligns with intuition. However, during stable or bull markets, the VVIX is primarily driven by the LRV component, reflecting persistent, underlying risk expectations.

In fact, when testing the explanatory power on market-neutral straddle portfolios using S&P 500 options, combining LRV and VOV produced an adjusted explanatory power up to three times greater than baseline models. The finding shows that the index provides “a clear answer to the question of the informational content of the VVIX, showing that it reflects not only the VOV but also an additional important component—the LRV”. Investors should thus view the VVIX not just as a fear gauge, but as a dual-sensor monitoring immediate market stress and long-term risk.

Reference

[2] Bacon, Étienne and Bégin, Jean-François and Gauthier, Geneviève, Beyond volatility of volatility: Decomposing the informational content of VVIX, 2025, SSRN 5611090

Closing Thoughts

In summary, both studies emphasize the role of volatility-of-volatility in understanding risk and market behavior. The first shows that volatility management is most effective in low-uncertainty environments, while the second reveals that the VVIX reflects not only short-term market stress but also long-term risk expectations. Together, they suggest that volatility-of-volatility offers deeper insight into both portfolio performance and the broader dynamics of market uncertainty.

Tail risk hedging plays a critical role in portfolio management. I discussed this topic in a previous article. In this post, I continue the discussion by presenting different techniques for managing tail risks.

Hedging with Puts: Do Volatility and Skew Signals Work?

Portfolio hedging remains a complex and challenging task. A straightforward method to hedge an equity portfolio is to buy put options. However, this approach comes at a cost—the option premiums—leading to performance drag. As a result, many research studies are focused on designing effective hedging strategies that offer protection while minimizing costs.

Reference [1] presents the latest research in this area. It examines hedging schemes for equity portfolios using several signals, including MOM (momentum), TREND, HVOL (historical volatility), IVOL (implied volatility), and SKEW. The study also introduces a more refined rehedging strategy for put options:

-If, during the investment period, a put option’s delta falls to −0.9 or lower, the option is sold to lock in profits and avoid losing them in case of a sudden price reversal.

-Put options are bought when implied volatility is below 10%, as they are considered cheap. No position is taken if implied volatility is above 30%, to avoid overpaying for expensive options.

Findings

-The study investigates how option strategies can be integrated into equity portfolios to improve performance under risk constraints. It highlights weaknesses in traditional equity and fixed-income diversification for institutional investors.

-The research tests backward-looking signals from equity markets and forward-looking signals from options markets in covered call and protective put strategies.

-The TREND signal is found to be the most valuable, reducing portfolio risk without reducing returns compared to equity-only portfolios.

-The SKEW signal has a positive impact on GMV allocation but is less effective under EW allocation.

-Adding extra trading rules (TR1, TR2) does not enhance performance and is often negative.

-Backtests of long-put strategies confirm that the TREND signal offers the best balance between downside protection and performance preservation.

-Bootstrapped results diverge from backtests, showing that HVOL and IVOL signals outperform the BASE portfolio in risk-adjusted terms.

-The differences between bootstrap and backtest results suggest that the effectiveness of signals depends on the prevailing market regime.

In short, buying put options using the TREND signal appears to improve portfolio risk-adjusted returns. While SKEW and IVOL add little in backtests, they perform better in bootstrapped results, suggesting that the effectiveness of put protection strategies is regime-dependent.

This study offers a comprehensive evaluation of various hedging rules. There is no conclusive answer yet, implying that designing an efficient hedging strategy is complex and requires ongoing effort. Still, the article is a strong step in the right direction.

Reference

[1] Sylvestre Blanc, Emmanuel Fragnière, Francesc Naya, and Nils S. Tuchschmid, Option Strategies and Market Signals: Do They Add Value to Equity Portfolios?, FinTech 2025, 4(2), 25

Tail Risk Hedging with Corporate Bond ETFs

Reference [2] proposed a tail risk hedging scheme by shorting corporate bonds. Specifically, it constructed three signals—Momentum, Liquidity, and Credit—that can be used in combination to signal entries and exits into short high-yield ETF positions to hedge a bond portfolio.

Findings

-Investment Grade (IG) bonds in the US typically trade at modest spreads over Treasuries, reflecting corporate default risk.

-During market crises, IG spreads widen and liquidity decreases due to rising credit risk and forced selling by asset holders such as mutual funds.

-This non-linear widening of spreads during drawdowns is referred to as downside convexity, which can be captured through short positions in IG ETFs.

-The study develops three signals—Momentum, Liquidity, and Credit—to time entry and exit for short IG positions as a dynamic hedge.

-The dynamic hedge effectively protects high-carry bond funds like PIMIX and avoids drawdowns for funds such as DODIX, even after considering trading and funding costs.

-Each signal captures different aspects of the IG bond market, and their combination provides the strongest results, improving the Sortino ratio by at least 0.7.

-The hedge model performs consistently well across a broad range of tested parameters, showing robustness.

-Shorting IG (LQD) and HY (HYG) ETFs is found to be more cost-effective than shorting individual IG bonds, due to liquidity and low bid-ask spreads.

-IG and HY CDXs, despite larger volumes, lack the downside convexity of ETFs and are less effective for hedging.

Overall, ETF-based hedging delivers both cost efficiency and strong downside protection, making it a practical approach for institutional investors.

An interesting insight from this paper is that it points out how using corporate ETFs benefits from downside convexity, while using credit default swaps, such as IG CDXs, does not.

Reference

[2] Travis Cable, Amir Mani, Wei Qi, Georgios Sotiropoulos and Yiyuan Xiong, On the Efficacy of Shorting Corporate Bonds as a Tail Risk Hedging Solution, arXiv:2504.06289

Closing Thoughts

Both studies highlight the importance of adapting traditional portfolio strategies by incorporating alternative approaches to better manage risk and improve performance. The first paper shows how option-based overlays, particularly when guided by signals such as trend, can enhance equity portfolios by providing downside protection without materially reducing returns. The second paper demonstrates that credit and liquidity risks in investment-grade bonds can be more effectively managed through dynamic hedging with liquid bond ETFs. Together, these findings underscore that integrating derivative-based strategies offers investors practical tools to navigate market volatility, reduce drawdowns, and achieve more resilient portfolio outcomes.