Hedging Tail Risk with Robust VIXY Models

Extreme market events, once perceived as statistical outliers, have become a central concern for investors. The persistence of sharp drawdowns and volatility spikes demonstrates that the cost of ignoring tail risks is not tolerable for long-term portfolio resilience. While diversification can mitigate ordinary fluctuations, it often fails when markets move in unison under stress. This makes explicit protection against severe downside events not just desirable but necessary. Tail hedging addresses this need by providing a structured defense against the most damaging scenarios, ensuring that portfolios remain robust when traditional risk management tools fall short. Using VIXY ETF, we will present and test a range of hedging strategies designed to protect portfolios under stress. By applying robust testing frameworks, we aim to evaluate how different implementations of VIXY ETF-based tail hedges perform across a variety of market environments, highlighting both their strengths and inherent trade-offs.

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Quantpedia Composite Seasonality in MesoSim

In one of our older posts titled ‘Case Study: Quantpedia’s Composite Seasonal / Calendar Strategy,’ we offer insights into seasonal trading strategies such as the Turn of the Month, FOMC Meeting Effect, and Option-Expiration Week Effect. These strategies, freely available in our database, are not only examined one by one, but are also combined and explored as a cohesive composite strategy. In partnership with Deltaray, using MesoSim — an options strategy simulator known for its unique flexibility and performance — we decided to explore and quantify how our Seasonal Strategy performs when applied to options trading. Our motivation is to investigate whether this strategy can be improved in terms of risk and return. We aim to systematically harvest the VRP (volatility risk premium) timing the entries using calendar strategy to avoid historically negative trading days.

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Time-Varying Equity Premia with a High-VIX Threshold

What does one of the most popular and well-known metrics, VIX, tell us about future returns? Academic research (Bansal and Stivers, July 2023) shows that a common, intuitive 20/80 thumb rule can be applied as time-variation in the returns earned from equity-market exposure can be explained well by a simple 2-term risk-return specification, which predicts (1) much higher returns 20% of the time following after VIX exceeds a high threshold at around its 80th percentile and (2) lower excess returns following a high market sentiment. They argue that VIX and market sentiment tend to measure complementary aspects of risk: the level of risk (VIX) and the price of risk or risk appetite (sentiment), and that, thus, both terms should be accounted for when evaluating time variation in the equity market’s risk premium.

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How Retail Loses Money in Option Trading

Over the last few years, we may have noticed a significant growth in retail investing. No surprise, the COVID pandemic outbreak increased the numbers even more, and undoubtedly, options trading is no exception. According to the authors (de Silva, Smith, Co), retail traders seek options expecting spikes in volatility and, for that reason, incline toward firms with more media coverage. Furthermore, their trading increases around the time of firms’ earnings announcements. As a result, market makers benefit from the behavior mentioned above, which causes a large flow of money from retail to market makers.

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What’s the Relation Between Grid Trading and Delta Hedging?

Delta hedging is a trading strategy that aims to reduce the directional risk of short option strategy and reach a so-called delta-neutral position. It does so by buying or selling small increments of the underlying asset. Similarly, grid trading is a trading strategy that buys/sells an asset depending on its price moves. When the price falls, it buys and sells when the price rises a certain amount above the buying price. This article examines the similarities between delta hedging and grid trading. Additionally, it analyzes numerous versions of grid trading strategies and compares their advantages and disadvantages.

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Implied Volatility Indexes for European Government Bond Markets

Volatility indexes are essential parts of the financial markets. They offer investable opportunities and exposure to the volatility, but most importantly, those indexes offer forward-looking measures of option-implied uncertainty. Therefore, such indexes are often used as indicators of risk or sentiment in the markets. For example, the well-known VIX index is often called the fear-index. The volatility indexes are not exclusive to the equity market. There are fixed-income option-implied volatility indexes for US Treasury futures, but the European fixed income market lacks such index. This novel research paper by Jaroslav Baran and Jan Voříšek fills this gap and proposes volatility indexes, connected to the euro bond futures using the Cboe TYVIX (US Treasury implied volatility index) (2018) methodology. As a result, the TYVIX and euro bond futures volatility indexes are directly comparable.

Authors: Jaroslav Baran and Jan Voříšek

Title: Volatility indices and implied uncertainty measures of European government bond futures

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Cryptocurrency Volatility Index

Whenever traders want to assess the stock market’s mood, there is one really popular and useful index the most of them turn to. Yes, you guessed it right, it’s CBOE’s VIX Index. And which index can we use if we want to determine the mood of the cryptocurrencies? We can turn to a paper written by Fabian Woebbeking, which offers the methodology to compute two cryptocurrency volatility indexes (CVX & CVX76). The CVX and CVX76 Indexes also extract the market’s expectation of future volatility from option prices, but from options on the Bitcoin. The research suggests that the cryptocurrency option market has finally reached a sufficient market size to extract stable cryptocurrency volatility information.

Authors: Fabian Woebbeking

Title: Cryptocurrency Volatility Markets

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Why Do Top Hedge Funds Outperform?

Every hedge fund manager and every trader wants to know what strategies are employed in a fund ran by his competition. The curiosity is even stronger if we want to see how strategies are mixed in the kitchen of the most successful hedge funds. Top performing funds are usually notoriously secretive about their portfolios. But we still can learn something from the history of their monthly returns. One such interesting methodology is described in a research paper written by Canepa, Gonzalez, and Skinner. Their analysis hints that the top-performing hedge funds are usually successful because they are able to manage their factor exposure better. They are not dependent so much on classical equity risk factors as average funds are. And if they are exposed to some risk factor, the top-performing hedge funds are able to close underperforming factor strategy sooner than average funds.

Authors: Canepa, Gonzales, Skinner

Title: Hedge Fund Strategies: A non-Parametric Analysis

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Quantitative Easing Increases Connectedness of Equities and Commodities

Quantitative Easing policy in the US triggered a massive inflow of liquidity to financial markets. This liquidity, combined with the growing popularity of commodities as an asset class, is a cause for a higher inter-connectedness among equity and commodities markets. A recent academic study written by  Ordu-Akkaya and Soytas shows that commodities are not such a good diversifier as they used to be in the past. Moreover, commodity markets are also affected, as periods of higher equity volatility impact commodities significantly more …

Authors: Ordu-Akkaya, Soytas

Title: Unconventional Monetary Policy and Financialization of Commodities

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