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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Bitcoin ETFs in Conventional Multi-Asset Portfolios

Understanding how Bitcoin-related instruments can fit into traditional portfolios is increasingly relevant for investors. Some risk-averse investors do not like to hold cryptocurrencies in their portfolios strategically; however, they may be open to investing in crypto-linked assets on a tactical level. In this context, our goal is to explore how we can provide short-term Bitcoin exposure while contributing to overall portfolio balance and potential downside protection.

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Can We Finally Use ChatGPT as a Quantitative Analyst?

In two of our previous articles, we explored the idea of using artificial intelligence to backtest trading strategies. Since then, AI has continued to develop, with tools like ChatGPT evolving from simple Q&A assistants into more complex tools that may aid in developing and testing investment strategies—at least, according to some of the more optimistic voices in the field. Over a year has passed since our first experiments, and with all the current hype around the usefulness of large language models (LLMs), we believe it’s the right time to critically revisit this topic. Therefore, our goal is to evaluate how well today’s AI models can perform as quasi-junior quantitative analysts—highlighting not only the promising use cases but also the limitations that still remain.

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Trading the Spread: Bitcoin ETFs vs. Cryptocurrencies Infrastructure ETFs

In this study, we explore the application of simple spread trading strategies using Bitcoin ETFs and cryptocurrency infrastructure ETFs—two highly correlated asset classes due to the broader influence of cryptocurrency market movements. Given their strong relationship, this setup provides a compelling case for implementing pair trading strategies based on mean reversion principles. Building on our previous work, How to Build Mean Reversion Strategies in Currencies, we adapt and extend these models to the cryptocurrency ETF space, demonstrating their broader applicability beyond traditional currency markets. Specifically, we test two sub-methods of mean reversion: linear and exponential. Our goal is to offer a clear and practical example of how traders can leverage these techniques across different asset classes.

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