Can We Blame Index Funds for More Volatile Financial Markets?

Over the past seven decades, U.S. equity-market volatility has roughly doubled—from about 10% to 20%—and this increase is concentrated at the market level and at high frequencies (daily volatility up by ~130%, weekly by ~75%, monthly by ~40%). A new paper by Lars Lochstoer and Tyler Muir argues that this structural change is not driven by macroeconomic fundamentals or firm-level shocks but by the dramatic growth of index-level trading (futures, ETFs, index mutual funds, and extended trading hours). Using statistical investigations—the 1997 introduction of E‑mini S&P 500 futures and historical NYSE trading‑hour changes—the authors provide causal evidence that easier and larger trading of the market portfolio has raised aggregate volatility through higher trading volume and a shift toward systematic demand shocks.

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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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Leveraged ETFs in Low-Volatility Environments

Leveraged ETFs (such as SPXL – (Direxion Daily S&P 500 Bull 3X Shares) offer amplified exposure to the S&P 500, promising high returns but exposing investors to volatility drag caused by daily rebalancing. This effect can significantly erode performance over longer horizons, particularly during periods of elevated market volatility. Inspired by recent research, The Volatility Edge, A Dual Approach For VIX ETNs Trading, focused on volatility-linked ETNs, we propose a volatility filter that adjusts ETF exposure based on the relationship between short-term realized volatility and implied volatility. By reducing exposure in high-volatility periods and maintaining it in calmer markets, this approach aims to harness leverage effectively while mitigating the most damaging drawdowns.

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How Can We Explain the Low-Risk Anomaly?

The low-risk anomaly in financial markets has puzzled researchers and investors, challenging the traditional risk-return paradigm (higher risk->higher return). This phenomenon, where low-risk assets outperform their high-risk counterparts on a risk-adjusted basis, has been observed across various asset classes, including stocks and mutual funds. What may be the possible explanation? Pass-through mutual funds, which aim to replicate the performance of specific market indices, play a crucial role in this context by channeling investor flows and potentially influencing asset prices through demand pressure.

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Why Most Markets and Styles Have Been Lagging US Equities?

Over the past decade and a half, the US equities have set the hard-to-beat performance benchmark. Nearly all of the other countries, no matter if small or big, emerging or developed, have lagged behind. However, what are the forces behind this outperformance? Why did most of the other markets and even investing styles bow to the US large-cap growth dominance? A new paper written by David Blitz nicely analyses the rise of the behemoth.

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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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Join the Race: Quantpedia Awards 2024 Await You

Two weeks ago, we promised you a surprise, and now it’s finally time to unveil what we have prepared for you :).

Our Quantpedia Awards 2024 aims to be the premier competition for all quantitative trading researchers. If you have an idea in your head about systematic/quantitative trading or investment strategy, and you would like to gain visibility on the professional scene, then submit your research paper, and you can compete for an attractive list of prizes. All info about the prizes, submission process, expert committee, and our partners are described in detail on our dedicated subpage: Quantpedia Awards 2024. But we will also give you a quick overview in this blog post.

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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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The Seasonality of Bitcoin

Seasonality effects, one of the most fascinating phenomena in the world of finance, have captured the attention of investors and researchers worldwide. Since these anomalies are often driven by factors other than general market trends, they usually don’t correlate strongly with market movements, which can help reduce the portfolio’s overall risk. Following the theme of our previous article Are There Seasonal Intraday or Overnight Anomalies in Bitcoin?, we decided to extend the data and conduct a more in-depth analysis of our earlier findings. This article explores potential seasonal patterns related to Bitcoin, focusing on whether these patterns are influenced by factors such as current market trends or the level of volatility in the market.

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Dissecting the Performance of Low Volatility Investing

Low volatility investing is an appealing approach to compound wealth in the stock market for the long term. This particular factor investing style exploits the popular naive notion that lower (higher) risk must always equal lower (higher) overall returns. But in fact, this naive assumption is not true, as low-volatility investments often yield more than their high-volatility counterparts. While low-volatility investing has many advantages, it also results in some disadvantages. How to overcome them? Bernhard Breloer, Martin Kolrep, Thorsten Paarmann, and Viorel Roscovan, in their study Dissecting the Performance of Low Volatility Investing, propose a solution.

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