Extreme VIX: Regime Shifts and Return Predictability

The Volatility Index (VIX) is widely regarded as a forward-looking measure of market uncertainty and investor sentiment. Although it has been extensively studied, certain aspects remain insufficiently explored.

Reference [1] investigates the implications of extreme VIX values, defined as VIX > 45, and examines two key research questions in this context.

1. Extreme implied volatility (VIX > 45) is followed by significantly positive equity returns over three-month and one-year horizons, and
2. Negative returns, elevated volatility, and deteriorating sentiment increase the probability of a VIX spike (VIX > 45)

Using U.S. data from 2008 to 2025, the authors employ linear regression, logistic regression, and GARCH(1,1) models to conduct the analysis. They pointed out,

Results show that extreme VIX spikes offer contrarian signals, with significant positive returns over three-month horizons. Logistic regression confirms significance over both three-month and one-year periods. These findings remain robust after controlling for valuation, credit spreads, PMI, sentiment ratios, and interaction effects. While GARCH captures conditional variance, it lacks forward-looking predictive power in high-stress regimes. Overall, the evidence suggests that volatility timing merits consideration as part of a tactical allocation framework. Our findings also contribute to the market efficiency debate by integrating market expectations with economic indicators for risk-aware decision-making.

In short, the paper's conclusions are as follows,

• The evidence robustly supports the contrarian effect at the 3-month horizon. At the 1-year horizon, the predictive power comes primarily from continuous volatility measures rather than the binary VIX>45 indicator.
• The results clearly demonstrate that extreme volatility episodes are statistically predictable using valuation, sentiment, and medium-term volatility measures. The evidence shows regime transitions are preceded by identifiable market conditions rather than occurring randomly.

This study makes an important contribution by formally examining phenomena long observed by practitioners and by providing results with meaningful practical implications.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Fazio, P., and Spohn, D. (2026). Predictive Signals from VIX Spikes: A Comparative Study of Linear, Logistic, and GARCH-Based Return Forecasting Models. Preprints.org.

Article Source Here: Extreme VIX: Regime Shifts and Return Predictability

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Volume Effects in Pairs Trading Performance

Volume is an important factor that has not been sufficiently studied in the literature, although increasing attention is now being devoted to its role. For example, we recently discussed the link between intraday volume and volatility.

Continuing this line of inquiry, Reference [1] revisits the age-old quantitative strategy of pairs trading and incorporates volume analysis into the framework. The author studied pairs formed from the S&P500 constituent stocks using the conitegration method. The data spans from 2005 to 2024. They pointed out,

This study investigated pairs trading strategies across market segments, parameters, weighting methods, and cost structures using U.S. equity data (2005-2024). Volume emerged as the dominant performance factor, with high-volume pairs consistently outperforming low-volume counterparts across multiple metrics, including superior returns when converging towards the historical relationship. The success of volume-based segmentation may stem partly from grouping stocks with similar trading characteristics, creating more stable price relationships. Notably, optimal parameter configurations were remarkably similar between volume deciles, demonstrating consistent underlying market dynamics across liquidity environments.

Contrary to efficient market predictions, performance improved in the testing period (2015-2024), suggesting structural market changes or institutional constraints have preserved the strategy’s effectiveness despite widespread documentation. Different weighting methodologies produced similar results, with equal weighting combining simplicity and strong performance.

In short, the paper finds that trading volume is a dominant performance driver, with high-volume pairs consistently outperforming low-volume counterparts. Contrary to efficient market predictions, strategy performance improved in the later sample period, while results remained robust across parameter choices and weighting schemes. Overall, pairs trading still remains profitable.

This represents an important contribution that may reinvigorate research on pairs trading and trading volume.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Mustafa Hussein, Can Safety and Profitability Coexist? Performance Analysis of Pairs Trading among S&P 500 Stocks, 2025, University of Gothenburg

Post Source Here: Volume Effects in Pairs Trading Performance

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State-Dependent Correlation Between the S&P 500 and the VIX

It is well known that the correlation between the S&P 500 and the VIX index is negative. In fact, arbitrage and hedging strategies have been designed around this relationship, for example, hedging vega risks with delta.

However, practitioners also recognize that this correlation can break down and even turn positive. Reference [1] addresses this issue by examining the time-varying nature of the correlation. The paper first posits and demonstrates that volatility-of-volatility (VoV) risk is distinct from volatility (Vol) risk, and then classifies the market into four regimes:

1. Low VOL risk and low VOV risk (LVOL–LVOV)
2. High VOL risk and low VOV risk (HVOL–LVOV)
3. Low VOL risk and high VOV risk (LVOL–HVOV)
4. High VOL risk and high VOV risk (HVOL–HVOV).

The authors pointed out,

We develop theoretical hypotheses on the correlations between the S&P 500 and VIX based on the VFE and two types of risk in the S&P 500–VIX pair: (1) VOL risk in the S&P 500 (i.e., the underlying asset) and (2) VOV risk in VIX (i.e., its VOL product). According to our hypothesis, the correlations between the S&P 500 and VIX do not remain constant and change across various VOL and VOV risk states. We developed a regime‐switching model with state‐contingent correlations to test our hypotheses.

Our empirical results are consistent with the following notions. First, shocks that drive high VOL risk are not necessarily identical to those that drive VOV risk and thus VOL and VOV do not respond to shocks in the same way. These results imply a four‐state VOL system: high/low VOL risk and high/low VOV risk pairs. Second, the minimum inverse correlation between the S&P 500 and VIX is observed under a high VOL–low VOV risk state. In contrast, the maximum inverse correlation between the S&P 500 and VIX occurs under a high VOL–high VOV risk state. Third, the proposed state‐contingent correlations prove more effective in portfolio construction than conventional time‐dependent correlations.

In short, the paper develops a regime-switching model to show that the relationship between the S&P 500 and the VIX varies across distinct volatility (VOL) and volatility-of-volatility (VOV) risk states. It identifies a four-state system and demonstrates that correlation strength depends on the joint VOL–VOV regime.

This is an important contribution, as it quantifies the dynamic relationship between the S&P 500 and the VIX, thereby providing useful guidance for refining hedging and arbitrage strategies.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Leon Li, Carl R. Chen, Volatility Risk and Volatility-of-Volatility Risk: State-Dependent Correlations Between VIX and the S&P 500 Stock Index and Hedging Effectiveness, Journal of Futures Markets, 2025; 1–20

Post Source Here: State-Dependent Correlation Between the S&P 500 and the VIX

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Multifractality and Its Underlying Drivers in Cryptocurrency Markets

Cryptocurrencies, like other financial time series, can be analyzed using traditional time series and econometric methods. However, they present additional challenges due to their distinctive characteristics, including extreme volatility, heavy-tailed distributions, and long-range temporal correlations, which [glossary_exclude]warrant [/glossary_exclude]specialized examination.

Reference [1] analyzes the multifractality characteristics of major cryptocurrencies and investigates their underlying sources.  The authors analyzed data from January 1, 2018, to December 31, 2024, and pointed out,

Taken together, these results reinforce the importance of applying multifractal frameworks to digital markets. They offer practical insights for volatility forecasting, systemic risk monitoring, and understanding the maturation of blockchain-based financial ecosystems. For example, the strong correlations between BTC and ETH on various time scales can be used in optimal portfolio construction [80]. The decay of the autocorrelation function, which indicates the average size of the volatility cluster [37], may also be used in risk management. The consistency of the log-return tail distributions with the inverse cubic power-law allows for the selection of appropriate distributions in various risk mitigation methods, such as Value-at-Risk. Moreover, the greater hierarchical dependence of correlations at the level of larger fluctuations compared to smaller ones, documented by left-sided asymmetry of the multifractal spectra, may potentially allow for extending risk management strategies to include fluctuation-specific correlation patterns. The direct link between multifractality and risk management is an interesting subject for further studies.

Briefly, the article finds that digital asset markets, including BTC, ETH, DEX trading, and NFTs, exhibit clear multifractal scaling, but this multifractality is driven primarily by temporal correlations, not by heavy-tailed return distributions alone.  It also shows strong multifractal cross-correlations between BTC and ETH, especially during large fluctuations. Overall, the study argues that genuine market complexity in crypto stems from persistent correlation structure rather than from fat tails alone.

This study provides important insights into cryptocurrency dynamics, enabling portfolio and risk managers to design more appropriate trading and risk management strategies.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Drozdz, S.; Kluszczynski. R., Kwapien. J.: Watorek. M., Multifractality and its sources in the digital currency market. Future Internet, 2022, 1, 0.

Article Source Here: Multifractality and Its Underlying Drivers in Cryptocurrency Markets

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Improving Momentum Strategies with Machine Learning

Machine learning (ML) is increasingly prominent in modern finance and is being adopted across a wide range of applications. However, using ML to extract alpha remains nontrivial. Reference [1] proposes a novel approach to improving the risk-adjusted returns of momentum strategies using machine learning.

It employs three ML methods—linear regression, random forest, and neural networks—to predict next month’s information coefficients (ICs) using information available at the end of the previous month. These predicted ICs are then combined with momentum factors to construct momentum and reversal strategies.

The authors pointed out,

Machine learning helps improve momentum strategies by capturing strong correlations between stocks and their returns using information coefficients (IC). Shallow machine learning enables us to trace the sources of advantage and adapt to non-linear correlations that traditional methods might overlook. We confirm that a unidirectional momentum strategy can yield significant returns. Furthermore, we observe that implementing a bidirectional approach leads to even more pronounced gains. We find that shorting weak stocks outperforms longing strong stocks, indicating the persistence of the "winners keep winning, losers keep losing" phenomenon. Additionally, we observe that price reversals do not occur within a month. Optimizing the number of stocks in the portfolio leads to the highest excess returns within a certain threshold range. However, implementing inverse trades using machine learning models does not yield positive returns, indicating that the predictive ability of the models may be inadequate to identify profitable opportunities in these situations. Finally, we discover that momentum factors with longer holding periods are more powerful predictors, accurately capturing the correlation between stocks and their returns. We assume that momentum factors with shorter observation periods are less effective, possibly due to the effect of mean reversion. Our research underscores the importance of accurately predicting IC values and the effectiveness of momentum strategies in generating positive returns.

In short, the paper applies machine learning models to momentum investing by predicting future information coefficients and using them to construct momentum and reversal portfolios. It finds that bidirectional momentum strategies outperform unidirectional ones, longer-horizon momentum signals are more informative, and portfolio performance is maximized when the number of selected stocks is optimized within a threshold.

This represents an interesting twist on applying machine learning to investing, using predicted information coefficients to guide portfolio construction rather than directly forecasting returns.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Chang, H.-L., Cheng, H.-W., Lan, Y.-M., & Yu, J.-P. (2025). Machine Learning in Momentum Strategies. EFMA

Post Source Here: Improving Momentum Strategies with Machine Learning

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Portfolio Timing and Allocation with the Variance Risk Premium

The volatility risk premium (VRP) refers to the systematic difference between implied volatility and subsequent realized volatility. Much of the academic literature focuses on developing strategies to harvest the VRP directly. However, Reference [1] departs from this conventional approach by proposing the use of the VRP as a market-timing mechanism.

Specifically, the author computes the VRP for four major index ETFs—SPY, QQQ, IWM, and DIA—and constructs a composite market-level VRP by averaging the normalized VRPs across these assets, yielding a daily indicator bounded between 0 and 1.

In addition, the author incorporates the VIX/VIX3M ratio as a defensive overlay. Based on these two indicators, market conditions are classified into offensive or defensive regimes, within which asset allocation is determined using 100-day moving averages.

The author pointed out,

The above summary highlights the superiority of VOLTEX-G across key investment metrics compared to the SPY. Specifically, there is a notable increase in annualized return from 14.74% to 17.87%, which is accompanied by a significant reduction in volatility from 17.40% to 12.78%, making VOLTEX-G more stable. Additionally, both the Sharpe Ratio and Sortino Ratio show substantial improvement, indicating a higher quality of risk adjusted returns. The Max Drawdown further confirms these advantages with a reduction from 33.72% to 16.34%, underscoring the strategy’s capital protection capability. Moreover, the significant rise in the Recovery Factor demonstrates SPY’s inability to rebound effectively after major drawdowns. Although improvements in other metrics vary in magnitude sometimes more pronounced and sometimes more moderate, the overall picture clearly reflects the superiority of VOLTEX-G.

In short, the results indicate that using the VRP as a timing indicator improves the portfolio’s risk–adjusted return, while the inclusion of technical filters enhances overall stability.

This represents an innovative application of the VRP that [glossary_exclude]warrants [/glossary_exclude]further investigation and experimentation.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Charalampopoulos, A. (2025). Using Variance Risk Premium to Time a Portfolio of Stock and Bond ETFs. University of Piraeus

Originally Published Here: Portfolio Timing and Allocation with the Variance Risk Premium

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Dynamic Delta Hedging with Confidence-Weighted Signals

Delta hedging is a critical component of option portfolio management. In the research literature, most studies assume strict delta hedging, where portfolio delta is maintained at zero. Reference [1] relaxes this restriction by introducing a partial delta hedging technique that conditions the hedge ratio on the confidence of the underlying’s directional prediction.

Specifically, the approach applies a multiplier to the hedge ratio: when delta is positive, and the model anticipates an upward move, the hedge is reduced to retain more exposure, and similarly, when delta is negative, and a decline is predicted, the hedge magnitude is again reduced. The authors pointed out,

We introduces a confidence-based hedge adjustment mechanism that integrates ML forecasts into option portfolio construction. By scaling hedge ratios according to model confidence, the approach moves beyond rigid delta neutrality and captures incremental returns otherwise missed. Empirically, we find that confidence scaling materially affects portfolio risk–return trade-offs: moderate scaling delivers the highest Sharpe ratios, while aggressive scaling yields higher volatility and weaker long-term performance. Our objective is not to identify the most predictive ML model or factor set, but to demonstrate a paradigm shift in hedging design. By relaxing the strict delta-neutral constraint, predictive signals can be more effectively incorporated into option strategies. Future research could extend this framework to multi-asset portfolios, employ more advanced ML models, or explore higher-dimensional feature sets, which may yield even stronger results and further validate the practical relevance of confidence-scaled hedging.

In short, the paper proposes a confidence-based delta hedging framework that dynamically adjusts hedge ratios based on the directional confidence of machine-learning classifiers. Results show that moderate scaling improves Sharpe ratios relative to a benchmark, while aggressive scaling increases volatility and deteriorates long-term performance.

This is a meaningful contribution, as it reinforces the importance of identifying market regimes and dynamically under- or over-hedging according to prevailing conditions.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Li, B., & Wu, C. (2026). Beyond delta neutrality: Confidence-scaled hedging with machine learning forecasts. Finance Research Letters, 87, 109098.

Originally Published Here: Dynamic Delta Hedging with Confidence-Weighted Signals

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Modeling High-Frequency Volatility with Volume-Driven Intraday Effects

Modeling and forecasting volatility is critically important for portfolio construction and risk management. Numerous volatility models exist, and one established line of research incorporates trading volume, motivated by the idea that volume reflects the rate of information flow into prices and is therefore positively related to volatility.

Reference [1] extends this line of research by proposing a model in which volume enters volatility dynamics in a lagged, rather than contemporaneous, manner. Specifically, the paper develops a high-frequency stochastic volatility model that integrates a persistent component, intraday periodicity, and volume-driven time-of-day effects.

The key novelty lies in modeling these volume-driven intraday effects as time-varying through lagged trading activity. The authors pointed out,

This paper develops a framework for modeling high-frequency volatility that jointly accounts for persistent behavior, static time-of-day adjustments, and volume-driven time-of-days effects. By conditioning intraday periodicity on past trading volume, our model captures timely and economically relevant deviations from static volatility patterns, providing a more accurate and microstructure consistent description of intraday risk dynamics.

Across equity indexes, currency, and commodity futures, the volume-driven component explains a large share of intraday volatility variation, highlighting the importance of conditioning on trading activity. The model not only reproduces well-known U-shaped volatility patterns but also uncovers features associated with global trading activity. It indicates that the deviations from the time-of-day average volume are deeply tied to volatility. Our results complement the Sequential Information Arrival Hypothesis (SIAH) literature by showing that the lead–lag relationship between trading volume and volatility also holds in a high-frequency setting…

From an economic perspective, these statistical improvements translate into superior portfolio performance. When applied to volatility-managed investment strategies, our forecasts consistently improve Sharpe ratios relative to unmanaged benchmarks and competing volatility models. The benefits are most striking in equity index futures, where dynamic exposure based on our specification more than triples the Sharpe ratio compared to GARCH or standard stochastic volatility alternatives. Overall, our results suggest that lagged trading volume provides a critical dimension for understanding and forecasting intraday volatility.

In short, the paper develops a high-frequency volatility framework that integrates persistent dynamics, time-of-day effects, and lagged trading volume. The results demonstrate that this volume-driven specification improves volatility forecasts and translates into higher risk-adjusted performance in volatility-managed portfolios, particularly for equity index futures.

This contribution is meaningful. We note that the instruments examined in the paper belong to relatively mature markets. It would be interesting to investigate whether similar effects hold in new, developing markets, such as cryptocurrencies.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Igor Ferreira Batista Martins, Audronè Virbickaitè, Hoang Nguyen and Hedibert Freitas Lopes, Volume-driven time-of-day effects in intraday volatility models, Örebro University School of Business, 2025

Originally Published Here: Modeling High-Frequency Volatility with Volume-Driven Intraday Effects

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Delta Hedging Under Fractional Brownian Motion

The Black–Scholes–Merton (BSM) model is the most frequently used option pricing framework in finance. However, it relies on simplifying assumptions, some of which are not realistic. Ongoing efforts aim to extend and generalize the BSM model, and Reference [1] represents a recent contribution in this direction.

The paper proposes an option pricing model in which the underlying asset follows Fractional Brownian Motion (FBM) rather than Geometric Brownian Motion. It not only derives pricing formulas for call and put options, but also introduces a hedging strategy based on FBM. Specifically, the strategy consists of buying a call option and dynamically delta-hedging it using deltas computed from the proposed fractional model.

The authors pointed out,

This study provides a comprehensive empirical evaluation of dynamic delta hedging strategies under fBm compared to standard Bm, focusing on two major financial crises: the 2007–2008 global financial crisis and the COVID-19 recession. While Bm assumes memoryless price movements, fBm incorporates long-range dependence, making it a potentially more robust framework for modeling asset dynamics during turbulent market conditions.

Using historical data from the S&P 500 and NYSE indices, the research estimates the Hurst exponent to calibrate fBm models and assess their predictive and hedging performance. The findings reveal that fBm generally improves prediction accuracy and significantly enhances hedging efficiency, especially when the Hurst exponent is close to or above 0.5. Notably, fBm-based strategies reduce downside risk, eliminate negative P&L outliers, and offer more consistent returns during and after crisis periods.

However, the study also uncovers that when the Hurst exponent falls below 0.5-as observed in the NYSE post-COVID—fBm may introduce greater volatility and tail risk, underscoring the importance of careful calibration. A key insight is the distinction between forecasting accuracy and hedging effectiveness: even when predictive gains are marginal, fBm’s memory-sensitive structure can lead to superior risk mitigation.

In short, the paper finds that FBM-based hedging improves prediction accuracy and, more importantly, enhances hedging efficiency and downside risk control when the underlying asset is trending (Hurst exponent is near or above 0.5), but volatility and tail risk may increase when the asset exhibits mean-reverting behavior (H < 0.5).

The results are somewhat counterintuitive. In practice, delta hedging is generally understood to add value primarily in mean-reverting regimes, yet the study suggests that delta hedging is effective when the underlying asset exhibits trending behavior.

We also note that the analysis is conducted using simulated option prices. It would be useful to evaluate this hedging strategy using real, empirical data. Nonetheless, this is an important study and [glossary_exclude]warrants [/glossary_exclude]further investigation.

Let us know what you think in the comments below or in the discussion forum.

References

[1]  Dufera, T.T., Kesto, D.A. & Legesse, T.S. Dynamic Delta Hedging During Crises: Fractional Brownian Motion in Action. Comput Econ (2025).

Post Source Here: Delta Hedging Under Fractional Brownian Motion

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When More Information Hurts: Social Media and Investor Underperformance

In today’s digital era, social media is ubiquitous, and market participants are exposed to an unprecedented volume of financial information. While investors are arguably more informed, it remains an open question whether this translates into better and more disciplined decision-making.

A recent incident illustrates this concern: a group of online traders, including both professionals and retail investors, followed an online influencer known as “Captain Condor” and collectively lost approximately USD 50 million around Christmas, with many losing their entire life savings; compounding these losses, members also paid annual fees exceeding USD 5,000 to gain access to the social media guru’s group.

This episode again raises a fundamental question: does more information necessarily lead to better judgment, and how is social media reshaping the investment landscape?

Reference [1] examines this issue formally, using StockTwits data from 2013 to 2022 to analyze the impact of social media activity around earnings announcements.

The authors pointed out,

Focusing on earnings announcements, I find that social media information is overly optimistic and fails to predict fundamentals. This social media attention is associated with systematic noise trading, specifically retail buying pressure from retail investors in the equity market, which generates a 58 bps price drift ahead of earnings announcements. Such systematic noise trading worsens price revelation, undermining price informativeness ahead of earnings announcements…

Overall, these findings reveal that social media-driven optimism can lead retail investors to trade systematically in the equity and options market, leading to reduced price efficiency and potentially harming their wealth.

In short, the paper finds that social media information around earnings announcements is overly optimistic and fails to predict fundamentals. This behavior worsens price discovery, reduces price efficiency in both equity and options markets, and can ultimately harm retail investors’ wealth.

In conclusion, increased information flow does not automatically result in better decision-making, underscoring the importance of critical thinking, judgment, and disciplined analysis in the presence of abundant information.

Let us know what you think in the comments below or in the discussion forum.

References

[1]  Edna Lopez Avila, Charles Martineau, and Jordi Mondria, Social Media and the Distortion of Price Revelation, SSRN 4439793, 2025

Post Source Here: When More Information Hurts: Social Media and Investor Underperformance

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