Volatility Risk Premium and Clustering: Intraday vs Overnight Dynamics

Decomposing risk: volatility, premium, and clustering in intraday vs overnight returns

Author

Nam Nguyen
April 19, 2026 • Estimated Reading Time: 4 minutes

The decomposition of risks and returns into overnight and intraday components is an emerging area of research. In this edition, 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.

In this issue:

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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.

Additional Reading

For further discussion on overnight vs. intraday risks, please refer to previous issues:

Educational Video

Quantopian: How Nocturnal Trading is Reshaping Wall Street

In this podcast, the discussion focuses on the rapid rise of nocturnal trading, defined as trading U.S. equities outside regular hours, and the structural forces driving this shift. The speakers explain that increased retail access and global participation, particularly from Asia, are key drivers, supported by alternative trading systems and major brokerage platforms. Despite concerns around lower liquidity and execution quality, the data show that while quoted spreads are wider, realized outcomes for liquid securities can be comparable or even better, indicating that the market is adapting to extended trading hours.

The podcast also highlights important implications for market dynamics, particularly price discovery and volatility. Although overnight trading represents a small fraction of total volume, it contributes disproportionately to price formation, with a significant portion of overnight price changes persisting into the next trading day. This suggests that global information is increasingly incorporated into prices outside traditional hours, especially for ETFs. At the same time, higher price impact and order imbalances point to potential risks, including herding behavior and reduced liquidity, reinforcing that overnight trading introduces both new opportunities and new challenges for market participants.

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