Month: May 2025

The Calendar Effects in Volatility Risk Premium

I recently covered calendar anomalies in the stock markets. Interestingly, patterns over time also appear in the volatility space. In this post, I’ll discuss the seasonality of volatility risk premium (VRP) in more detail.

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 active 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.

An interesting finding of the paper 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.

Findings

-The analysis is conducted on implied variance stock indices across the US, Europe, and Asia.

-Results show that the VRP switches signs between overnight and intraday periods—negative overnight and positive intraday.

-The findings suggest that the negative VRP observed in previous studies is primarily driven by the overnight component.

-The study evaluates the predictive power of both intraday and overnight VRP in forecasting future equity returns.

-The intraday VRP component captures short-term risk and demonstrates predictive ability over 1–3-month horizons.

-The overnight VRP component reflects longer-term risk and shows predictive power over 6–12-month horizons.

Reference

[1] Papagelis, Lucas and Dotsis, George, The Variance Risk Premium Over Trading and Non-Trading Periods (2024), SSRN 4954623

Volatility Risk Premium Seasonality Across Calendar Months

Reference [2] examines the VRP in terms of months of the year. It concluded that the VRP is greatest in December and smallest in October.

An explanation for the large VRP in December is that during the holiday season, firms might refrain from releasing material information, leading to low trading volumes. The combination of low trading volume and the absence of important news releases would result in lower realized volatility.

Findings

-The paper identifies a “December effect” in option returns, where delta-hedged returns on stock and S&P 500 index options are significantly lower in December than in other months.

-This effect is attributed to investors overvaluing options at the start of December due to underestimating the typically low volatility that occurs in the second half of the month.

– The reduced volatility is linked to lighter stock trading during the Christmas holiday season.

– A trading strategy that involves shorting straddles at the beginning of December and closing the position at the end of the month yields a hedged return of 13.09%, with a t-value of 6.70.

-This return is much higher than the unconditional sample mean of 0.88%, highlighting the strength of the effect.

The paper is the first in academic literature to document and analyze this specific December anomaly in option markets. It is another important contribution to the understanding of the VRP.

Reference

[2] Wei, Jason and Choy, Siu Kai and Zhang, Huiping, December Effect in Option Returns (2025). SSRN 5121679

Closing Thoughts

In this post, I have discussed volatility patterns in terms of both days of the week and months of the year. Understanding this seasonality is crucial for traders and portfolio managers, as it can inform better timing of volatility trades and risk management strategies.

Stock-Bond Correlation: What Drives It and How to Predict It

The correlation between stocks and bonds plays a crucial role in portfolio allocation and diversification strategies. In this issue, I discuss stock-bond relationships, the factors that influence their correlation, and techniques for forecasting it.

What Influences Stock-Bond Correlation?

Correlation between stocks and bonds is crucial for portfolio allocation and diversification, but this correlation can vary over time due to factors like inflation and real returns on short-term bonds.

Reference [1] conducts a study on stock-bond correlation spanning an extended timeframe. Their findings indicate that contrary to conventional assumptions, stock-bond correlation generally tends to exhibit a positive or near-zero relationship. Exceptions, where the correlation drops below -0.2, were notably observed during the early 1930s, the late 1950s, and most of the 2000s.

Findings

-The correlation between stock and bond returns is a key component in asset allocation decisions. This correlation is not stable and can vary significantly over time, affecting how portfolios should be constructed.

– The recent market environment has shown that stock-bond correlation can turn positive, potentially impacting diversified portfolios negatively.

– The article suggests that contrary to conventional assumptions, stock-bond correlation generally tends to be positive or near-zero.

– Exceptions to positive correlation occurred during the early 1930s, late 1950s, and most of the 2000s.

– Factors such as inflation, real returns on short-term bonds, and uncertainty surrounding inflation play pivotal roles in determining the direction and strength of stock-bond correlation.

– Time variation in stock and bond volatility can also affect the impact of stock-bond correlation.

– Bond risk premia are positively correlated with estimates of the stock-bond correlation.

– The correlation between stocks and bonds can significantly fluctuate over time and across countries.

In short, the correlation between stocks and bonds can significantly fluctuate over time. Factors such as inflation and real returns on short-term bonds, along with the associated uncertainty regarding inflation, play pivotal roles in determining both the direction and strength of the stock-bond correlation.

Reference

[1] Molenaar, Roderick and Senechal, Edouard and Swinkels, Laurens and Wang, Zhenping, Empirical evidence on the stock-bond correlation (2023), SSRN 4514947

Forecasting Short-Term Stock-Bond Correlation

Reference [2] employs a country’s Correlation Outlook, Prospective Inflation Volatility, the Yield Curve Momentum Regime, and the Trailing 3-month stock-bond correlation to build a predictive model.

Findings

-This paper extends a macroeconomic framework that explains long-term changes in stock-bond correlation.

-Prior research explains around 70% of the variation in 10-year rolling stock-bond correlations using the relative volatility and correlation of growth and inflation.

-The authors shift focus to forecasting short-term (three-month) variations in stock-bond correlation.

-Their method uses indicators based on whether individual forecasters expect stock and bond markets to move in the same or opposite directions.

– This approach improves the ability to forecast stock-bond correlations over tactical, short-term horizons.

This paper complements previous work by focusing on short-term horizons, showing that detailed forecast data can help predict high-frequency changes in stock-bond correlation. It also highlights the value of granular forecast data, especially the correlation between responses, which may be missed in standard survey summaries.

Reference

[2] Flannery, Garth and Bergstresser, Daniel, A Changing Stock-Bond Correlation: Explaining Short-term Fluctuations (2023). SSRN 4672744

Closing Thoughts

As we have seen, stock-bond correlation plays a crucial role in portfolio management and asset allocation. We have discussed how this correlation shifts over time, influenced by macroeconomic factors such as inflation and growth volatility, and how it can be forecasted. Accurately anticipating these shifts enables more informed portfolio construction and risk management.