Newsletter: Volatility Risk Premium Across Different Asset Classes

Evidence from a Multi-Market Empirical Study

The volatility risk premium has been studied extensively in the equity space, but less so in other asset classes. In this issue, we are going to examine the VRP across different asset classes.

Web-only posts Recap

Below is a summary of the web-only posts I published during last week.

Volatility Term Structures of Individual Stocks

Fixed and Trailing Stop Losses in the Commodity Market

Bilateral Credit Value Adjustment With Default Correlation

The Effectiveness of Dollar Cost Averaging Under Varying Market Conditions

Statistical Arbitrage in the Crude Oil Markets

Factor Model for Delta-Hedged Options Returns

Upcoming Conference

The CBOE Quant Conference is a gathering of thought leaders from academia and industry to examine the future of quantitative finance. This year, they feature fantastic speakers such as Jim Gatheral and Julien Guyon. If you’re a member of an academic institution, you can receive an 80% discount. Email CboeRMC@cboe.com for the discount code. For the full agenda, follow the link below:

RMC Quant Conference 2025

Volatility Risk Premium Across Different Asset Classes

The volatility risk premium (VRP) is the compensation investors receive for bearing the risk associated with fluctuations in market volatility, typically measured as the difference between implied and realized volatility. The VRP in equities has been studied extensively. However, relatively little attention has been paid to the VRP in other asset classes.

Reference [1] examined the VRP in different asset classes. It specifically studied the VRP in 18 different underlyings belonging to the commodity, fixed-income, and equity asset classes.

Findings

-The paper analyzes the use of the volatility risk premium (VRP) for volatility forecasting across 18 distinct markets in two time periods.

-The study introduces RIV models, which adjust current implied volatility for the VRP, and finds that these models produce significantly more accurate forecasts compared to other approaches.

-Multiple methodologies for deriving RIV models are examined, with their strengths and limitations evaluated.

-The findings are consistent across most of the assets analyzed and are supported by various loss functions and statistical tests, reinforcing their robustness.

-The newly introduced RIV models outperform implied volatility (IV) and GARCH-based forecasts in predictive accuracy.

-The study finds that the VRP is generally positive across most markets.

-A link is identified between the magnitude of VRP and the trading volume of underlying futures, with higher volumes associated with positive VRP.

-Negative VRP is observed in a few low-volume markets, suggesting that insufficient depth in these markets prevents efficient option pricing.

In short, the VRP is positive in most markets and is positively correlated with trading volume. Additionally, the VRP can be used to predict future realized volatility.

This is an interesting look at the VRP in different markets. We note, however, that just because the VRP is positive in a given market, it does not necessarily mean that P&L can be easily extracted without taking on too much risk. To earn a respectable risk-adjusted return in a given market, a sophisticated system must be developed.

Reference

[1] Štěpán Havel, Volatility Risk Premium Across Multiple Asset Classes, Charles University, 2024

Illiquidity Premium in the Bitcoin Options Market

The previous article explored the VRP across asset classes, primarily commodities. Reference [2] examines the illiquidity risk premium in the crypto market, which is indirectly related to the VRP. Specifically, it studies the role of liquidity risks in the returns of bitcoin options.

In the bitcoin options market, market makers face significant challenges in hedging inventory risk due to price jump risks and lower liquidity. As a result, they charge a higher risk premium.

Findings

-The paper examines the economic drivers of illiquidity in cryptocurrency options markets and their impact on option returns.

-It uses transaction-level data for Bitcoin (BTC) options on Deribit from January 2020 to July 2024 to compute intraday measures of option illiquidity.

-The results show that when market makers hold net-long positions, they demand a positive illiquidity premium to offset hedging and rebalancing costs.

-A one standard deviation increase in option illiquidity raises daily delta-hedged returns by about 0.07% for calls and 0.06% for puts.

-A factor model based on latent instruments derived from option characteristics confirms that illiquidity is a distinct pricing factor in the cross-section of option returns.

-The Bitcoin options market remains illiquid, with this structure leading to a significant illiquidity premium where higher illiquidity predicts higher subsequent returns.

-Investors on average tend to sell options, though the net sell imbalance has declined with increased participation from small retail investors.

-Both panel OLS and IPCA factor models show a robust and positive relationship between illiquidity and expected option returns, consistent across different proxies and model specifications.

-The illiquidity premium compensates market makers for risks and costs associated with delta-hedging, rebalancing, and inventory management.

-Regression analyses indicate that relative spreads are driven by hedging costs, inventory costs, and asymmetric information, and remain an important determinant of expected returns, especially for options with negative order imbalances.

In short, Bitcoin options market makers and active traders earn excess returns, partly driven by the illiquidity premium.

Reference

[2] C Atanasova, T Miao, I Segarra, TT Sha, F Willeboordse, Illiquidity Premium and Crypto Option Returns, Working paper, 2024

Closing Thoughts

Together, these studies expand the understanding of risk premia beyond traditional equity markets. While the first paper demonstrates the existence of the VRP across asset classes, the second highlights the presence of an illiquidity risk premium in cryptocurrency options, reflecting unique market frictions. For traders and researchers alike, the results underscore the importance of adapting models and expectations to the characteristics of each market.

Educational Video

Virtual Barrels Quant Talk, Episode 1: Who is Who in Oil Derivatives?

Dr. Ilia Bouchouev is a quant trader specializing in oil derivatives. He has conducted research on the volatility risk premium in the crude oil market. Part of this work was published in his book Virtual Barrels, while newer results will be published in upcoming papers. He recently started a YouTube channel, which is very educational. As of this writing, he has not discussed the VRP yet, but it is likely he will cover it in the future, so check back. Below is a general introduction to oil quant trading.

Around the Quantosphere

-Intel investors snap up bullish options to chase furious rally (bloomberg)

-94% Beta: The hedge fund performance illusion (delano)

-Quantum bond trading (bloomberg)

-Qube human stockpickers trading in the fourth quarter (businessinsider)

-Risk Awards 2025: The winners (risk)

-Quantum leap in finance: HSBC unveils breakthrough in bond price prediction (financialcontent)

- US Stocks May Surge Another 10% as ‘Powerful Forces’ Drive Rally (yahoo)

- How to Get a Job at Millennium, the Hedge Fund Paying $100m (efinancialcareers)

-Popular hedge fund options strategy attracts contrarian bets (tradealgo)

-Rates ‘Volmageddon’ hits Wall Street’s tail hedges (bloomberg)

-Quant trades popularized by Ray Dalio bounce back with 19% gain (bloomberg)

Recent Newsletters

Below is a summary of the weekly newsletters I sent out recently

-When Trading Systems Break Down: Causes of Decay and Stop Criteria (12 min)

-Volatility Targeting Across Asset Pricing Factors and Industry Portfolios (12 min)

-Tail Risk Hedging Using Option Signals and Bond ETFs (12 min)

-Stochastic Volatility Models for Capturing ETF Dynamics and Option Term Structures (11 min)

-Cross-Sectional Momentum: Results from Commodities and Equities (11 min)

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