Month: June 2025

Gold Ratios as Stock Market Predictors

The ratio of gold prices to other asset classes has been shown to be a useful predictor of stock market returns. In this post, we discussed several gold-based ratios and how they can be used to forecast equity market performance.

Gold Oil Price Ratio As a Predictor of Stock Market Returns

Analyzing intermarket relationships between assets can help identify trends and predict returns. Traditionally, analysts use commodity, currency, and interest-rate data to predict the direction of the stock market. In this regard, Reference [1] brings a fresh new perspective. It utilizes price ratios of gold over other assets in order to forecast stock market returns.

Findings

-The gold-oil price ratio (GO) is shown to be a strong predictor of future stock market returns.

-Researchers created ten different gold price ratios by comparing gold to various assets like oil, silver, CPI, corn, copper, and several financial indicators.

-They used statistical models (univariate and bivariate regressions) to test how well these ratios could predict U.S. stock returns.

-Among all the ratios tested, the gold-oil ratio (GO) had the highest predictive power.

-A one standard deviation increase in the GO ratio is linked to a 6.60% rise in annual excess stock returns for the following month.

-The GO ratio performs better than traditional forecasting methods, including the historical average model.

-It also offers meaningful economic benefits for investors who use mean-variance strategies.

-The study concludes that the predictive ability of the GO ratio is both statistically reliable and economically useful.

In summary, the gold-oil price ratio is identified as a robust predictor of stock market returns, outperforming traditional predictors and other gold price ratios. A one standard deviation increase in GO is associated with a significant 6.60% increase in annual excess returns for the next month.

Reference

[1] T. Fang, Z. Su, and L Yin, Gold price ratios and aggregate stock returns, SSRN 3950940

The Bitcoin-Gold Ratio as a Predictor of Stock Market Returns

The ratio of gold prices to other asset classes has been shown to be a useful predictor of stock market returns. The previous article discussed how the gold-oil ratio serves as one such indicator.

Continuing this line of inquiry, Reference [2] examines the informational value of the Bitcoin-gold (BG) price ratio. The logic behind this metric is that Bitcoin represents a high-risk asset, whereas gold is traditionally viewed as a safe haven. Therefore, a rising BG ratio may signal increased investor risk appetite. It may also reflect growing optimism and interest in technological innovation, which boosts demand for Bitcoin. As a result, a higher BG ratio can indicate a tech-driven risk appetite that translates into stronger stock returns.

Findings

-The Bitcoin-Gold (BG) ratio is positively linked to U.S. stock market returns, especially during and after the COVID-19 pandemic.

-A rising BG ratio suggests increased investor risk appetite, as Bitcoin is seen as high-risk and gold as a safe haven.

-The effect of the BG ratio on stock returns remains strong even when using Ethereum instead of Bitcoin, showing broader crypto-gold relevance.

-The positive impact of the BG ratio also applies to the European stock market, not just the U.S., indicating global relevance.

-The main channel through which the BG ratio affects stock returns is investor risk aversion or appetite.

-The study uses various economic controls, like volatility, inflation, and liquidity, and still finds the results hold strong.

-There was no significant impact of the BG ratio on stock returns before the pandemic, suggesting this relationship is more recent.

-The BG ratio reflects shifts in market sentiment and offers a new tool for gauging investor behavior.

-Investors can use the BG ratio as a signal to adjust their equity exposure based on prevailing market conditions.

In summary, the paper makes a novel contribution by introducing crypto-gold ratios as reliable indicators of stock market direction across multiple regions.

Reference

[2] Elie Bouri, Ender Demir, Bitcoin-to-gold ratio and stock market returns, Finance Research Letters (2025) 107456

Closing Thoughts

Both studies show that gold price ratios can offer valuable insights into stock market returns. The gold-oil ratio (GO) stands out as a strong, traditional predictor, while the Bitcoin-gold ratio (BG) brings a modern twist by capturing shifts in investor risk appetite. Together, these findings suggest that combining safe-haven and risk assets in a ratio form can help investors better understand and respond to changing market conditions.

Volatility of Volatility: Insights from VVIX

The volatility of volatility index, VVIX, is a measure of the expected volatility of the VIX index itself. In this post, we will discuss its dynamics, compare it with the VIX index, and explore how it can be used to characterize market regimes.

Dynamics of the Volatility of Volatility Index, VVIX

The VVIX, also known as the Volatility of Volatility Index, is a measure that tracks the expected volatility of the CBOE Volatility Index (VIX). As the VIX reflects market participants’ expectations for future volatility in the S&P 500 index, the VVIX provides insights into the market’s perception of volatility uncertainty in the VIX itself.

Reference [1] studied the dynamics of VVIX and compared it to the VIX.

Findings

-The VVIX tracks the expected volatility of the VIX, providing a direct measure of uncertainty around future changes in market volatility itself.

-It shows strong mean-reverting behavior, indicating that large deviations from its average level tend to reverse over time.

-The VVIX responds asymmetrically to S&P 500 movements, typically increasing more sharply during market downturns than it decreases during upswings.

-It experiences sudden jumps in both directions, reflecting its sensitivity to abrupt changes in market sentiment and conditions.

-A persistent upward trend in the VVIX began well before 2020, driven by factors such as rising VIX volatility and an increasing volatility-of-volatility risk premium (VVRP).

-The growth of the VIX options market from 2006 to 2014 improved liquidity, which likely contributed to the VVIX’s upward trend and closer link to the VIX.

-VVIX and VIX innovations are highly correlated, highlighting their structural connection despite often differing in their responses to specific market events.

-VVIX quickly incorporates new market information, with minimal autocorrelation beyond a single day, showing its responsiveness to real-time market changes.

In summary, this paper analyzes the similarities and differences between the VIX and VVIX, offering key insights for traders and hedgers in the VIX options market. Understanding their relationship helps improve risk management, refine hedging strategies, and better assess market sentiment.

Reference

[1]  Stefan Albers, The fear of fear in the US stock market: Changing characteristics of the VVIX, Finance Research Letters, 55

Using Hurst Exponent on the Volatility of Volatility Indices

A market regime refers to a distinct phase or state in financial markets characterized by certain prevailing conditions and dynamics. Two common market regimes are mean-reverting and trending regimes. In a mean-reverting regime, prices tend to fluctuate around a long-term average, with deviations from the mean eventually reverting back to the average. In a trending regime, prices exhibit persistent directional movements, either upwards or downwards, indicating a clear trend.

Reference [2] proposed the use of the Hurst exponent on the volatility of volatility indices in order to characterize the market regime.

Findings

-The study analyzes the volatility of volatility indices using data from five international markets—VIX, VXN, VXD, VHSI, and KSVKOSPI—covering the period from January 2001 to December 2021.

-It employs the Hurst exponent to evaluate long-term memory and persistence in volatility behavior, providing a framework to characterize market regimes over time.

-Different range-based estimators were used to calculate the Hurst exponent on various volatility measures, improving the robustness of the analysis.

-The volatility of volatility indices was estimated through a GARCH(1,1) model, which captures time-varying volatility dynamics effectively.

-The results show that Hurst exponent values derived from volatility of volatility indices reflect market regime shifts more accurately than those from standard volatility indices, supporting the authors’ hypothesis (H1).

-The analysis explores how different trading strategies—momentum, mean-reversion, and random walk—align with the Hurst exponent values, linking theoretical behavior to practical trading outcomes.

-The study highlights the effectiveness of the Hurst exponent as a tool for identifying and interpreting market regimes, which is essential for informed trading and investment decisions.

-Findings are particularly useful for financial analysts and researchers working with volatility indices and market behavior analysis.

-The paper contributes a novel methodological approach by combining Hurst exponent estimation with GARCH modeling and strategy backtesting, offering a comprehensive view of volatility behavior across regimes.

In short, the article highlights the effectiveness of employing the Hurst exponent on the volatility of volatility indices as a suitable method for characterizing the market regime.

Reference

[2] Georgia Zournatzidou and Christos Floros, Hurst Exponent Analysis: Evidence from Volatility Indices and the Volatility of Volatility Indices, J. Risk Financial Manag. 2023, 16(5), 272

Closing Thoughts

In this post, we explored the dynamics of the VVIX index, and how to use the Hurst exponent on it to characterize the market regime, offering a practical lens through which traders can gauge the persistence or randomness in volatility movements. By understanding these dynamics, market participants can better anticipate shifts in sentiment, enhance their hedging strategies, and adapt more effectively to evolving risk conditions in the options market.