Crypto Markets

DCC-GARCH: Correlation Between Crypto and Stocks Explained

Jitender Garg
By Jitender Garg Contributor
Reviewed By Guillermo Jimenez Editor-in-Chief
· 4 min read · 758 words
DCC-GARCH: Correlation Between Crypto and Stocks Explained
Quick Summary
  • DCC-GARCH separates volatility estimation from correlation estimation, allowing the correlation between two assets to evolve dynamically over time rather than remaining constant
  • Research using DCC-GARCH found Bitcoin's correlation with the S&P 500 increased significantly after the January 2024 spot Bitcoin ETF approval, indicating stronger equity market integration
  • Over the same post-ETF period, Bitcoin's correlation with gold stabilized near zero, while its correlation with the US Dollar Index remained consistently negative
  • Studies examining the COVID-19 period using DCC-GARCH found statistically significant correlations between Bitcoin and stock market returns across several countries, with volatility influenced by historical stock market performance
  • DCC-GARCH studies comparing cryptocurrencies across different fiat-denominated pairs (such as BTC-USD versus BTC-KRW) have found extremely high correlations, often above 0.95, for the same underlying asset traded in different currencies
  • Some research has found that Bitcoin offered better diversification benefits relative to gold and major stock indices specifically during the COVID-19 period, despite rising stock-crypto correlation more broadly
  • DCC-GARCH is most useful for evaluating whether crypto still provides genuine diversification value in a portfolio, since static historical correlation figures can be misleading if the relationship has shifted

DCC-GARCH (Dynamic Conditional Correlation GARCH) is a statistical model that estimates how the correlation between two or more assets, such as Bitcoin and the S&P 500, changes over time, rather than assuming it stays fixed. Research using DCC-GARCH has found that Bitcoin’s correlation with the S&P 500 increased significantly following the approval of spot Bitcoin ETFs in January 2024, signaling stronger integration with traditional equity markets, while Bitcoin’s relationship with gold stabilized near zero over the same period. This guide explains how DCC-GARCH works, what current research shows about the crypto-stock relationship, and what this means for portfolio diversification.

What DCC-GARCH Solves That Simple Correlation Cannot

A simple historical correlation coefficient between two assets, calculated over a fixed sample period, only tells you the average relationship across that entire window. It cannot tell you whether that relationship has been stable throughout the period, or whether it has shifted meaningfully, for example becoming much stronger after a specific structural event.

DCC-GARCH, developed primarily through work by Robert Engle, addresses this limitation directly. The model works in two stages: first, it estimates univariate GARCH volatility for each individual asset in the pair. Second, it uses the standardized residuals from those univariate models to estimate a time-varying correlation matrix, allowing the correlation between the two assets to move period by period rather than being fixed at a single historical average.

This two-step structure is computationally more tractable than fully multivariate alternatives like BEKK-GARCH for larger systems of assets, which is part of why DCC-GARCH has become one of the most widely used tools for studying how the crypto-stock relationship has evolved.

How Bitcoin’s Correlation with Stocks Has Changed

One of the most significant recent findings in this area concerns how the approval of spot Bitcoin ETFs reshaped Bitcoin’s relationship with traditional markets.

A study examining Bitcoin’s hedging properties using DCC-GARCH found that Bitcoin’s correlation with the S&P 500 increased significantly following the January 2024 spot Bitcoin ETF approval, a finding the researchers attributed to increased institutional adoption and Bitcoin’s deeper integration into traditional finance. The same study found that Bitcoin’s relationship with gold stabilized near zero over this period, while its correlation with the US Dollar Index (DXY) remained consistently negative throughout.

Asset Pair Correlation Trend (Post-2024 ETF Approval)
Bitcoin vs. S&P 500 Increased significantly
Bitcoin vs. Gold Stabilized near zero
Bitcoin vs. US Dollar Index (DXY) Remained consistently negative

What COVID-19 Era Research Found

Several DCC-GARCH studies examined the crypto-stock relationship specifically during the COVID-19 pandemic and the subsequent period of US monetary policy normalization.

One study using a Dynamic Conditional Correlation Multivariate GARCH model, applying a GARCH(1,1) specification, analyzed the relationship between Bitcoin and major stock market indices across several countries during this period, finding statistically significant correlations in multiple countries.

A separate line of research using artificial neural networks to compare Bitcoin, gold, and major US stock indices before and during COVID-19 found substantial dynamic conditional correlation, but with an important nuance: despite this correlation, Bitcoin offered better diversification opportunities to mitigate risk in key stock markets during the COVID-19 period specifically.

DCC-GARCH for Crypto-to-Crypto and Cross-Currency Pairs

While much research attention focuses on crypto-stock correlation, DCC-GARCH has also been applied extensively to study correlation between cryptocurrencies themselves, and between the same cryptocurrency denominated in different fiat currencies.

A 2026 study examining cryptocurrency market efficiency across South Korea, the United States and Japan found that the average dynamic conditional correlation between BTC-KRW and BTC-USD was approximately 0.9624, while the correlation between BTC-USD and BTC-JPY was approximately 0.9791, with both pairs rarely falling below 0.95.

Separately, research applying a DCC-GJR-GARCH model to high-frequency 15-minute data across five major cryptocurrencies found that uncertainty and volatility spread most significantly between Bitcoin and Ethereum specifically, while a portfolio strategy informed by the model achieved the lowest risk among the strategies tested.

Why Dynamic Correlation Matters More Than Static Correlation for Diversification

The practical value of DCC-GARCH for an investor or portfolio manager lies in answering a specific question that static historical correlation cannot: is the diversification benefit relied upon still present today, or has the relationship between assets shifted? Static, long-run historical correlation figures can understate how connected crypto and equities have become recently.

Who Should Use DCC-GARCH Analysis?

Profile Relevance
Risk managers assessing systemic crypto-market linkages High. Tracks whether crypto’s connection to traditional finance is strengthening over time
Academic researchers studying market integration High. Standard methodology for time-varying correlation analysis in finance literature
Long-term retail crypto holders Moderate. Useful context for understanding how crypto’s role in a broader portfolio may be shifting

 

Final Verdict

Our Take

DCC-GARCH has become one of the most important tools for understanding how the relationship between cryptocurrency and traditional stock markets has evolved, particularly following structurally significant events such as the 2024 spot Bitcoin ETF approval. The consistent finding across recent research is that Bitcoin’s correlation with major stock indices has increased over time, reflecting deeper integration with traditional finance, while its relationship with gold has weakened, challenging the long-standing “digital gold” framing.

For portfolio construction, the key takeaway is that static, long-run historical correlation figures can understate how connected crypto and equities have become recently. Dynamic, time-varying correlation models like DCC-GARCH provide a more current and actionable picture, though investors should remain aware that correlation patterns, including the diversification benefits they imply, can shift again as the structural relationship between these markets continues to develop.

This article is for informational and educational purposes only and does not constitute financial or investment advice. Quantitative models such as DCC-GARCH carry inherent statistical limitations and should be interpreted alongside other forms of market analysis. Past correlation patterns do not guarantee future relationships between assets.

FAQ

Frequently Asked Questions

DCC-GARCH is used to estimate how the correlation between two or more financial assets changes over time, rather than assuming it remains fixed. It is widely applied to study the evolving relationship between cryptocurrencies and traditional assets such as stocks, gold and the US Dollar Index.
Research using DCC-GARCH found that Bitcoin's correlation with the S&P 500 increased significantly following the January 2024 approval of spot Bitcoin ETFs, a finding attributed to growing institutional adoption and Bitcoin's increasing integration into traditional financial markets.
This depends on the magnitude and timing of the correlation. Some COVID-19 era research found that despite showing meaningful correlation with stock markets, Bitcoin still offered better diversification opportunities than some traditional alternatives during that specific crisis period. Rising average correlation does not automatically eliminate diversification value, but it does reduce it relative to a lower-correlation baseline.
One study found that Bitcoin's correlation with gold stabilized near zero following the 2024 spot Bitcoin ETF approval, suggesting a decoupling from gold's traditional role, which differs from earlier periods when some research observed brief positive correlation between the two assets.
Research has found correlations often above 0.95 between the same cryptocurrency priced in different fiat currencies, such as BTC-USD and BTC-KRW. This is expected since both pairs are fundamentally driven by Bitcoin's own underlying price movement, with currency-specific factors playing a comparatively minor role.
DCC-GARCH separates volatility and correlation estimation into two sequential steps, making it more computationally tractable for larger asset groups. BEKK-GARCH jointly estimates the full variance-covariance matrix in one step, which can capture spillover dynamics more comprehensively but becomes difficult to estimate reliably as the number of assets grows.
DCC-GARCH is primarily a descriptive and forecasting tool for short-term correlation dynamics based on recent historical patterns. It is not designed to predict structural shifts caused by unprecedented events, such as a major new regulatory change, before those events occur.
Jitender Garg
Written by Jitender Garg Contributor

Jitender Garg is a content writer and SEO professional with experience in digital marketing and online publishing. He covers finance, cryptocurrency, forex, and market trends, focusing on creating clear, accurate, and easy-to-understand content for readers.

Reviewed by Guillermo Jimenez Editor-in-Chief

Guillermo Jimenez is the Editor-in-Chief of your website. He is based in Dubai, United Arab Emirates, and has worked as a writer, editor, and content producer across finance and digital media platforms. He oversees editorial quality, ensures accuracy of financial content, and guides the publication’s content strategy. Disclosure: No significant crypto or financial holdings.

Disclaimer: This article is for informational and educational purposes only. It does not constitute financial, investment, legal, or tax advice. Always conduct your own research (DYOR) and consult a qualified financial advisor before making investment decisions. Cryptocurrency, gold and forex carry significant risk of loss.