Interactive historical chart showing the daily level of the CBOE VIX Volatility Index back to 1990. The VIX index measures the expectation of stock market volatility over the next 30 days implied by S&P 500 index options.

Graph and download economic data for CBOE Volatility Index: VIX (VIXCLS) from 1990-01-02 to 2025-06-27 about VIX, volatility, stock market, and USA.

Graph and download economic data for CBOE S&P 500 3-Month Volatility Index (VXVCLS) from 2007-12-04 to 2025-06-27 about VIX, volatility, 3-month, stock market, and USA.

United States - CBOE Volatility : VIX was 16.59000 Index in June of 2025, according to the United States Federal Reserve. Historically, United States - CBOE Volatility : VIX reached a record high of 82.69000 in March of 2020 and a record low of 9.14000 in November of 2017. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - CBOE Volatility : VIX - last updated from the United States Federal Reserve on June of 2025.

Graph and download economic data for CBOE DJIA Volatility Index (VXDCLS) from 1997-10-07 to 2025-06-26 about VIX, volatility, stock market, and USA.

Graph and download economic data for CBOE NASDAQ 100 Volatility Index (VXNCLS) from 2001-02-02 to 2025-06-27 about VIX, volatility, stock market, and USA.

National Stock Exchange of India Limited: Index: India VIX Index data was reported at 16.890 NA in 15 May 2025. This records a decrease from the previous number of 17.230 NA for 14 May 2025. National Stock Exchange of India Limited: Index: India VIX Index data is updated daily, averaging 15.989 NA from Jan 2012 (Median) to 15 May 2025, with 3308 observations. The data reached an all-time high of 83.608 NA in 24 Mar 2020 and a record low of 10.135 NA in 28 Jul 2023. National Stock Exchange of India Limited: Index: India VIX Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under High Frequency Database’s Financial and Futures Market – Table IN.EDI.SE: National Stock Exchange of India Limited.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

VIX Futures Signal Increased Market Volatility Ahead.

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Volatility Gauge Signals Stability for S&P 500 VIX Index

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Graph and download economic data for CBOE Gold ETF Volatility Index (GVZCLS) from 2008-06-03 to 2025-06-27 about ETF, VIX, gold, volatility, stock market, and USA.

Graph and download economic data for CBOE Crude Oil ETF Volatility Index (OVXCLS) from 2007-05-10 to 2025-06-27 about ETF, VIX, volatility, crude, oil, stock market, and USA.

Prices for Euro Stoxx 50 Volatility EUR Price Index including live quotes, historical charts and news. Euro Stoxx 50 Volatility EUR Price Index was last updated by Trading Economics this July 1 of 2025.