15 datasets found
  1. F

    CBOE Gold ETF Volatility Index

    • fred.stlouisfed.org
    json
    Updated Jun 20, 2025
    + more versions
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    (2025). CBOE Gold ETF Volatility Index [Dataset]. https://fred.stlouisfed.org/series/GVZCLS
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    jsonAvailable download formats
    Dataset updated
    Jun 20, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-citation-requiredhttps://fred.stlouisfed.org/legal/#copyright-citation-required

    Description

    Graph and download economic data for CBOE Gold ETF Volatility Index (GVZCLS) from 2008-06-03 to 2025-06-18 about ETF, VIX, gold, volatility, stock market, and USA.

  2. T

    United States - CBOE Gold ETF Volatility

    • tradingeconomics.com
    csv, excel, json, xml
    Updated Feb 8, 2020
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    TRADING ECONOMICS (2020). United States - CBOE Gold ETF Volatility [Dataset]. https://tradingeconomics.com/united-states/cboe-gold-etf-volatility-index-fed-data.html
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    csv, xml, excel, jsonAvailable download formats
    Dataset updated
    Feb 8, 2020
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 1, 1976 - Dec 31, 2025
    Area covered
    United States
    Description

    United States - CBOE Gold ETF Volatility was 20.41000 Index in June of 2025, according to the United States Federal Reserve. Historically, United States - CBOE Gold ETF Volatility reached a record high of 64.53000 in October of 2008 and a record low of 8.88000 in May of 2019. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - CBOE Gold ETF Volatility - last updated from the United States Federal Reserve on June of 2025.

  3. M

    Gold ETF Volatility Index (2008-2025)

    • macrotrends.net
    csv
    Updated Jun 30, 2025
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    MACROTRENDS (2025). Gold ETF Volatility Index (2008-2025) [Dataset]. https://www.macrotrends.net/3448/gold-etf-volatility-index
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    csvAvailable download formats
    Dataset updated
    Jun 30, 2025
    Dataset authored and provided by
    MACROTRENDS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    2008 - 2025
    Area covered
    United States
    Description

    Exchange Traded Funds (ETFs) are shares of trusts that hold portfolios of stocks designed to closely track the price performance and yield of specific indices. Copyright, 2016, Chicago Board Options Exchange, Inc. Reprinted with permission.

  4. T

    United States - CBOE Gold Miners ETF Volatility (DISCONTINUED)

    • tradingeconomics.com
    csv, excel, json, xml
    Updated Jun 17, 2025
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    TRADING ECONOMICS (2025). United States - CBOE Gold Miners ETF Volatility (DISCONTINUED) [Dataset]. https://tradingeconomics.com/united-states/cboe-gold-miners-etf-volatility-index-fed-data.html
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    json, csv, excel, xmlAvailable download formats
    Dataset updated
    Jun 17, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 1, 1976 - Dec 31, 2025
    Area covered
    United States
    Description

    United States - CBOE Gold Miners ETF Volatility (DISCONTINUED) was 40.41000 Index in February of 2022, according to the United States Federal Reserve. Historically, United States - CBOE Gold Miners ETF Volatility (DISCONTINUED) reached a record high of 118.75000 in March of 2020 and a record low of 15.40000 in June of 2018. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - CBOE Gold Miners ETF Volatility (DISCONTINUED) - last updated from the United States Federal Reserve on June of 2025.

  5. Stocks dataset for Gold Price prediction

    • kaggle.com
    Updated Aug 16, 2021
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    Ravi Chauhan (2021). Stocks dataset for Gold Price prediction [Dataset]. https://www.kaggle.com/datasets/ravichauhan7/stocks-dataset-for-gold-price-prediction
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 16, 2021
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Ravi Chauhan
    Description

    Context

    Content

    Ticker Description 0 GC=F Gold 1 SI=F Silver 2 CL=F Crude Oil 3 ^GSPC S&P500 4 PL=F Platinum 5 HG=F Copper 6 DX=F Dollar Index 7 ^VIX Volatility Index 8 EEM MSCI EM ETF 9 EURUSD=X Euro USD 10 ^N100 Euronext100 11 ^IXIC Nasdaq 12 ^BSESN Bse sensex 13 ^NSEI Nifty 50 14 ^DJI Dow

  6. Annual volatility of major commodity indices and precious metals worldwide...

    • statista.com
    Updated Jan 11, 2022
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    Statista (2022). Annual volatility of major commodity indices and precious metals worldwide 2019 [Dataset]. https://www.statista.com/statistics/1061421/annual-volatility-gold-major-commodity-indices-precious-metals-global/
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    Dataset updated
    Jan 11, 2022
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jun 2009 - Jun 2019
    Area covered
    Worldwide
    Description

    Between June 2009 and June 2019, gold had an annualized daily volatility of 15.81 percent, which made it considerably less volatile than silver and the Bloomberg WTI Oil Index.

  7. k

    Dow Jones North America Select Junior Gold Index Forecast Data

    • kappasignal.com
    csv, json
    Updated May 24, 2024
    + more versions
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    KappaSignal (2024). Dow Jones North America Select Junior Gold Index Forecast Data [Dataset]. https://www.kappasignal.com/
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    json, csvAvailable download formats
    Dataset updated
    May 24, 2024
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    The Dow Jones North America Select Junior Gold index is expected to trend higher in the short term, supported by positive momentum and bullish technical indicators. However, investors should be aware of potential risks, including geopolitical tensions, rising interest rates, and economic uncertainties, which could lead to market volatility and downward pressure on gold prices.

  8. f

    Summary statistics for the log return of S&P 500 index, VIX, USDX, and gold....

    • plos.figshare.com
    xls
    Updated Oct 5, 2023
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    Seok-Jun Yun; Sun-Yong Choi; Young Sung Kim (2023). Summary statistics for the log return of S&P 500 index, VIX, USDX, and gold. [Dataset]. http://doi.org/10.1371/journal.pone.0291684.t009
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Oct 5, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Seok-Jun Yun; Sun-Yong Choi; Young Sung Kim
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Summary statistics for the log return of S&P 500 index, VIX, USDX, and gold.

  9. f

    Data from: GOLD AS AN INVESTMENT VEHICLE

    • figshare.com
    pdf
    Updated May 31, 2023
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    Arti Chandani; Arzoo Bista; Krittika Magotra; Shruti Mahavarthayajula (2023). GOLD AS AN INVESTMENT VEHICLE [Dataset]. http://doi.org/10.6084/m9.figshare.1044170.v1
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    pdfAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    figshare
    Authors
    Arti Chandani; Arzoo Bista; Krittika Magotra; Shruti Mahavarthayajula
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Gold has been used extensively for savings, investments and consumption since ages;however the importance of the gold as an investment instruments has been much talked in the recenttimes. This research paper intends to find various applications of gold portfolios as an alternate assetclass: the benefits of including gold to an investment portfolio have been analyzed. The results indicatethat gold has performed significantly better than other assets like debt and equity in both emerging andUS markets. It was noted that addition of gold to portfolios helped reduce the volatility and increaseoverall returns during the period 2009-12. For example, in 2008, when the U.S. equity market plunged to36.99%, gold in fact showed returns of 5.8%. It is also observed that the inverse correlation existsbetween the dollar index and the gold prices helped reduce the portfolio risk as a result of diversification.

  10. Commodity Index Funds Market Report | Global Forecast From 2025 To 2033

    • dataintelo.com
    csv, pdf, pptx
    Updated Oct 5, 2024
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    Dataintelo (2024). Commodity Index Funds Market Report | Global Forecast From 2025 To 2033 [Dataset]. https://dataintelo.com/report/commodity-index-funds-market
    Explore at:
    csv, pdf, pptxAvailable download formats
    Dataset updated
    Oct 5, 2024
    Dataset authored and provided by
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    Commodity Index Funds Market Outlook



    The global commodity index funds market size was valued at approximately $200 billion in 2023 and is projected to reach nearly $400 billion by 2032, growing at a robust CAGR of 7.5% during the forecast period. The significant growth in this market can be attributed to the increasing demand for diversification in investment portfolios and the inherent benefits of hedging against inflation that commodity investments provide. Furthermore, the volatility in global stock markets and geopolitical uncertainties have led investors to seek safer, more stable investment avenues, thus driving the growth of commodity index funds.



    One of the primary growth factors propelling the commodity index funds market is the rising awareness among investors about the advantages of commodity investments as a hedge against inflation. Commodities, unlike stocks and bonds, often move inversely to the stock market, providing a cushion during market downturns. This characteristic makes commodity index funds an attractive option for risk-averse investors and those looking to balance their portfolios. Additionally, the globalization of trade and the increasing demand for raw materials in emerging markets have further spurred the demand for commodity investments.



    Technological advancements in trading platforms have also significantly contributed to the growth of this market. The advent of sophisticated online platforms has made it easier for retail investors to access and invest in commodity index funds. These platforms offer a range of tools and resources that help investors make informed decisions, thereby democratizing access to commodity investments. Moreover, the rise of robo-advisors and algorithm-based trading strategies has further simplified the investment process, attracting a new generation of tech-savvy investors.



    The regulatory landscape has also played a crucial role in shaping the commodity index funds market. Governments and financial regulatory bodies across the globe have been working to create a transparent and secure trading environment. Regulatory reforms aimed at reducing market manipulation and increasing transparency have instilled confidence among investors, thereby boosting the market. Additionally, tax incentives and favorable policies for commodity investments in various countries have also contributed to market growth.



    In terms of regional outlook, North America holds a significant share of the global commodity index funds market, followed by Europe and Asia Pacific. The presence of well-established financial markets and a high level of investor awareness in North America are key factors driving the market in this region. Europe, with its strong regulatory framework and increasing adoption of alternative investment strategies, is also witnessing substantial growth. Meanwhile, the Asia Pacific region is emerging as a lucrative market, driven by the rapid economic growth in countries like China and India, and the increasing interest in commodity investments among institutional and retail investors.



    Fund Type Analysis



    When analyzing the market by fund type, Broad Commodity Index Funds dominate the landscape. These funds invest in a diversified portfolio of commodities, making them a popular choice for investors seeking broad exposure to the commodity markets. The broad commodity index funds are designed to track the performance of a basket of commodities, ranging from energy products to metals and agricultural goods. This diversification helps mitigate risks associated with the volatility of individual commodities, thereby providing a more stable investment option for risk-averse investors.



    Single Commodity Index Funds, on the other hand, focus on specific commodities such as gold, oil, or agricultural products. These funds appeal to investors who have a strong conviction about the performance of a particular commodity. For instance, during periods of economic uncertainty, gold-focused funds often see a surge in demand as investors flock to the safe-haven asset. Similarly, energy-focused funds attract investors when there are disruptions in oil supply or significant geopolitical events affecting oil prices. While these funds offer the potential for high returns, they also come with higher risks due to their lack of diversification.



    Sector Commodity Index Funds are another important segment within the commodity index funds market. These funds concentrate on commodities within a specific sector, such as energy, agriculture, or metals, allowing investors to target particular segments of the commo

  11. f

    Average dynamic connectedness.

    • plos.figshare.com
    xls
    Updated Feb 15, 2024
    + more versions
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    Muneer Shaik; Mustafa Raza Rabbani; Mohd. Atif; Ahmet Faruk Aysan; Mohammad Noor Alam; Umar Nawaz Kayani (2024). Average dynamic connectedness. [Dataset]. http://doi.org/10.1371/journal.pone.0286963.t003
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    xlsAvailable download formats
    Dataset updated
    Feb 15, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Muneer Shaik; Mustafa Raza Rabbani; Mohd. Atif; Ahmet Faruk Aysan; Mohammad Noor Alam; Umar Nawaz Kayani
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    We investigate the dynamic volatility connectedness of geopolitical risk, stocks, bonds, bitcoin, gold, and oil from January 2018 to April 2022 in this study. We look at connectivity during the Pre-COVID, COVID, and Russian-Ukraine war subsamples. During the COVID-19 and Russian-Ukraine war periods, we find that conventional, Islamic, and sustainable stock indices are net volatility transmitters, whereas gold, US bonds, GPR, oil, and bitcoin are net volatility receivers. During the Russian-Ukraine war, the commodity index (DJCI) shifted from being a net recipient of volatility to a net transmitter of volatility. Furthermore, we discover that bilateral intercorrelations are strong within stock indices (DJWI, DJIM, and DJSI) but weak across all other financial assets. Our study has important implications for policymakers, regulators, investors, and financial market participants who want to improve their existing strategies for avoiding financial losses.

  12. d

    Data from: On the Return-volatility Relationship in the Bitcoin Market...

    • search.dataone.org
    • dataverse.harvard.edu
    Updated Nov 21, 2023
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    Bouri, ElieNachname, Vorname; Azzi, Georges; Haubo Dyhrberg, Anne (2023). On the Return-volatility Relationship in the Bitcoin Market Around the Price Crash of 2013 [Dataset]. http://doi.org/10.7910/DVN/IBNWEV
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    Dataset updated
    Nov 21, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Bouri, ElieNachname, Vorname; Azzi, Georges; Haubo Dyhrberg, Anne
    Description

    The authors examine the relation between price returns and volatility changes in the Bitcoin market using a daily database denominated in various currencies. The results for the entire period provide no evidence of an asymmetric return-volatility relation in the Bitcoin market. They test if there is a difference in the return-volatility relation before and after the price crash of 2013 and show a significant inverse relation between past shocks and volatility before the crash and no significant relation after. This finding shows that, prior to the price crash of December 2013, positive shocks increased the conditional volatility more than negative shocks. This inverted asymmetric reaction of Bitcoin to positive and negative shocks is contrary to what the authors observe in equities. As leverage effect and volatility feedback don’t adequately explain this reaction, they propose the safe-haven effect (Baur, Asymmetric volatility in the gold market, 2012). The authors highlight the benefits of adding Bitcoin to a US equity portfolio, especially in the pre-crash period. Robustness analyses show, among others, a negative relation between the US implied volatility index (VIX) and Bitcoin volatility. Those additional analyses further support their findings and provide useful information for economic actors who are interested in adding Bitcoin to their equity portfolios or are curious about the capabilities of Bitcoin as a financial asset.

  13. f

    Descriptive Statistics of return, volatility, and sentiment in crude oil,...

    • plos.figshare.com
    xls
    Updated Jun 1, 2023
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    Xianfang Su; Yong Li (2023). Descriptive Statistics of return, volatility, and sentiment in crude oil, gold, and Bitcoin markets. [Dataset]. http://doi.org/10.1371/journal.pone.0242515.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Xianfang Su; Yong Li
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Descriptive Statistics of return, volatility, and sentiment in crude oil, gold, and Bitcoin markets.

  14. f

    The statistical results of time-frequency dynamics of sentiment spillovers...

    • plos.figshare.com
    xls
    Updated Jun 6, 2023
    + more versions
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    Xianfang Su; Yong Li (2023). The statistical results of time-frequency dynamics of sentiment spillovers in crude oil, gold, and Bitcoin markets. [Dataset]. http://doi.org/10.1371/journal.pone.0242515.t002
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 6, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Xianfang Su; Yong Li
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The statistical results of time-frequency dynamics of sentiment spillovers in crude oil, gold, and Bitcoin markets.

  15. f

    Summary statistics of hedge ratio.

    • plos.figshare.com
    xls
    Updated Oct 5, 2023
    + more versions
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    Seok-Jun Yun; Sun-Yong Choi; Young Sung Kim (2023). Summary statistics of hedge ratio. [Dataset]. http://doi.org/10.1371/journal.pone.0291684.t006
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Oct 5, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Seok-Jun Yun; Sun-Yong Choi; Young Sung Kim
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This study utilizes the hedging potential of the U.S. Dollar Index (USDX) during the COVID-19 period, specifically comparing its positive effects on optimal portfolio weights and hedging ratios with those of traditional hedging assets, such as the VIX and gold. The scalar BEKK GARCH model is employed to forecast volatility and calculate hedging indicators. The results show that USDX exhibits strong hedging abilities against S&P 500 index volatility. These findings highlight the advantageous role of the USDX as a hedging instrument, particularly during periods of heightened market uncertainty, such as during the COVID-19 crisis. Despite the increased market volatility during the COVID-19 pandemic, the value of the optimal portfolio weights is stable and the volatility of the weights is significantly reduced, demonstrating the strength of the USDX’s low risk and volatility in hedging against market fluctuations. Moreover, the increase in the hedge ratio indicates that more capital is allocated to hedging, reflecting the increased correlation between the USDX and S&P 500 index. These results emphasize the beneficial role of the USDX as a hedging instrument during times of elevated market uncertainty, such as during the COVID-19 crisis. Ultimately, USDX can provide valuable insights for market participants seeking effective hedging strategies.

  16. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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(2025). CBOE Gold ETF Volatility Index [Dataset]. https://fred.stlouisfed.org/series/GVZCLS

CBOE Gold ETF Volatility Index

GVZCLS

Explore at:
96 scholarly articles cite this dataset (View in Google Scholar)
jsonAvailable download formats
Dataset updated
Jun 20, 2025
License

https://fred.stlouisfed.org/legal/#copyright-citation-requiredhttps://fred.stlouisfed.org/legal/#copyright-citation-required

Description

Graph and download economic data for CBOE Gold ETF Volatility Index (GVZCLS) from 2008-06-03 to 2025-06-18 about ETF, VIX, gold, volatility, stock market, and USA.

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