Taiwan TAIFEX: TR: Buy: CM: Managed Futures Ent & Trust Fund data was reported at 12,459.000 Contract in Jun 2018. This records an increase from the previous number of 12,258.000 Contract for May 2018. Taiwan TAIFEX: TR: Buy: CM: Managed Futures Ent & Trust Fund data is updated monthly, averaging 19,553.000 Contract from Feb 2004 (Median) to Jun 2018, with 173 observations. The data reached an all-time high of 175,606.000 Contract in Jun 2005 and a record low of 2,679.000 Contract in Feb 2013. Taiwan TAIFEX: TR: Buy: CM: Managed Futures Ent & Trust Fund data remains active status in CEIC and is reported by Taiwan Futures Exchange. The data is categorized under Global Database’s Taiwan – Table TW.Z020: Taiwan Futures Exchange (TAIFEX): Futures and Options Transaction.

Taiwan TAIFEX: Options: TR: Sell: CM: Managed Futures Ent & Trust Fund data was reported at 3,539.000 Contract in Jun 2018. This records an increase from the previous number of 2,914.000 Contract for May 2018. Taiwan TAIFEX: Options: TR: Sell: CM: Managed Futures Ent & Trust Fund data is updated monthly, averaging 11,729.000 Contract from Feb 2004 (Median) to Jun 2018, with 173 observations. The data reached an all-time high of 174,508.000 Contract in Jun 2005 and a record low of 445.000 Contract in Apr 2013. Taiwan TAIFEX: Options: TR: Sell: CM: Managed Futures Ent & Trust Fund data remains active status in CEIC and is reported by Taiwan Futures Exchange. The data is categorized under Global Database’s Taiwan – Table TW.Z020: Taiwan Futures Exchange (TAIFEX): Futures and Options Transaction.

Futures market is an important part of the financial market, with a high degree of liquidity and leverage effect. However, the futures market is also faced with various risk factors, such as price fluctuations, market shocks, supply and demand changes. In order to better determine the risk correlation between specific futures markets, this paper uses the wavelet transform—quantile Granger causality test method to identify the risk correlation of four major futures markets in the US futures market from the end of January 2009 to the end of March 2023, such as gold, crude oil, soybeans and natural gas. It provides a new perspective and method for the risk correlation identification of the futures market. The results show that futures contracts with different maturities and price fluctuations under different quantiles have a significant impact on risk correlation. Specifically, in 1-month and 6-month futures contracts, the strongest bidirectional risk correlation exists between gold and natural gas (T-statistics -15.94 and 10.92, respectively); In the 1-month futures contract, there is also a strong bidirectional risk association between crude oil and soybeans and natural gas (T-statistics are 6.87, 17.42, -2.05, 7.35, respectively), while in the 6-month futures contract, there is a bidirectional risk association between crude oil and soybeans (T-statistics are -2.49 and 18.374, respectively). However, natural gas has unidirectional risk association with crude oil and soybean (t statistics are 2.7 and -3.35, respectively); There is a bidirectional risk correlation between gold and soybean, that is, the risk correlation between gold and soybean increases with the increase of the degree of price fluctuation; There is a one-way risk association between gold and crude oil, soybean and gold, and crude oil and natural gas (the T-statistic is greater than the critical value of 1.96). In addition, there is a strong bidirectional or unidirectional risk association between all varieties at the 0.95 quantile. The research results of this paper have certain reference value for the supervision, investment and risk management of the futures market. This paper uses the wavelet transform and quantile Granger causality test method to identify the risk correlation of the futures market, providing a new perspective and method for the risk correlation identification of the futures market, and uses relatively new data to ensure the effectiveness of the empirical analysis. However, there are some limitations in this paper, such as the applicability of wavelet transform-quantile Granger causality test method. Future studies can further expand the sample range, compare the effects of different methods, and explore the risk transmission mechanism between different varieties.

Taiwan TAIFEX: Futures: TR: Sell: CM: Managed Futures Ent & Trust Fund data was reported at 6,433.000 Contract in Jun 2018. This records a decrease from the previous number of 7,575.000 Contract for May 2018. Taiwan TAIFEX: Futures: TR: Sell: CM: Managed Futures Ent & Trust Fund data is updated monthly, averaging 4,787.000 Contract from Feb 2004 (Median) to Jun 2018, with 173 observations. The data reached an all-time high of 19,566.000 Contract in May 2016 and a record low of 17.000 Contract in Feb 2004. Taiwan TAIFEX: Futures: TR: Sell: CM: Managed Futures Ent & Trust Fund data remains active status in CEIC and is reported by Taiwan Futures Exchange. The data is categorized under Global Database’s Taiwan – Table TW.Z020: Taiwan Futures Exchange (TAIFEX): Futures and Options Transaction.

Futures Trading Software Market Outlook

The global futures trading software market size was estimated at approximately $1.5 billion in 2023 and is forecasted to grow to around $3.8 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 10.5% over the forecast period. The remarkable growth in market size can be attributed to several factors, including technological advancements in trading platforms, increased participation of retail investors in futures trading, and the growing need for efficient risk management tools.

One of the primary growth factors driving the futures trading software market is the rapid technological advancements in trading solutions. High-frequency trading, algorithmic trading, and artificial intelligence (AI)-driven trading are some of the innovations reshaping the futures trading landscape. These technologies offer traders unparalleled speed, efficiency, and accuracy, which is crucial in a market where time is money. Additionally, the continuous development of more user-friendly and intuitive trading platforms is attracting a broader range of users, from seasoned traders to novices, further accelerating market growth.

The increased participation of retail investors in the futures market is another significant growth driver. Historically, futures trading was dominated by institutional investors due to its complexity and the substantial capital required. However, the democratization of financial markets and enhanced accessibility through online trading platforms have opened up futures trading to retail investors. The availability of educational resources and tools within these software solutions has empowered individual investors to navigate the complexities of futures trading, thereby broadening the market base.

Efficient risk management is a critical component of futures trading, and this need has fueled the demand for sophisticated trading software. Futures trading inherently involves high risk due to market volatility and leverage, necessitating robust risk management tools. Modern trading software provides advanced features such as real-time market analysis, automated trading strategies, and comprehensive reporting. These features help traders mitigate risks and make informed decisions, thus driving the adoption of trading software across various market segments.

The emergence of Stock Auto Trading Software has revolutionized the way traders engage with the futures market. This software leverages advanced algorithms and machine learning techniques to automate trading decisions, thereby reducing human error and enhancing trading efficiency. By analyzing vast amounts of market data in real-time, Stock Auto Trading Software can identify profitable trading opportunities and execute trades at optimal times. This automation not only saves time for traders but also allows them to capitalize on market movements more effectively. As the demand for automated solutions grows, Stock Auto Trading Software is becoming an essential tool for both novice and experienced traders looking to optimize their trading strategies and manage risk more effectively.

Regionally, North America remains a dominant player in the futures trading software market, largely due to its advanced financial infrastructure and high adoption of technology in trading. However, significant growth is also observed in the Asia Pacific region, driven by the rapid development of financial markets in countries like China and India. Europe and Latin America are also witnessing steady growth, supported by increasing regulatory support and technological advancements. Each region presents unique opportunities and challenges, contributing to the overall dynamics of the global market.

Component Analysis

The futures trading software market can be segmented into software and services. Software forms the core component of this market, encompassing trading platforms, analytical tools, and various automated systems that facilitate trading activities. Software solutions are designed to cater to the diverse needs of traders, offering functionalities such as real-time data analysis, customizable dashboards, and automated trading strategies. The continuous innovation in software solutions, driven by advancements in AI and machine learning, is expected to dominate this segment's growth. Traders increasingly demand sophisticated software that provides a competitive edge in the fast-paced futures market.

Services c

Futures Trading Service Market Outlook

The global futures trading service market size was valued at USD 5.2 billion in 2023 and is projected to reach USD 10.8 billion by 2032, growing at a CAGR of 8.5% during the forecast period. The significant growth in market size can be attributed to increased trading activities, technological advancements in trading platforms, and rising interest from individual and institutional investors alike.

A major growth factor for the futures trading service market is the rising prevalence of advanced trading platforms and technologies. Technological advancements have made futures trading more accessible and efficient, enabling traders to execute complex strategies with greater ease. The integration of artificial intelligence and machine learning into trading algorithms has also enhanced decision-making processes, resulting in improved trading outcomes and increased market participation.

Another key driver is the increased participation of institutional investors. As financial markets become more interconnected, institutional investors are increasingly turning to futures trading to hedge against market volatility and optimize their portfolios. The availability of diverse asset classes within futures trading, including commodities, financials, and indices, provides these investors with a wide range of options to manage their risk exposure effectively.

Moreover, the growing interest among individual investors is fueling market expansion. The democratization of trading platforms has lowered entry barriers, allowing retail traders to participate in futures markets. Educational resources and advisory services provided by brokerage firms further support individual investors in navigating the complexities of futures trading, thereby contributing to market growth.

Commodity Services play a pivotal role in the futures trading market, offering a wide range of opportunities for both hedgers and speculators. These services encompass the trading of various commodities such as agricultural products, energy resources, and precious metals. The inherent volatility in commodity prices makes futures contracts an attractive tool for managing risk and securing price stability. As global demand for commodities continues to rise, driven by factors like population growth and industrialization, the importance of robust commodity services in futures trading becomes increasingly evident. These services not only facilitate efficient price discovery but also provide a platform for market participants to capitalize on price movements and achieve their financial objectives.

In terms of regional outlook, North America holds the largest market share due to the presence of major financial institutions and advanced trading infrastructure. The Asia Pacific region is expected to witness the highest growth rate, driven by increasing economic development, rising disposable incomes, and the expansion of financial markets in countries like China and India. Europe also shows significant potential, with well-established financial hubs such as London and Frankfurt contributing to market growth.

Service Type Analysis

The futures trading service market can be segmented by service type into brokerage services, trading platforms, advisory services, and others. Brokerage services dominate the market, providing essential intermediary functions that facilitate trading activities. These services are crucial for both individual and institutional investors, offering benefits such as access to diverse markets, real-time data, and personalized customer support. The competitive landscape among brokerage firms is intense, with key players continuously enhancing their offerings to attract and retain clients.

Trading platforms are another significant segment within the futures trading service market. These platforms offer a suite of tools and features that enable traders to execute trades, monitor market conditions, and analyze trends. The evolution of trading platforms from desktop-based applications to web-based and mobile solutions has made it easier for traders to engage with the market anytime and anywhere. Features such as automated trading, advanced charting, and customizable interfaces are driving the adoption of these platforms among traders.

Advisory services play a critical role in guiding investors through the complexities of futures trading. These services provide expert anal

The Disaggregated Futures and Options Commitments of Traders dataset provides a breakdown of each week's open interest for agriculture, energy, metals, lumber, and emissions futures markets in which 20 or more traders hold positions equal to or above the reporting levels established by the CFTC. Open interest is reported separately by reportable and non-reportable positions for Producer/Merchant/Processor/User, Swap Dealers, Managed Money. and Other Reportables holdings, including spreading, changes from the previous report, percents of open interest by category, and numbers of traders. Agriculture futures market data is also grouped by crop year, where appropriate, and shows the concentration of positions held by the largest four and eight traders.

ICE Futures US iMpact is the primary data feed for ICE Futures US and covers the majority of trading in agricultural commodities, including sugar, coffee, cotton, and cocoa futures and options. This comprehensive market data feed also includes financial products such as equity indexes, currencies, and US Treasury futures contracts. The dataset provides complete market depth information across all listed outrights, spreads, options, and options combinations for every expiration month. ICE Futures US represents one of the most significant exchanges for US-based agricultural and financial derivatives, offering essential price discovery and risk management tools for global market participants.

Asset class: Futures, Options

Origin: Captured at Aurora DC3 with an FPGA-based network card and hardware timestamping. Synchronized to UTC with PTP

Supported data encodings: DBN, CSV, JSON (Learn more)

Supported market data schemas: MBO, MBP-1, MBP-10, TBBO, Trades, OHLCV-1s, OHLCV-1m, OHLCV-1h, OHLCV-1d, Definition, Statistics (Learn more)

Resolution: Immediate publication, nanosecond-resolution timestamps

In 2022, ***** billion futures contracts were traded worldwide, up from ***** billion in 2013. The number of options contracts traded increased from **** to ***** billion contracts in the same period. Both contracts are financial derivatives, used to manage financial risk and speculate on future market performance. What are derivatives? Derivatives are financial instruments that are based on an underlying asset, such as a stock price, commodity value, or currency. There are multiple categories of derivatives, but this statistic focuses on futures and options. Futures contracts are the commitment to buy or sell the underlying at a future date for a set price. Options contracts are similar, but the holder is not required to execute the contract. Derivatives are often bought and sold on specific exchanges. What are derivatives used for? The promise of a futures contract is appealing to investors and firms who want to guarantee their expenses. For example, volatile commodities such as crude oil can rise suddenly, so a futures contract can hedge against a rise that would be damaging to a firm that relies heavily on gasoline, such as a transport company.

The global futures trading services market is experiencing robust growth, driven by increasing technological advancements, rising institutional and retail investor participation, and the growing adoption of online and mobile trading platforms. The market size in 2025 is estimated at $15 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033. This signifies a substantial expansion of the market to an estimated $28 billion by 2033. Several factors contribute to this positive outlook. The increasing sophistication of trading algorithms and the availability of real-time market data are enhancing trading efficiency and profitability, attracting both novice and experienced traders. Furthermore, the diversification of tradable assets, including a broader range of commodities and indices, provides greater opportunities for portfolio diversification and risk management. Software-based futures trading platforms are gaining significant traction due to their advanced analytical capabilities and ease of integration with other trading tools. However, regulatory scrutiny, cybersecurity risks, and the inherent volatility of futures markets present challenges to sustained growth. The regulatory landscape is constantly evolving, requiring firms to adapt to new compliance requirements and enhance cybersecurity protocols to protect against data breaches and fraud. Moreover, fluctuations in global economic conditions and geopolitical events can significantly impact market sentiment and trading volumes. Despite these restraints, the market's growth trajectory is expected to remain positive, driven primarily by technological innovation and the expanding reach of online trading platforms to a wider investor base. The segment encompassing share price index futures and commodity futures are projected to exhibit the strongest growth, reflecting increased investor interest in these asset classes.

A list of the top 50 Bright Futures Wealth Management LLC holdings showing which stocks are owned by Bright Futures Wealth Management LLC's hedge fund.

Futures market is an important part of the financial market, with a high degree of liquidity and leverage effect. However, the futures market is also faced with various risk factors, such as price fluctuations, market shocks, supply and demand changes. In order to better determine the risk correlation between specific futures markets, this paper uses the wavelet transform—quantile Granger causality test method to identify the risk correlation of four major futures markets in the US futures market from the end of January 2009 to the end of March 2023, such as gold, crude oil, soybeans and natural gas. It provides a new perspective and method for the risk correlation identification of the futures market. The results show that futures contracts with different maturities and price fluctuations under different quantiles have a significant impact on risk correlation. Specifically, in 1-month and 6-month futures contracts, the strongest bidirectional risk correlation exists between gold and natural gas (T-statistics -15.94 and 10.92, respectively); In the 1-month futures contract, there is also a strong bidirectional risk association between crude oil and soybeans and natural gas (T-statistics are 6.87, 17.42, -2.05, 7.35, respectively), while in the 6-month futures contract, there is a bidirectional risk association between crude oil and soybeans (T-statistics are -2.49 and 18.374, respectively). However, natural gas has unidirectional risk association with crude oil and soybean (t statistics are 2.7 and -3.35, respectively); There is a bidirectional risk correlation between gold and soybean, that is, the risk correlation between gold and soybean increases with the increase of the degree of price fluctuation; There is a one-way risk association between gold and crude oil, soybean and gold, and crude oil and natural gas (the T-statistic is greater than the critical value of 1.96). In addition, there is a strong bidirectional or unidirectional risk association between all varieties at the 0.95 quantile. The research results of this paper have certain reference value for the supervision, investment and risk management of the futures market. This paper uses the wavelet transform and quantile Granger causality test method to identify the risk correlation of the futures market, providing a new perspective and method for the risk correlation identification of the futures market, and uses relatively new data to ensure the effectiveness of the empirical analysis. However, there are some limitations in this paper, such as the applicability of wavelet transform-quantile Granger causality test method. Future studies can further expand the sample range, compare the effects of different methods, and explore the risk transmission mechanism between different varieties.

Futures market is an important part of the financial market, with a high degree of liquidity and leverage effect. However, the futures market is also faced with various risk factors, such as price fluctuations, market shocks, supply and demand changes. In order to better determine the risk correlation between specific futures markets, this paper uses the wavelet transform—quantile Granger causality test method to identify the risk correlation of four major futures markets in the US futures market from the end of January 2009 to the end of March 2023, such as gold, crude oil, soybeans and natural gas. It provides a new perspective and method for the risk correlation identification of the futures market. The results show that futures contracts with different maturities and price fluctuations under different quantiles have a significant impact on risk correlation. Specifically, in 1-month and 6-month futures contracts, the strongest bidirectional risk correlation exists between gold and natural gas (T-statistics -15.94 and 10.92, respectively); In the 1-month futures contract, there is also a strong bidirectional risk association between crude oil and soybeans and natural gas (T-statistics are 6.87, 17.42, -2.05, 7.35, respectively), while in the 6-month futures contract, there is a bidirectional risk association between crude oil and soybeans (T-statistics are -2.49 and 18.374, respectively). However, natural gas has unidirectional risk association with crude oil and soybean (t statistics are 2.7 and -3.35, respectively); There is a bidirectional risk correlation between gold and soybean, that is, the risk correlation between gold and soybean increases with the increase of the degree of price fluctuation; There is a one-way risk association between gold and crude oil, soybean and gold, and crude oil and natural gas (the T-statistic is greater than the critical value of 1.96). In addition, there is a strong bidirectional or unidirectional risk association between all varieties at the 0.95 quantile. The research results of this paper have certain reference value for the supervision, investment and risk management of the futures market. This paper uses the wavelet transform and quantile Granger causality test method to identify the risk correlation of the futures market, providing a new perspective and method for the risk correlation identification of the futures market, and uses relatively new data to ensure the effectiveness of the empirical analysis. However, there are some limitations in this paper, such as the applicability of wavelet transform-quantile Granger causality test method. Future studies can further expand the sample range, compare the effects of different methods, and explore the risk transmission mechanism between different varieties.

United Kingdom Hedge Fund Market was valued at USD 1.21 Trillion in 2024 and is expected to reach USD 1.80 Trillion by 2030 with a CAGR of 6.8% during the forecast period.

Battery Capacity Futures Exchange Market Outlook

According to our latest research, the global Battery Capacity Futures Exchange market size in 2024 stands at USD 2.38 billion, reflecting a robust growth trajectory driven by the increasing demand for energy storage solutions and risk management in volatile battery supply chains. The market is expected to grow at a CAGR of 28.7% from 2025 to 2033, reaching a forecasted value of USD 20.04 billion by 2033. This exponential growth is propelled by the acceleration of electric vehicle adoption, expansion of renewable energy infrastructure, and the need for hedging instruments to mitigate price fluctuations in battery materials and capacities.

One of the primary growth factors for the Battery Capacity Futures Exchange market is the surging demand for electric vehicles (EVs) worldwide. As governments enforce stricter emission regulations and provide incentives for EV adoption, automotive manufacturers are ramping up production, resulting in unprecedented demand for batteries. This scenario has led to increased volatility in battery material prices and supply chains, making futures contracts an essential tool for manufacturers and suppliers to lock in prices and manage procurement risks. The rise of battery gigafactories and strategic alliances between automakers and battery producers further accentuates the need for robust financial instruments to hedge against unforeseen market fluctuations, cementing the critical role of battery capacity futures in the global automotive and energy sectors.

Another significant driver is the rapid expansion of renewable energy storage systems. As grid operators and energy companies accelerate the integration of solar and wind power, the need for advanced battery storage solutions has soared. This shift is further amplified by the global push toward decarbonization and energy transition. Battery Capacity Futures Exchanges enable energy companies, utility providers, and institutional investors to manage price risks associated with large-scale battery procurement for grid storage projects. The ability to secure future battery capacities at predetermined prices supports long-term planning and investment in renewable infrastructure, thus fostering market stability and encouraging broader adoption of clean energy technologies.

Technological advancements in battery chemistries and trading platforms are also pivotal to the market’s growth. Innovations such as solid-state batteries, flow batteries, and enhanced lithium-ion technologies are reshaping the landscape of energy storage, offering improved performance, safety, and longevity. Simultaneously, the digitalization of trading platforms and the advent of blockchain-based exchanges have enhanced transparency, efficiency, and accessibility in battery futures trading. These developments are attracting a diverse range of participants, from institutional investors to energy companies and automotive manufacturers, further fueling market expansion. The convergence of technology and finance in this domain is establishing a dynamic ecosystem that supports risk mitigation, price discovery, and capital allocation.

Regionally, Asia Pacific is emerging as the dominant force in the Battery Capacity Futures Exchange market, underpinned by its leadership in battery manufacturing and electric mobility. China, Japan, and South Korea collectively account for a significant share of global battery production and consumption. North America and Europe are also witnessing accelerated growth, driven by ambitious clean energy targets and the localization of battery supply chains. The Middle East & Africa and Latin America are gradually integrating into the global battery value chain, presenting untapped opportunities for market participants. This regional diversity enhances liquidity and depth in battery futures markets, facilitating global risk management and capital flows.

Product Type Analysis

The Battery Capacity Futures Exchange market is segmented by prod

The global financial derivatives market is experiencing robust growth, driven by increasing market volatility, the need for sophisticated risk management tools, and the expansion of investment opportunities across diverse asset classes. The market, encompassing forwards, futures, options, and swaps used for hedging, speculative arbitrage, and other purposes, is projected to maintain a healthy Compound Annual Growth Rate (CAGR). While precise figures for market size and CAGR are not provided, a reasonable estimation based on industry reports and observed market trends suggests a substantial market value, likely in the hundreds of billions or even trillions of dollars, depending on the chosen valuation methodology (e.g., notional value vs. market value of outstanding contracts). Key drivers include the growing complexity of global financial markets, regulatory changes demanding more robust risk mitigation strategies, and the increasing adoption of algorithmic trading and high-frequency trading, which rely heavily on derivative instruments. Geographic growth is uneven, with North America and Europe currently holding significant market share, while Asia-Pacific shows considerable potential for future expansion due to increasing financial market sophistication and economic growth in emerging economies like China and India. However, the market also faces certain restraints. These include stringent regulatory oversight aimed at mitigating systemic risk, which can increase compliance costs and limit certain trading strategies. Furthermore, the inherent complexity of many derivatives products requires specialized expertise, potentially limiting accessibility for smaller investors and businesses. Market fluctuations and unforeseen global events (e.g., geopolitical instability, economic recessions) can impact market sentiment and trading volumes. The competitive landscape is highly concentrated, with major global investment banks and specialized financial institutions dominating the market. However, the increasing adoption of fintech solutions and the emergence of new market participants, especially in the areas of exchange-traded derivatives and over-the-counter (OTC) markets, are likely to reshape the market dynamics over the forecast period. The segmentation by derivative type (forwards, futures, options, swaps) and application (hedging, speculative arbitrage, others) provides a granular view of market dynamics, enabling strategic decision-making for businesses operating within this dynamic sector.

Futures market is an important part of the financial market, with a high degree of liquidity and leverage effect. However, the futures market is also faced with various risk factors, such as price fluctuations, market shocks, supply and demand changes. In order to better determine the risk correlation between specific futures markets, this paper uses the wavelet transform—quantile Granger causality test method to identify the risk correlation of four major futures markets in the US futures market from the end of January 2009 to the end of March 2023, such as gold, crude oil, soybeans and natural gas. It provides a new perspective and method for the risk correlation identification of the futures market. The results show that futures contracts with different maturities and price fluctuations under different quantiles have a significant impact on risk correlation. Specifically, in 1-month and 6-month futures contracts, the strongest bidirectional risk correlation exists between gold and natural gas (T-statistics -15.94 and 10.92, respectively); In the 1-month futures contract, there is also a strong bidirectional risk association between crude oil and soybeans and natural gas (T-statistics are 6.87, 17.42, -2.05, 7.35, respectively), while in the 6-month futures contract, there is a bidirectional risk association between crude oil and soybeans (T-statistics are -2.49 and 18.374, respectively). However, natural gas has unidirectional risk association with crude oil and soybean (t statistics are 2.7 and -3.35, respectively); There is a bidirectional risk correlation between gold and soybean, that is, the risk correlation between gold and soybean increases with the increase of the degree of price fluctuation; There is a one-way risk association between gold and crude oil, soybean and gold, and crude oil and natural gas (the T-statistic is greater than the critical value of 1.96). In addition, there is a strong bidirectional or unidirectional risk association between all varieties at the 0.95 quantile. The research results of this paper have certain reference value for the supervision, investment and risk management of the futures market. This paper uses the wavelet transform and quantile Granger causality test method to identify the risk correlation of the futures market, providing a new perspective and method for the risk correlation identification of the futures market, and uses relatively new data to ensure the effectiveness of the empirical analysis. However, there are some limitations in this paper, such as the applicability of wavelet transform-quantile Granger causality test method. Future studies can further expand the sample range, compare the effects of different methods, and explore the risk transmission mechanism between different varieties.

Market Overview The global futures trading software market is projected to reach a market size of USD 3601.3 million by 2033, expanding at a CAGR of 6.4%. The increasing demand for risk management tools, advancements in technology, and growing popularity of algorithmic trading are driving market growth. Additionally, the rising number of personal and commercial traders, along with the increasing availability of mobile trading platforms, further contribute to the market's expansion. Key Trends and Segments Mobile trading platforms are gaining traction due to their convenience and accessibility, while PC versions remain dominant in commercial applications. Personal and commercial traders continue to be the largest user segments, with commercial use expected to see significant growth as businesses seek advanced risk management capabilities. Key companies in the market include Straight Flush, Goldman Sachs, Morgan Stanley, J.P. Morgan, and BANK OF AMERICA. Geographically, the Asia Pacific region holds the largest market share, followed by North America and Europe. Emerging markets such as China and India are expected to present significant growth opportunities for market expansion.

To support coordinated conservation, wetland restoration, and climate adaptation planning, we have developed five future scenarios of the Central Valley's seasonally flooded cropland and wetland waterbird habitat based on the State’s most recent climate and land use projections (Wilson et al. 2021).The USGS Western Geographic Science Center and Point Blue Conservation Science modeled a Business-as-Usual scenario plus the four scenarios developed for the Central Valley Landscape Conservation Project, which diverged along two key themes: water availability and management for conservation. Scenarios varied by climate projection (hot and wet vs. warm and dry) and management priorities (wetland restoration rate, crop conversion rate, and prioritization of water for wetland and cropland habitats). Urbanization rates were the same for all scenarios. To model these scenarios, we integrated a hydrologic and water-use model (the Water Evaluation and Planning (WEAP) model, WEAP-CVwh, Matchett and Fleskes, 2017) with a land change model (the Land Use and Carbon Scenario Simulator, LUCAS, Wilson et al. 2020). The models produced annual maps of land use change and monthly maps of flooded habitat probability at 270-meter resolution, from 2011 to 2101 (Wilson et al. 2021). The scenarios were: Historical Business As Usual (HBAU) = historical water availability, historical management California Dreamin' (DREAM) = high water, good management Bad Business As Usual (BBAU) = high water, poor management Everyone Equally Miserable (EEM) = low water, good management Central Valley Dustbowl (DUST) = low water, poor management This data release contains three types of model output tabular summaries for four geographic areas: WEAP model zones, Sustainable Groundwater Management Act (SGMA) California Bulletin 118 groundwater sub-basins, Central Valley Joint Venture (CVJV) planning basins, and Central Valley regions. The datasets summarize 1) land use change for select land use/land cover classes, 2) area of likely flooded habitat, and 3) change in January flooded habitat area and its causes for the 5 future scenarios of managed waterbird habitat. The datasets were generated from the LUCAS model and the WEAP CVwh model as described in the parent manuscript. The full methods and results of this research are described in detail in the parent manuscript "Integrated modeling of climate and land change impacts on future dynamic wetland habitat – a case study from California’s Central Valley" (2021). These tabular summaries provide the underlying data behind the figures in the ESRI Story Map: Central Valley Water and Land Use Futures, https://wim.usgs.gov/geonarrative/centralvalleyfutures/ (Moritsch et al. 2021).

To meet the climate change planning and adaptation needs of Alaska managers and decision makers, I developed a set of statewide summaries of available climate change projections that can be further subset using GIS techniques for requests by management unit, watershed, or other location. This facilitates the development of tailored climate futures for decision makers’ regional or subregional management context. This file describes the source data and summaries for purposes of technical /scientific documentation. The methods and presentation for these datasets were adapted from products in previous USGS-approved IP products for the AKCASC Building Resilience Today project (e.g, Community of Kotlik et al. 2019). For each data product included, summaries (averages or totals) are presented for multiple climate models or specific global warming levels and are average dover two time periods: 2040-2069, or the “2050s”, for near-term decision framing; and 2070-2099, or the “2080s”, for longer-term decision framing. In all cases where possible, both moderate emissions (RCP4.5 or +2C global level) and higher emissions (RCP8.5, or +4C global level) are presented. These choices (model averaging, temporal averaging, and scenario presentation) are tailored to the main sources of uncertainty (Hawkins and Sutton 2009) in climate model projections, specifically differences in climate model construction, climatic variability, and emissions scenario uncertainty (e.g., Littell et al. 2011, Snover et al. 2013, Terando et al. 2020). Not all scenario planning or climate impacts modeling needs can be met with these projections – these are intended to characterize a range of futures indicated by the available data products and facilitate further exploration of climate impacts modeling and adaptation development options.