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Hedge Fund Market in US Size 2025-2029

The US hedge fund market size is forecast to increase by USD 738 billion at a CAGR of 8.1% between 2024 and 2029.

US Hedge Fund Market is experiencing significant growth due to increasing investor interest in alternative investment options. This trend is driven by the desire for higher returns and risk diversification, leading to a surge in assets under management. Furthermore, technological advancements are transforming the hedge fund industry, enabling companies to offer innovative solutions and improve operational efficiency. However, the market is not without challenges. Regulatory constraints continue to pose significant obstacles, with stringent regulations governing fund operations, investor protection, and transparency.
Compliance with these regulations requires substantial resources and expertise, presenting a significant challenge for hedge fund managers. Companies seeking to capitalize on market opportunities and navigate these challenges effectively must stay informed of regulatory developments and invest in robust compliance frameworks. Additionally, leveraging technology to streamline operations and enhance transparency can help hedge funds remain competitive and meet investor demands.

What will be the Size of the Hedge Fund Market in US during the forecast period?

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US hedge funds market activities and evolving patterns continue to unfold, shaping the industry's landscape. Hedge funds employ various strategies, such as quantitative methods, algorithmic trading, and relative value strategies, to manage risk and generate alpha. Investor relations play a crucial role in attracting and retaining capital from high-net-worth individuals, family offices, pension funds, and institutional investors. Fund of funds and multi-strategy funds offer diversification, while big data analytics and alternative data inform investment decisions. Machine learning and artificial intelligence enhance risk management and performance measurement. Regulatory compliance and transparency are essential components of hedge fund operations, ensuring liquidity and mitigating drawdowns.
Market dynamics are influenced by various factors, including hedge fund leverage, volatility, and capacity. Hedge fund managers must navigate these complexities to deliver competitive returns, employing due diligence and effective fee structures. Hedge fund distribution channels, such as conferences and sales efforts, facilitate access to new investors. The hedge fund market is a continually evolving ecosystem, where technology, regulatory requirements, and investor expectations shape the industry's future. Hedge fund liquidation and exit strategies, performance fees, and risk appetite are critical considerations for hedge fund managers and investors alike. Ultimately, the hedge fund industry's success hinges on its ability to adapt and innovate in a rapidly changing financial landscape.

How is this Hedge Fund in US Industry segmented?

The hedge fund in US industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Offshore
  Domestic
  Fund of funds


Method

  Long and short equity
  Event driven
  Global macro
  Others


End-user

  Institutional
  Individual


Fund Structure

  Small (
  Medium (USD500M-USD2B)
  Large (>USD2B)


Investor Type

  Institutional
  High-Net-Worth Individuals


Geography

  North America

    US

By Type Insights

The offshore segment is estimated to witness significant growth during the forecast period.

The offshore segment of the hedge fund market in the US houses funds that are managed or marketed by American firms but are domiciled and operated in offshore jurisdictions. These funds, located in financial centers known for their favorable regulatory environments, tax treatment, and legal infrastructure, offer investors tax efficiency through lower or zero taxation on investment income, capital gains, and distributions. The reduced regulatory burden in offshore jurisdictions enables greater flexibility in fund operations, investment strategies, and disclosure obligations, making offshore hedge funds an appealing choice for tax-conscious investors. Portfolio construction, risk management, and hedge fund allocation strategies are crucial elements for these funds, with relative value and long-short equity strategies commonly employed.

Performance fees and management fees are the primary revenue sources for hedge fund managers, while family offices and institutional investors provide significant hedge fund capital. Regulatory compliance and due diligence are essential for investors, ensuring transparency and performance measurement. Hedge fund research, risk appetite, and investor r

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.

Landcadia's Warrant: A Hedge Against Market Volatility? (LCAHW)

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

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

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.

Hedge Fund Management Tool Market Outlook

The global hedge fund management tool market size was valued at approximately USD 4.5 billion in 2023 and is expected to reach around USD 12.3 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 12.1% during the forecast period. The growth of this market is fueled by the increasing complexity of hedge fund operations and the need for advanced tools to optimize portfolio management and ensure regulatory compliance.

One of the primary growth factors driving the hedge fund management tool market is the escalating need for sophisticated risk management solutions. In an environment where market volatility and regulatory scrutiny are at an all-time high, hedge funds are increasingly turning to advanced software tools to better manage and mitigate risks. These tools offer real-time analytics and predictive modeling capabilities, which are essential for making informed investment decisions and safeguarding assets.

Another significant growth driver is the growing demand for automation in trading and operational processes. Hedge funds are constantly seeking ways to enhance operational efficiency and reduce manual errors. Automation tools not only streamline these processes but also provide critical insights into trading activities, allowing fund managers to optimize strategies and improve overall performance. The integration of Artificial Intelligence (AI) and Machine Learning (ML) in these tools further enhances their capabilities, making them indispensable in the modern financial landscape.

The proliferation of cloud-based solutions is also contributing significantly to market growth. Cloud deployment offers several advantages, including reduced infrastructure costs, scalability, and remote accessibility. This is particularly beneficial for small and medium enterprises (SMEs) that may not have the resources to invest in extensive on-premises infrastructure. Cloud-based hedge fund management tools are therefore becoming increasingly popular, enabling firms of all sizes to leverage advanced functionalities without substantial upfront costs.

From a regional perspective, North America currently holds the largest market share, driven by the presence of a large number of hedge funds and advanced financial markets. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, thanks to the rapid development of financial markets and increasing adoption of advanced financial technologies in countries like China, India, and Singapore. Europe also remains a significant market, benefiting from stringent regulatory requirements which necessitate the use of advanced compliance management tools.

Component Analysis

The hedge fund management tool market is segmented into software and services. The software segment is further divided into various types of applications such as portfolio management, risk management, and compliance management, among others. The software segment holds the largest market share due to the increasing demand for integrated platforms that provide comprehensive solutions for various hedge fund operations. Software tools are essential for automating complex tasks, analyzing large datasets, and generating actionable insights, making them indispensable in today's hedge fund management landscape.

Services, the other major component, include consulting, implementation, and support services. These services are crucial for the successful deployment and operation of hedge fund management tools. Consulting services help firms understand their specific needs and choose the right tools, while implementation services ensure that these tools are correctly installed and configured to work seamlessly with existing systems. Support services, including ongoing maintenance and updates, are vital for ensuring the long-term effectiveness and reliability of these tools.

One of the key trends in the component segmentation is the increasing integration of Artificial Intelligence (AI) and Machine Learning (ML) capabilities in software solutions. These advanced technologies enable more accurate predictive analytics, automated trading strategies, and enhanced risk management. The shift towards AI and ML-powered tools is driven by the need for more sophisticated and efficient management of hedge fund operations, particularly in the face of increasing market volatility and regulatory scrutiny.

In addition, the rise of cloud-based software solutions is revolutionizing the hedge fund management tool market

In March 2024 Bitcoin BTC reached a new all-time high with prices exceeding 73000 USD marking a milestone for the cryptocurrency market This surge was due to the approval of Bitcoin exchange-traded funds ETFs in the United States allowing investors to access Bitcoin without directly holding it This development increased Bitcoin’s credibility and brought fresh demand from institutional investors echoing previous price surges in 2021 when Tesla announced its 15 billion investment in Bitcoin and Coinbase was listed on the Nasdaq By the end of 2022 Bitcoin prices dropped sharply to 15000 USD following the collapse of cryptocurrency exchange FTX and its bankruptcy which caused a loss of confidence in the market By August 2024 Bitcoin rebounded to approximately 64178 USD but remained volatile due to inflation and interest rate hikes Unlike fiat currency like the US dollar Bitcoin’s supply is finite with 21 million coins as its maximum supply By September 2024 over 92 percent of Bitcoin had been mined Bitcoin’s value is tied to its scarcity and its mining process is regulated through halving events which cut the reward for mining every four years making it harder and more energy-intensive to mine The next halving event in 2024 will reduce the reward to 3125 BTC from its current 625 BTC The final Bitcoin is expected to be mined around 2140 The energy required to mine Bitcoin has led to criticisms about its environmental impact with estimates in 2021 suggesting that one Bitcoin transaction used as much energy as Argentina Bitcoin’s future price is difficult to predict due to the influence of large holders known as whales who own about 92 percent of all Bitcoin These whales can cause dramatic market swings by making large trades and many retail investors still dominate the market While institutional interest has grown it remains a small fraction compared to retail Bitcoin is vulnerable to external factors like regulatory changes and economic crises leading some to believe it is in a speculative bubble However others argue that Bitcoin is still in its early stages of adoption and will grow further as more institutions and governments recognize its potential as a hedge against inflation and a store of value 2024 has also seen the rise of Bitcoin Layer 2 technologies like the Lightning Network which improve scalability by enabling faster and cheaper transactions These innovations are crucial for Bitcoin’s wider adoption especially for day-to-day use and cross-border remittances At the same time central bank digital currencies CBDCs are gaining traction as several governments including China and the European Union have accelerated the development of their own state-controlled digital currencies while Bitcoin remains decentralized offering financial sovereignty for those who prefer independence from government control The rise of CBDCs is expected to increase interest in Bitcoin as a hedge against these centralized currencies Bitcoin’s journey in 2024 highlights its growing institutional acceptance alongside its inherent market volatility While the approval of Bitcoin ETFs has significantly boosted interest the market remains sensitive to events like exchange collapses and regulatory decisions With the limited supply of Bitcoin and improvements in its transaction efficiency it is expected to remain a key player in the financial world for years to come Whether Bitcoin is currently in a speculative bubble or on a sustainable path to greater adoption will ultimately be revealed over time.

Download Historical CBOE S&P 500 3-Month Volatility Index Indicies Data. CQG daily, 1 minute, tick, and level 1 data from 1899.

Quantum-AI Option Portfolio Hedging Market Outlook

According to our latest research, the global Quantum-AI Option Portfolio Hedging market size reached USD 1.42 billion in 2024, growing at a robust pace. The market is expected to expand at a CAGR of 28.4% from 2025 to 2033, with projections indicating a value of USD 12.36 billion by 2033. This remarkable growth is primarily driven by the increasing adoption of advanced quantum computing and artificial intelligence technologies within the financial sector, as institutions seek to enhance portfolio risk management and optimize hedging strategies in highly volatile and complex market environments.

One of the most significant growth factors for the Quantum-AI Option Portfolio Hedging market is the escalating complexity and volatility of global financial markets. As traditional risk management tools struggle to keep pace with rapidly changing market dynamics, financial institutions are turning to quantum computing and AI solutions to gain a competitive edge. These technologies enable the processing and analysis of vast datasets at unprecedented speeds, allowing for more accurate modeling of risk scenarios and the development of sophisticated hedging strategies. The demand for real-time analytics, improved decision-making, and the ability to anticipate market shocks further fuels the adoption of Quantum-AI solutions, particularly among hedge funds and investment banks aiming to protect portfolios against extreme events and optimize returns.

Another vital driver is the surge in technological advancements and investments in quantum computing infrastructure. Major technology vendors and financial institutions are collaborating to build scalable quantum hardware and software ecosystems tailored for financial applications. The integration of AI algorithms with quantum computational power has unlocked new possibilities in portfolio optimization, risk assessment, and algorithmic trading. As quantum computing hardware becomes more accessible and cloud-based quantum services proliferate, a wider range of market participants—including asset management firms and even retail investors—are able to leverage these advanced tools. This democratization of technology is expected to further accelerate market growth and foster innovation across the financial services landscape.

Regulatory pressures and the need for compliance with evolving risk management standards are also catalyzing market expansion. Regulatory bodies across regions are increasingly mandating robust risk assessment and reporting frameworks, prompting financial organizations to invest in next-generation hedging solutions. Quantum-AI platforms offer enhanced transparency, auditability, and predictive capabilities, helping institutions meet stringent regulatory requirements while maintaining operational efficiency. Furthermore, as the global financial ecosystem becomes more interconnected, the ability to model and hedge against systemic risks using advanced technologies is becoming a critical differentiator for market players.

From a regional perspective, North America currently dominates the Quantum-AI Option Portfolio Hedging market, driven by the presence of leading technology providers, a mature financial sector, and significant R&D investments. However, Europe and Asia Pacific are rapidly catching up, fueled by strong regulatory frameworks, government initiatives supporting quantum research, and the growing adoption of AI-driven financial technologies. Emerging markets in Latin America and the Middle East & Africa are also beginning to explore Quantum-AI applications, particularly as local financial institutions seek to modernize their risk management practices and attract global capital flows. The interplay of regional regulatory environments, technology adoption rates, and market maturity will continue to shape the competitive landscape and growth trajectory of this dynamic market.

Component Analysis

The Quantum-AI Option Portfolio Hedging market is segmented by component into software, hardware, and services, each playing a pivotal role in the ecosystem's development and deployment. Software solutions represent the backbone of this market, encompassing advanced analytics platforms, quantum algorithm libraries, and AI-driven decision support systems. These software offerings enable financial institutions to simulate complex option pricing models, optimize hedging strategies, and automate risk

Volatility connectedness among ESG, conventional and shariah-compliant indices: Evidence from Quantile VAR analysis

The quantitative investment market is experiencing robust growth, driven by the increasing adoption of advanced analytical techniques and algorithmic trading strategies. The market's sophistication is reflected in its segmentation, encompassing various investment types (stocks, bonds, futures, options) and employing diverse strategies (trend judgment, volatility judgment). The substantial market size, estimated at $500 billion in 2025, demonstrates the significant capital allocated to these strategies. A Compound Annual Growth Rate (CAGR) of 12% is projected from 2025 to 2033, suggesting a market value exceeding $1.5 trillion by 2033. This growth is fueled by several factors: the availability of vast datasets, advancements in machine learning and artificial intelligence, and a growing need for efficient portfolio management in increasingly complex financial markets. Furthermore, the rise of fintech and the proliferation of high-frequency trading further accelerate market expansion. However, the quantitative investment market is not without challenges. Regulatory scrutiny, particularly regarding algorithmic trading's potential for market manipulation and systemic risk, poses a significant restraint. The high initial investment costs associated with developing and maintaining sophisticated quantitative models also present a barrier to entry for smaller firms. Despite these challenges, the long-term outlook for quantitative investment remains positive, driven by ongoing technological innovation and the inherent demand for superior risk-adjusted returns in the financial industry. The competitive landscape is dominated by established giants like Millennium Management and Bridgewater Associates alongside emerging players in Asia, indicating a globally distributed and dynamic market.

The results of the Kruskal-Wallis test on the optimal portfolio weights and hedge ratio of each portfolio.

AI-Driven Energy Hedging Platform Market Outlook

According to our latest research, the AI-Driven Energy Hedging Platform market size globally reached USD 1.34 billion in 2024, reflecting robust adoption across the energy sector. The market is expected to grow at a compound annual growth rate (CAGR) of 18.2% from 2025 to 2033, reaching an estimated USD 6.08 billion by 2033. This growth is being propelled by rising volatility in energy prices, increased digitalization in the energy sector, and the need for advanced risk management solutions leveraging artificial intelligence.

The primary growth factor for the AI-Driven Energy Hedging Platform market is the escalating complexity and volatility in global energy markets. Geopolitical tensions, supply chain disruptions, and the transition towards renewable energy sources have introduced unprecedented price fluctuations. These dynamics make traditional hedging strategies less effective, pushing energy companies and traders to adopt AI-powered platforms capable of processing vast datasets and generating real-time insights. The ability of AI to identify patterns, forecast prices, and optimize portfolios offers a competitive advantage, making it an indispensable tool for energy market participants striving to mitigate risks and maximize returns.

Another significant driver stems from the digital transformation initiatives undertaken by utilities, oil & gas companies, and large industrial consumers. As organizations aim to modernize their operations, the integration of AI-driven analytics within energy trading and procurement processes is becoming standard practice. These platforms enable seamless automation of complex hedging decisions, improve operational efficiency, and ensure compliance with increasingly stringent regulatory frameworks. The scalability and adaptability of cloud-based AI solutions, in particular, are accelerating adoption among both large enterprises and smaller market players who seek to remain agile in a rapidly changing environment.

Furthermore, the increasing focus on sustainability and renewable energy integration is reshaping the risk landscape for energy stakeholders. As intermittent sources such as wind and solar become more prominent, the unpredictability of supply necessitates sophisticated forecasting and risk management tools. AI-driven energy hedging platforms are uniquely positioned to address these challenges by incorporating weather data, demand forecasts, and market signals to provide actionable recommendations. This capability not only supports financial performance but also aligns with broader environmental, social, and governance (ESG) objectives, further fueling market expansion.

From a regional perspective, North America currently leads the AI-Driven Energy Hedging Platform market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The advanced energy trading infrastructure, high adoption of AI technologies, and the presence of major industry players contribute to North America's dominance. Meanwhile, Europe is experiencing rapid growth due to aggressive renewable energy targets and regulatory support for digital innovation in the energy sector. Asia Pacific is emerging as a high-growth market, driven by expanding energy demand, urbanization, and increasing investment in smart grid technologies. Latin America and the Middle East & Africa are also witnessing gradual adoption, albeit at a slower pace, as digital transformation initiatives gain momentum.

Component Analysis

The Component segment of the AI-Driven Energy Hedging Platform market is bifurcated into Software and Services, each playing a pivotal role in the overall ecosystem. Software solutions form the backbone of AI-driven platforms, providing advanced analytics, machine learning models, and user interfaces that facilitate risk management, price forecasting, and portfolio optimization. These software offerings are designed to integrate seamlessly with existing energy managemen

Download Historical Yen Volatility Index Indicies Data. CQG daily, 1 minute, tick, and level 1 data from 1899.

Download Historical 10yr T Note Volatility Index Indicies Data. CQG daily, 1 minute, tick, and level 1 data from 1899.

Taiwan Life Insurance:Ex&Hedging GL,Volatility on Fx ValuationReserve:Total data was reported at -49,900.000 NTD mn in Mar 2025. This records a decrease from the previous number of -47,200.000 NTD mn for Feb 2025. Taiwan Life Insurance:Ex&Hedging GL,Volatility on Fx ValuationReserve:Total data is updated monthly, averaging -97,850.000 NTD mn from Jan 2020 (Median) to Mar 2025, with 62 observations. The data reached an all-time high of 102,400.000 NTD mn in Oct 2022 and a record low of -302,900.000 NTD mn in Dec 2024. Taiwan Life Insurance:Ex&Hedging GL,Volatility on Fx ValuationReserve:Total data remains active status in CEIC and is reported by Insurance Bureau, Financial Supervisory Commission. The data is categorized under Global Database’s Taiwan – Table TW.Z035: Insurance Statistics: Profit, Loss, Exchange Gain & Loss and Premium Revenue.

The Goodness-of-fit results and standardized residual diagnostics for the three portfolios.

Quantum-AI Volatility Surfaces Generation Market Outlook

According to our latest research, the Quantum-AI Volatility Surfaces Generation market size globally stood at USD 1.37 billion in 2024, driven by rapid advancements in quantum computing and artificial intelligence integration within the financial sector. The market is projected to expand at a robust CAGR of 31.8% from 2025 to 2033, reaching a forecasted value of USD 14.67 billion by 2033. This exponential growth is underpinned by the increasing demand for advanced risk analytics, real-time pricing, and the need for high-precision volatility modeling in trading and investment strategies.

The primary growth factor for the Quantum-AI Volatility Surfaces Generation market is the escalating complexity of financial instruments and the corresponding need for sophisticated risk management solutions. Financial institutions are increasingly leveraging quantum computing and AI to generate more accurate volatility surfaces, which are essential for pricing derivatives, managing portfolios, and optimizing trading strategies. The ability of quantum-AI systems to process massive datasets and uncover subtle market patterns far surpasses traditional computational methods, enabling institutions to gain a competitive edge in fast-moving markets. This transformation is further fueled by the rising adoption of algorithmic trading and the proliferation of complex financial derivatives, both of which necessitate precise and dynamic volatility modeling.

Another significant driver propelling the market is the continuous evolution of quantum hardware and AI-powered software platforms. Major technology vendors are investing heavily in research and development to create scalable, commercially viable quantum processors and AI algorithms tailored for financial applications. This has led to an ecosystem where financial institutions, hedge funds, and asset management firms can access cutting-edge volatility surfaces generation tools through both on-premises and cloud-based deployments. The integration of quantum-AI solutions is also reducing computational costs and enhancing the speed and reliability of risk analytics, making these technologies increasingly accessible to a broader range of market participants.

The expanding regulatory landscape and the growing emphasis on transparency and compliance in financial markets are also catalyzing the adoption of Quantum-AI Volatility Surfaces Generation solutions. Regulators are demanding more robust risk assessment and reporting mechanisms, particularly in the wake of market volatility and systemic shocks. Quantum-AI technologies provide financial organizations with the tools to not only meet these regulatory requirements but also to anticipate and mitigate risks proactively. The convergence of regulatory pressures, technological innovation, and the strategic imperative for real-time analytics is expected to sustain the market’s high growth trajectory throughout the forecast period.

From a regional perspective, North America currently leads the global Quantum-AI Volatility Surfaces Generation market, accounting for a significant share due to the presence of major financial hubs and early adoption of quantum computing technologies. Europe follows closely, driven by its strong financial services sector and supportive regulatory frameworks. Asia Pacific is emerging as a high-growth region, propelled by rapid digitalization, expanding capital markets, and increasing investments in quantum-AI research. Meanwhile, Latin America and the Middle East & Africa are gradually integrating these advanced solutions, albeit at a slower pace, constrained by infrastructural and regulatory challenges. Overall, the global market is poised for widespread adoption as institutions worldwide recognize the transformative potential of quantum-AI in volatility modeling and risk management.

Component Analysis

The Quantum-AI Volatility Surfaces Generation market by component is segmented into Software, Hardware, and Services<

This study employs a bivariate EGARCH model to examine the Taiwan Futures Exchange’s regular and after-hours trading, focusing on the critical aspects of spillover and expiration effects, as well as volatility clustering and asymmetry. The objective of this study is to observe the impact on the trading sessions in Taiwan by the influences of the European and American markets, focusing on the essential roles of the price discovery function and risk disclosure effectiveness of the regular hours trading. This research is imperative considering the increasing interconnectedness of global financial markets and the need for comprehensive risk assessment for investment strategies. It also examines the hedging behavior of after-hours traders, thereby aiming to contribute to pre-investment analysis by future investors. This examination is vital for understanding the dynamics of after-hours trading and its influence on market stability. Results indicate price continuity between both trading sessions, with regular trading often determining after-hours price ranges. Consequently, after-hours price changes can inform regular trading decisions. This finding highlights the importance of after-hours trading for shaping market expectations. Significant profit potential exists in after-hours trading open interest, which serves speculative and hedging purposes. While regular trading volatility influences after-hours trading, the reverse is not true. This suggests Taiwan market information poses a higher risk impact than European and American market data, emphasizing the unique position of the Taiwan market in the global financial ecosystem. After-hours trading volatility reflects the absorption of international market information and plays a crucial role in advance revelation of risks. This underscores the importance of after-hours trading in global risk management and strategy formulation.

Focus, in the past four decades, has been obtaining closed-form expressions for the noarbitrage prices and hedges of modified versions of the European options, allowing the dynamic of the underlying assets to have non-constant parameters. In this paper, we obtain a closed-form expression for the price and hedge of an up-and-out European barrier option, assuming that the volatility in the dynamic of the risky asset is an arbitrary deterministic function of time. Setting a constant volatility, the formulas recover the Black and Scholes results, which suggestsminimum computational effort. We introduce a novel concept of relative standard deviation for measuring the exposure of the practitioner to risk (enforced by a strategy). The notion that is found in the literature is different and looses the correct physical interpretation. The measure serves aiding the practitioner to adjust the number of rebalances during the option's lifetime.

This article provides a data-driven analysis of the volatility risk premium, using tools from high-frequency finance and Big Data analytics. We argue that the volatility risk premium, loosely defined as the difference between realized and implied volatility, can best be understood when viewed as a systematically priced bias. We first use ultra-high-frequency transaction data on SPDRs and a novel approach for estimating integrated volatility on the frequency domain to compute realized volatility. From that we subtract the daily VIX, our measure of implied volatility, to construct a time series of the volatility risk premium. To identify the factors behind the volatility risk premium as a priced bias, we decompose it into magnitude and direction. We find compelling evidence that the magnitude of the deviation of the realized volatility from implied volatility represents supply and demand imbalances in the market for hedging tail risk. It is difficult to conclusively accept the hypothesis that the direction or sign of the volatility risk premium reflects expectations about future levels of volatility. However, evidence supports the hypothesis that the sign of the volatility risk premium is indicative of gains or losses on a delta-hedged portfolio.