The Dynamic Influence of Uncertainty on Sector Equity Funds: A Time-Frequency Analysis of Oil, Gold, and Market Volatility

doi:10.21203/rs.3.rs-5323657/v1

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The Dynamic Influence of Uncertainty on Sector Equity Funds: A Time-Frequency Analysis of Oil, Gold, and Market Volatility

https://doi.org/10.21203/rs.3.rs-5323657/v1

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This research employs a time-frequency approach to explore the interrelations among US sector equity ETFs, oil, gold, the overall stock market, and various uncertainty indicators across different time horizons from 2019 to 2023. It utilizes a frequency-dependent spillover index and strategies for portfolio hedging to construct its findings. The analysis reveals that the market's anticipation of volatility in the US stock market for the next 30 days, as measured by the VIX, significantly influences US sector equity ETFs over both short and long durations. This influence is succeeded by expectations of oil price volatility, denoted by OVX. Among the indicators of uncertainty, the impact of US economic policy uncertainty on sector ETFs is found to be minimal. Comparatively, oil exhibits a more pronounced impact on sector ETFs than gold, across both observed time frames. The interactions among sector ETFs, gold, oil, and the uncertainty measures display a non-uniform nature, being more pronounced in the short term and intensifying during periods of financial distress and economic instability. From a portfolio hedging perspective, oil emerges as a superior hedge against sector ETF volatility in both the immediate and extended terms, with the Consumer Staples ETF showing the greatest hedging efficiency.

Business and commerce/Economics

Business and commerce/Finance

Connectedness

hedging effectiveness

Sector ETFs

Frequency spillovers

Gold and oil

There has been some stability in the global economy recently, but that is beginning to change. Managing Director Kristalina Georgieva of the World Economic Fund (IMF) and World Bank gave a policy address at the 2023 annual conference of the two organizations on October 6th.

Unusual developments in recent times have contributed to increased political and economic ambiguity, contributing to a greater spread of financial difficulties throughout global markets. Bruit, trade tensions between the U.S. and China, the COVID-19 pandemic, Russia's invasion of Ukraine, and the cost-of-living crisis that followed were all significant incidents that occurred in quick succession, leading to a long period of elevated ambiguity (Tiwari et al., 2020). Several threats to global markets have emerged as a direct consequence of the conflict. These include, but are not limited to, higher interest rates on credit from other countries, rising inflation, unstable commodity markets, and limited access to markets. As a result of the COVID-19 pandemic, the dangers have made the already severe problems even worse.

Furthermore, constraints and weaknesses would be caused by the sudden shift from a long time of low-interest rates to rapid rises in interest rates, which are needed to control unusually excessive inflation. At this very unpredictable time, it is crucial to construct portfolios that can weather the storm by carefully selecting assets to help achieve this goal. To make educated investment choices and mitigate risk in the face of significant global uncertainty, it is crucial to understand the magnitude and nature of spillover impacts (Salisu et al., 2021). Because U.S. exchange-traded funds (ETFs) are becoming more critical in the financial markets and market uncertainty is rising, it is critical to look at how ETFs and uncertainty indexes interact. Investors, portfolio managers, market participants, and policymakers may all benefit from this research. The goal is to ensure the financial system is stable and to assist them in comprehending how systemic risk spreads. These factors have a massive impact on risk management and portfolio allocation.

A few papers, including (Ullah et al., 2021) and (Segura-Salazar et al., 2019), are the works that have attempted to look at the sources that could accomplish the job of reducing uncertainty. These advantages include affordable transaction fees, dispersed assets, clarity, and trading opportunities (Chudziak, 2023). These features mean that it is easier to diversify, manage cash, and apply hedging strategies. (Sardianou & Kostakis, 2020) have postulated that a rapid response may occur and may manifest in changes made to ETF investments because such investments are liquid. This the why the transmission of data through this channel is more than that of less frequently transmitted data (S. Chen & Liu, 2022). Despite the high level of uncertainty around the unusual COVID-19 problem, ETFs have shown to be resilient (Y. S. Wang & Chueh, 2013).While significant markets' liquidity dropped significantly during the selloff, exchange-traded funds (ETFs) kept trading effectively. They were crucial in helping investors set prices by clarifying their risk tolerance levels (BlackRock, 2020). In times of heightened uncertainty due to unanticipated occurrences, ETFs provide investors with fresh investing options. The correlation between Cods and other uncertainty indices has been the subject of microscopic study.

The research on the topic examines how socioeconomic policy uncertainty indexes and other similar indices communicate with various asset classes, including commodity and stock markets (Norouzi & Fani, 2020). The links between the U.I. and ETFs markets still need to be well-addressed. Both the short-term and long-term transfer of variance from the indicated variance index (VIX) to U.S. (Y. J. Zhang & Wei, 2010). (Kang et al., 2017) shows that ETF returns, which consider international trading, are adversely affected by policy uncertainty in economics, fiscal policy, and money. However, the impact of these concerns on ETF returns is market-dependent. Different exchange-traded funds (ETFs) react differently to fluctuations in trade policy. Focusing on the time-frequency domain. investigate how COVID-induced sentiment affects spillovers. The study's authors look at many investor-related psychological and behavioral aspects. Their research demonstrates that the interconnection of variability in U.S. ETFs significantly correlates with investor sentiment. (M. Arfaoui & Ben Rejeb, 2017) examine the relationship between EPU and other U.S. international Funds from 2012 to 2018, looking at data from 2012 to 2018. Even after accounting for returns and macroeconomic factors, the data show that EPU substantially impacts ETFs. Although these results are essential, more evidence is still needed to link the peculiarities of the U.S. financial sector, energy marketplace, and economic policy to the interplay between intraday variability across U.S. ETFs. More and more asset classes, such as stocks, bonds, commodities, and indices, are tracked by ETFs as their popularity continues to (Yaya et al., 2016). Therefore, volatility expectations for the U.S. stock market, crude oil, and gold affect ETFs. In addition, most of the work on spillover effects is based on the assumption that asset returns follow a normal distribution and that volatility is a good indicator of risk. Still, it only touches on a subset of the risk concept, which could be improved during trying times when the assignment of returns exhibits very atypical behavior. In order to provide a more precise evaluation of spillovers related to exceptional occurrences, it is necessary to include higher-order moments.

This research analyses how realized estimators such as jump risks, skewness, kurtosis, and volatility affect U.S. ETFs and factors connected to economic and financial uncertainty. The goal is to resolve the identified shortcomings and fill in the blanks in previous research. The goal is to understand better how different risk assessments are related to the breakdown of ETFs and ambiguity indices.

There are a lot of unanswered questions about the transfer of volatility across ETFs and unknown factors, even though some research is accessible. Several essential questions must be answered. Can you explain the connection between U.S. sector ETFs and concern indices? Do exchange-traded funds (ETFs) and uncertainty indices interact only via actual risk channels? Are they related in any way via jump channels and higher moments? Do unfavorable market circumstances impact these spillover effects? Who are the primary receivers, and which ETFs and U.I.s serve as primary conduits for transmitting realized volatility, other metrics of volatility, and abrupt changes? Do predictions for the U.S. stock market's volatility or the returns on oil and gold have a more significant impact on ETFs?

Our research aims to accomplish two main things: first, we want to know how important it is to look at the bias and kurtosis of the daily returns shipping for ETFs and uncertainty factors, specifically realized-volatilities derived from frequent data, and how useful they are for hedging analysis. Second, we want to know how ETF markets and uncertainty indexes are connected to learn how effects are transmitted via more significant moments and leaps channels in a time-varying scheme. By doing so, we may learn how various crises alter the influence of spillover effects.

We utilize a large dataset containing intraday data gathered at 5-minute periods from around the globe to conduct our research (Jain & Biswal, 2016). There are a lot of uncertainty indexes and data on ten U.S. ETFs in the collection. In order to examine the returns of these assets, we use this data to calculate measures like actual fluctuation, skewness, kurtosis and realized leaps. The time frame of the research is December 2016 through April 2022. The current study had four stages. We begin by calculating the exact variability of U.S. ETFs and uncertain indices using the intraday data collected every 5 minutes. We also calculate kurtosis and skewness to assess the asymmetry of the conditional asset return distribution, and we find the continuous and extreme elements of the cubic variance. Leaps' effects are linked to these metrics. We deploy a time-varying parameter vector self-regressive (TVP-VAR) version developed by (Raza et al., 2016). As an extra to the rolling-window estimate approach used by (Sheikh et al., 2020) to find spillovers between ETFs and fluctuations of their unknown components. By avoiding data loss and the need to set the rolling window size arbitrarily, the proposed dynamic connectedness index improves upon the technique (Li et al., 2021). A network-based approach was also implemented to improve further the efficiency of collecting and showing structural changes. This study is the first effort to investigate, in the setting of significant shocks, the changing transmission of different financial and economic sources of ambiguity to U.S. sector ETFs, considering both frequency and time characteristics. Our main goal is to assess the value of various portfolio components and the efficacy of hedging. Based on these findings, we may advise customers to adjust their portfolio strategy to deal with unusual risks.

The primary results indicate that skewness and kurtosis transmission are statistically significant in addition to volatility. Not only that, but research shows that when financial instability and economic uncertainty are on the rise, spillovers have an even more significant impact. Our directional connectedness research discovered that U.S. sector ETFs are most strongly associated with market expectations of 30-day fluctuation in oil prices and the U.S. stock market. As seen by the Minimum Connection Portfolio (Mop) method's superior Sharpe ratio, it is crucial to comprehend risk sharing in order to construct effective portfolios. These results provide buyers and risk managers with correct information for thorough assessments of portfolio risk control, especially in the face of multiple shocks (Jin et al., 2019). Policymakers tasked with reducing the negative implications of risk transfer and maintaining financial stability may also find this helpful study.

The following is a breakdown of the remaining sections of this research. While Section 3 delves into the approach, Section 2 provides a synopsis of the existing literature. A comprehensive evaluation of the data and associated descriptive statistics is provided in Section 4. A thorough analysis and assessment of the findings are presented in Section 5. Section 6, which discusses important policy implications, concludes the article

Researchers have explored the interaction between the factors required to assess dependence before, during, and after periods of stability and turmoil and how these factors contribute to the amplification of market volatility (Radu et al., 2021). A clear understanding of the data flow patterns from one sector to another is very important, especially during moments of increased global volatility. There is greater interconnectedness of knowledge across segments of assets than there was before the 2008 global meltdown and the Eurozone sovereign debt calamity. Portfolio investors and managers are in search of new ideas that would help them decrease their risks by following adequate means of mitigating large risks. Numerous papers researched how US ETFs, along with other assets, perform regarding effects and spillovers in the context of ETFs. It has been used in various such research, for example, (Y.-C. Wang & Chen, 2024). Given the implications to such areas as asset price determination, risk assessment, and liquidity this research is central to investors, investment managers as well as regulators. These papers conclude that ETFs as efficient forms of diversification that enable an investor to construct a fully diversified portfolio across the globe without having to go through the troubles of investing in foreign markets.

(Jatuporn, 2024; Radu et al., 2021) themselves are only two of the many papers that have sought to quantify the effectiveness of diversity and DI in terms of the increasing level of data efficiency in ETFs in the context of the COVID-19 pandemic. With COVID-19 incidences fluctuating in a short time, investors can, within the shortest time, change the proportion of their portfolio using ETFs. As the pandemic of interest intensifies (Abakah et al., 2023), investigate the effectiveness of stock ETFs to provide information relative to the market indices on which they track. As a result of market fluctuation caused by the COVID outbreak, the informational efficiency of ETFs is lower than before. Transmission of effects and interconnections between ETFs and other securities has been under investigation in previous studies (Rahman et al., 2022). The COVID-19 outbreak has slowed down the ability to address risks and the rate of diffusion of their consequences across various sectors.

Although research on exchange-traded funds (ETFs) has been expanding, more needs to be established about the dynamic transmission that forms when ETFs engage with uncertainty (Aslam et al., 2023). The relation is now stronger, and the global financial markets are more interconnected and exposed to economic and financing volatility occasioned by COVID-19 and the crisis in Ukraine, according to (Y. Wang et al., 2023). Specifically, there is a need for more research regarding the ETFs and the uncertainty factors spillovers. Certain research endeavors have tried to analyze the issue of hedging and diversification with the help of ETFs (N. Arfaoui et al., 2023). Uncertainty in sending information to equities and strategic goods, according to (Cerca et al., 2024). However, there is a low demand for the consideration of the effects of ETFs and the unknowns (Musango, 2022). There exists no empirical evidence in the previous literature relating the intraday volatility of US sector ETFs to other finance and economic matters. The majority of the literature deals with the starting and endpoints of alterations in the characteristics of the asset return distribution with an accent on the value of volatility and return (Acheampong, 2018).

Most of the research undertaken on the transfer of risks needs to explore the process beyond the initial two seconds. Most of the existing studies have focused on the stock, currency, and commodities markets. High kurtosis and large skewness may be due to many great values arguable by exceptional occurrences and unforeseen events. According to (Pan et al., 2024), claiming that returns follow the normal distribution is unrealistic. Therefore, there are substantive options than to limit the spillover study to only the first two seconds. As indicated by (Jena et al., 2021), more amazing values such as skewness and kurtosis may shed light on the asymmetrical risk and worse situation. To extend the literature on ETFs on hedging and growth and the association between ETFs and ambiguity, this work analyzes certain variables of uncertainty in finance, the energy market, and economics, which have yet to be investigated in prior studies. The study seeks to assess the effect of these variables in situations of constant shocks with regard to US industry ETFs. Since we hypothesize that the price dynamics of the selected ETF will differ due to the impact of various types of economic and financial risks, we will consider the rules’ ambiguity index proposed by (Fu & Ng, 2021) and the US equity marketplace fluctuation proposed by (Benz-Saliasi, 2020). The US market is the biggest share exchange in the world, and therefore, investing in the market is one of the preferred choices. For instance, raw oil and platinum are classified in many instances as strategic commodities. Gold is the kind of money that has been regarded as a medium, which preserves and safeguards wealth and is a measure against risk.

Altogether, this work is devoted to a particular branch of knowledge, which elaborates on the importance of advanced communications concerning the dispersion of buyers. The present analysis continues the investigation of outcomes for higher-order instances, although the following research questions are raised more explicitly: (Zhao et al., 2023), brown power, and the dissemination of technological shares for shares and goods (Bárcena-Ruiz et al., 2023). Consequently, this research seeks to complement existing studies by focusing on the US stock marketplace fluctuation index, also referred to as the Crude Oil Volatility Index, the Gold Volatility Index, and the role played by ambiguity in economic policy in an attempt to isolate ETFs’ volatility. More so, it investigates the interaction and carryover of return during crisis periods in the context of US sector ETFs.

Based on the above debate, research evaluates the following hypotheses:

H 1

The interdependencies between ETFs and fear indices are fluid and prone to temporal variation.

H 2

The transmission and reception of impacts via the network rely upon the rate under investigation, which emphasizes the presence of asymmetric repercussions.

H 3

Exchange-traded funds (ETFs) are influenced by changes in macroeconomic uncertainties and the anticipated levels of instability in the US market for stocks, gasoline, and silver prices, although to different degrees.

H 4

The repercussions of problems are likely to escalate throughout times of turmoil.

H 5

In addition to the transmission of fluctuation, there are significant transmissions of shifts, kurtosis, and leaps.

3.1. Frequency spillovers method

Our primary aim is to analyze the impact of AI coins and AI Funds on other markets via tail-based ripples. We apply the quantile-VAR technique (Cheng et al., 2023).developed for a tail spill study. The tail-spillover vectors are calculated using the infinity order-based scalar rolling requirements of QVAR.

$$\:{y}_{t}=\mu\:\left(\tau\:\right)+{\sum\:}_{j}^{p}\:{{\Phi\:}}_{j}\left(\tau\:\right){y}_{t-j}+{u}_{t}\left(\tau\:\right)=\mu\:\left(\tau\:\right)+{\sum\:}_{i=0}^{\infty\:}\:{{\Omega\:}}_{i}\left(\tau\:\right){u}_{t-i}$$

The specification of the generalized forecasting variance decomposition (GFEVD) using a prediction window is based on the works of Psarian (Reboredo, 2013).

$$\:{{\Theta\:}}_{ij}^{g}\left(H\right)=\frac{\sum\:{\left(\tau\:\right)}_{jj}^{-1}{\sum\:}_{h=0}^{H-1}\:{\left({e}_{i}^{{\prime\:}}{{\Omega\:}}_{h}\left(\tau\:\right)\sum\:\left(\tau\:\right){e}_{j}\right)}^{2}}{{\sum\:}_{h=0}^{H-1}\:\left({e}_{i}^{{\prime\:}}{{\Omega\:}}_{h}\left(\tau\:\right)\sum\:\left(\tau\:\right){{\Omega\:}}_{h}{\left(\tau\:\right)}^{{\prime\:}}{e}_{i}\right)}$$

The symbol $\:{e}_{i}$ "where" denotes a null vector with a unity value in $\:\text{i}$ the nth place. The de-composition vector reflects the normalizing of items.

$$\:{\overrightarrow{{\Theta\:}}}_{ij}^{g}\left(H\right)=\frac{{{\Theta\:}}_{ij}^{g}\left(H\right)}{{\sum\:}_{j=1}^{k}\:{{\Theta\:}}_{ij}^{g}\left(H\right)},\text{w}\text{i}\text{t}\text{h}{\sum\:}_{j=1}^{k}\:{\overrightarrow{{\Theta\:}}}_{ij}^{g}.1\text{a}\text{n}\text{d}{\sum\:}_{i,j=1}^{k}\:{\overrightarrow{{\Theta\:}}}_{ij}^{g}\left(H\right)=1$$

The connectivity measurements depending on the Generalized Forecast Error Variance Decomposition (GFEVD) are outlined below, using a technique proposed by Blum and Yilmaz (2012):

$$\:{TO}_{j,t}={\sum\:}_{i=1,i\ne\:j}^{k}\:{\overrightarrow{{\Theta\:}}}_{ij,t}^{g}\left(H\right),$$

$$\:{FROM}_{j,t}={\sum\:}_{i=1,i\ne\:j}^{k}\:{\overrightarrow{{\Theta\:}}}_{ji,t}^{g}\left(H\right)$$

$$\:{NET}_{j,t}={TO}_{j,t}-{FROM}_{j,t},$$

$$\:{TCI}_{t}=\frac{{\sum\:}_{i,j=1,i\ne\:j}^{k}\:{\overrightarrow{{\Theta\:}}}_{ij}^{g}\left(H\right)}{k-1}$$

$$\:\text{v}\text{e}\text{c}\left({B}_{t}\right)=\text{v}\text{e}\text{c}\left({B}_{t-1}\right)+{\nu\:}_{t},{\nu\:}_{t}\sim\:N\left(0,{R}_{t}\right)$$

$$\:{y}_{t}=\mu\:+{\sum\:}_{j}^{p}{{\Phi\:}}_{j}{y}_{t-j}+{\epsilon\:}_{t}=\mu\:+{\sum\:}_{i=0}^{{\infty\:}}{{\Psi\:}}_{i}{\epsilon\:}_{t-i}$$

$$\:{C}_{ij}^{g}\left(H\right)=\frac{{{\Sigma\:}}_{jj}^{-1}{{\sum\:}_{h=0}^{H-1}\left({e}_{i}^{{\prime\:}}{{\Psi\:}}_{h}{\Sigma\:}{e}_{j}\right)}^{2}}{{\sum\:}_{h=0}^{H-1}\left({e}_{i}^{{\prime\:}}{{\Psi\:}}_{h}\sum\:{{\Psi\:}}_{h}^{{\prime\:}}{e}_{i}\right)}$$

$\:{TO}_{j,t}$ "indicates" refers to the relationship between an indicator and factor i, while "represents" denotes the influence of I. Illustrates the difference between 'TO' and 'FROM,' where a negative (favorable) value indicates the receiver (the transmitter) of overflow $\:{FROM}_{j,t}$. Denotes the mean degree of overall connectivity $\:{NET}_{j,t}$. In addition to conducting measure connectivity evaluation, we calculate optimum (F. Liu et al., 2023) considering conditions on instability and volatility obtained from the estimations of the DCC-GARCH paradigm.

3.3. Data and preliminary analysis

The primary objective of this research is to examine the degree of connectivity among more conventional assets, especially those on the furthest ends of the spectrum and emerging industries like AI coins and AI ETFs. To achieve this, we use the daily data of several AI tokens (OCEAN- Ocean Procedure, FET- Fetch.ai, AGIX- Singularity NET), three AI exchange-traded funds (ETFs) (ROBT- Firstly Trust the NYSE robot and AI ETF, IRBO- its shares Robotics, also and fabricated Artificial intelligence Multisector ETF, ROBO- Robot Global AI and Robotics Index ETF), and a variety of other asset classes, such as Gold, bitcoin, Oil, MSCI world, REIT- the Jones All REIT Ranking, Bonds- PIMCO Funding Grade Business Bond Index, and USDI- the United States currency Index. The following scholarly articles are consulted to choose different asset types: While (R. Chen & Xu, 2019). We got the AI token data from coinmarketcap.com and investment data from a website called .4. The given time frame begins on August 1, 2019, and ends on January 28, 2023.

The study's assets, including AI tokens and AI ETFs, are summarized in Table 1. Every market's average return is positive except oil and bonds. According to the findings, AI tokens outperform AI ETFs and other traditional assets regarding average return. Compared to other assets, AI tokens, like AI ETFs, have the highest average deviation values, suggesting they are the most volatile. All assets except OCEAN, FET connect, and USDI show left skewness, as shown by their lower skewness scores, according to the skewness test findings. There is a noticeable amount of kurtosis in all the examined series, indicating that the return series have all been distributed with heavy tails. According to the results of the Jarque-Bera test, all return series do not follow a normal distribution. At the 1% significance level, the ADF unit roots test rejects the unproven theory of a unit root, and the findings show that each of the return series is linear.

Table 1

Overview of data.
	Mean	Max	Min	S.D.	Skew	Kurt	J.B.	ADF
DEEP-SEA	0.0082	0.662	−0.646	0.006	0.608	8.406	2220.4^a	–42.244^a
FET	0.0066	0.446	−0.608	0.000	0.240	8.620	866.2^a	−20.048^a
AGIX	0.0224	6.464	−0.866	0.208	20.660	640.448	20648826.0^a	–44.406^a
ROBT	0.0004	0.008	−0.220	0.028	−0.640	0.880	2842.8^a	−0.288^a
IRBO	0.0004	0.084	−0.202	0.020	−0.260	6.462	448.6^a	−20.888^a
ROBO	0.0006	0.200	−0.222	0.028	−0.280	8.620	2224.8^a	−8.024^a
MEDAL	0.0004	0.046	−0.068	0.020	−0.424	6.028	462.2^a	−28.464^a
BTC	0.0020	0.222	−0.482	0.044	−0.626	22.240	2486.2^a	−40.488^a
FAT	−0.0028	0.642	−4.020	0.220	−20.826	488.220	8664680.0^a	−22.066^a
PARITY	0.0004	0.088	−0.000	0.024	−0.860	24.640	6060.0^a	−8.624^a
REIT	0.0002	0.000	−0.280	0.028	−2.266	20.466	22288.6^a	−26.466^a
OATH	−0.0002	0.082	−0.060	0.006	0.400	44.848	48222.8^a	−22.846^a
USDI	0.0000	0.020	−0.022	0.006	0.006	6.462	202.6^a	−28.042^a

The multivariate conditional correlation among average wealth, AI ETFs, and AI coins is reported in Table 2. Our findings indicate a favorable correlation between AI coins, AI Etf, and more conventional resources, except the USDI currency indexes. A solid, beneficial link exists between AI ETFs and other assets, particularly the Equity-AI and REIT-AI ETF pairings, and a weak negative correlation among oil-AI Funds. In light of these findings, there are better choices than AI ETFs for people looking to diversify their holdings or protect themselves from more conventional market dangers (Bouri et al., 2017). Figure 1 indicates the integration levels of various AI Tokens with different categories of assets. Among them, there is only a weak beneficial correlation with all other assets of the AI token, except the USDI-AI token rate. This implies that AI tokens are novel assets that might be useful while investing as hedges or diversifiers to other traditional investments. In real life, investors who wish to outrun risks and diversify their portfolio may be able to use AI tokens as insurance against traditional investments. That is if their investment portfolio is focused on stocks, bonds, or commodities, adding the AI tokens might decrease portfolio variability because these tokens are not significantly correlated with the three mainstream asset classes. This diversification benefit is important especially in managing the systemic risk while at the same time being able to attain better returns in the particular volatile markets. Nevertheless, the USDI-AI token may be an exception due to the absence of positive interactions, meaning that not all of the conventional assets will have the same response when combined with the AI tokens. Thus, when choosing and analyzing correlations, it is crucial to achieve the best risk/return ratio of the portfolio.

However, in terms of practice, the structure of Fig. 1 demonstrated that correlations between different classes of assets can be applied to decision-making for investments. Relative to the rest of the tokens, the characteristics and patterns of AI tokens’ interactions with other assets help investors understand which assets should be combined in order to reach the goals of diversification and risk management. The information illustrated in Fig. 1 implies that expansion into new kinds of assets, such as AI tokens, may hold certain advantages. However, it also suggests that understanding how these assets are connected to conventional investments and how they should be integrated to fully capitalize on their potential is crucial.

Table 2

Connectivity without conditions
	SEA	FET	AGIX	ROBT	IRBO	ROBO	GILDED	BTC	FAT	PARITY	REIT	WORD
SEA	2.000
FET	0.480	2.000
AGIX	0.204	0.220	2.000
ROBT	0.208	0.406	0.244	2.000
IRBO	0.400	0.400	0.248	0.046	2.000
ROBO	0.404	0.408	0.246	0.066	0.044	2.000
GILDED	0.082	0.008	0.024	0.282	0.260	0.266	2.000
BTC	0.620	0.642	0.262	0.400	0.440	0.402	0.266	2.000
FAT	0.048	0.040	0.026	0.000	0.086	0.080	0.024	0.064	2.000
PARITY	0.420	0.406	0.244	0.006	0.866	0.028	0.282	0.408	0.228	2.000
REIT	0.262	0.264	0.228	0.826	0.646	0.828	0.284	0.202	0.220	0.820	2.000
WORD	0.048	0.002	0.020	0.446	0.424	0.424	0.448	0.248	0.064	0.428	0.442	2.000
USDI	−0.048	−0.208	−0.064	−0.286	−0.282	−0.424	−0.424	−0.266	−0.042	−0.406	−0.20

Figure 2 shows how the value of conventional wealth, AI ETFs, and AI coins has changed. Frogman coins increased constantly from the start of the study until the first third of 2022. In mid-2021, they suddenly dropped. Following that day, the cost of AI tokens rose until it peaked again near the close of 2022. Then, it fell and hit its lowest value after the era. The graphs show that the value of AI Funds fell somewhat in the initial half of 2020 but then continued their upward trend, reaching a peak in the second part of 2021 and staying there till the end of 2021. From this point forward, every AI ETF saw a steady decline throughout the remainder of the sample period. In the initial half of 2021, coinciding with the COVID-19 pandemic, oil, equities, and bonds costs all took a nosedive. Everything save the oil price took another nosedive in the first year of twenty-two. Shows how returns have changed throughout time. In the initial quarter of 2021, when the COVID-19 health crisis began, asset prices were very volatile, as seen by most return types' highly volatile returned pathways.

4.1. Static quantile connectedness

To examine the high correlation of traditional assets or AI super powered Funds and coins (Hasselgren et al., 2024), we apply a variant of the Die Bold and Yilmaz 2013 means based metrics, namely the quantile based self- regressive vectors (QVAR) of He et al. By using it, it is possible to observe the interconnection of the values reflected in the parameters of the method in various financial conditions, for instance, a market with an indicator equal to zero as an average measure. 5, a very negative marketplace with a measure only at level zero. 05, while the measure of the market was found to be extremely favorable with a very high value of 0. 95. Table 3 shows the estimated values of the static QVAR method (Eq. 1) and connectivity metrics, which are the total connections, index (TCI), and directions closeness (TO, VIA, and NET).

Table 3

spillovers at the mean (q = 0.60) that are structural.
	SEA	FET	AGIX	ROBT	IRBO	ROBO	Golden	BTC	Fat	Fairness	REIT	Promise	USDI	FROM
SEA	46.48	22.26	8.20	4.40	4.48	4.60	2.42	22.20	2.24	4.84	2.60	2.04	2.42	64.64
FET	22.80	42.88	0.22	4.04	4.00	4.28	2.66	22.42	0.08	4.06	2.06	2.40	2.68	68.22
AGIX	8.66	8.40	62.42	4.42	4.60	4.84	2.24	20.40	2.08	4.04	2.82	2.24	2.00	48.68
ROBT	2.20	2.48	2.26	22.22	28.64	28.24	2.00	4.80	2.64	26.48	6.02	2.00	2.88	88.80
IRBO	2.00	2.20	2.46	28.42	22.02	28.84	2.22	4.08	2.46	24.82	6.82	2.82	2.08	88.08
ROBO	2.08	2.46	2.26	28.88	26.40	22.20	2.20	4.82	2.60	26.64	8.60	2.80	4.46	88.82
Golden	2.86	2.86	2.06	4.28	4.26	4.42	40.68	4.60	4.04	4.46	4.24	8.26	0.60	60.44
BTC	20.24	0.68	0.28	6.20	6.84	6.64	2.42	48.08	2.02	6.02	4.20	2.42	2.42	62.02
Fat	2.66	2.42	2.26	4.64	4.62	4.04	4.26	2.28	66.24	4.86	2.40	2.24	2.66	44.88
Fairness	2.02	2.22	2.42	26.62	24.08	26.84	2.44	4.80	2.28	22.66	20.22	2.66	4.42	88.44
REIT	2.62	2.40	2.60	0.02	8.84	20.88	2.60	4.20	2.62	24.04	44.82	4.86	2.86	66.20
Promise	2.26	2.88	2.42	6.00	6.24	6.28	8.46	2.22	2.42	6.06	4.02	62.26	4.06	48.86
USDI	2.86	2.40	2.02	8.84	6.86	8.80	8.80	4.64	2.46	8.46	4.08	4.00	48.22	62.80
NEAR	46.02	40.48	46.00	00.48	04.40	202.44	48.68	64.40	22.62	202.06	68.44	44.28	48.06	804.60
Inc. Own	02.40	02.24	08.22	222.68	226.42	224.64	88.24	202.28	86.64	224.82	02.26	86.42	86.28	TCI
LEFT	−8.62	−8.86	−2.88	22.68	26.42	24.64	−22.86	2.28	−24.46	24.82	−8.86	−24.68

Using the QVAR connection approach in a normal market situation that was earlier described, the following results have been obtained Under the condition when q = 0. 5, Table 3 presents the means & quantile for all measurements at HI combine and I-404. Analyzing the directional FROM column of the results, it was identified that the market spillovers are most significant in the stock market and AI ETFs. In particular, the nature of segmentation is such that there is almost a 78% spillover between the stock market and Not one, not two, but four AI funds. On the other hand, the bond and energy markets indicate almost no case of systemic spillovers (X. Zhang et al., 2022). There is a visible trend here in the data; energy markets are the most addressed at 34.87% of all spillovers, and bond markets globally acquired spillovers. 47.75%. Based on the data, these markets have no relation with AL ETFs and other related markets referred to as standard markets. However, AI ETFs face more substantial system spills than AI coins on the blockchain (Fig. 3).

More specifically, the findings indicate that the stock market and AI ETFs have the most impact on other system factors. The very high spillover values—102% for ROBO and the stock market combined—and the relatively low values—99% and 93% for ROBT and IRBO, respectively, make this point clear. Our research shows that bonds (34.17% of the total) and gold (38.57%) have the most negligible impact on the system. This supports the theory That the oil, bond, and metal prices have little to no impact on AI coins and Iris (Fig. 4).

The Fig. 5 demonstrates the substantial Function of Nan exchange-traded funds in guiding systems and doing various types of price forecasting. A lower level of system connectivity is also shown by the mean number of emitted (received) spillovers, which range from 48–57% for AI tokens. Because of their strong correlation and decentralization, our findings emphasize the value of AI tokens as a diversifier for conventional asset portfolios. According to the NET row's analysis of net conditional connection, AI Etf and cryptocurrency sales have spillover impacts on other assets (Kumar & Singh, 2022). The positive outcomes of the unconditional NET measure clearly show this. However, the low numbers of the NET measures indicate a net inflow of spillovers into the AI token market and the other markets under consideration. Contrary to expectations, the median quantile of spillover for AI tokens is the lowest, indicating little to no relationship between AI coins and other markets.

Even under typical conditions, the high TCI value (61.04%) in Table 3 indicates a highly interdependent system. Diebold and Yilmaz's (2012, 2014) mean-based connection approach will be our strength and comparison capability benchmark. We will compare this approach to the quantile connection technique at the median quantile. In the appendix, you may find the findings in Table A1. able A1 in the appendix shows the results obtained via the DY mean-based method. Results produced with the static DY connections method are similar to those using the median quantile for several connection metrics. In terms of global connectivity, the value is 62.25%., conditional connection (both ways) to and from Measures a system's net direction connectivity. (Atri et al., 2021). are among the researchers whose work is consistent with these results. Figure 6 compares median connection with mean-based connectivity, paying close attention to the two extremes at the top and bottom of the distribution. The findings presented here are derived from studies conducted by (EryiĞit, 2017). We can differentiate between large negative and negative shocks (q = 0.95) and q = 0.05, using the connectivity measures at the highest and lowest quantiles. For the extreme upper quantile relationship, see Table 4; for the severe upper quantile connection, see Table 5. All markets considered have the same systemic spillover effects when the circumstances are unfavorable, including at the lowest quantiles (q = 0.05). Evidence of this may.be seen in the system's FROM column. Tokens backed by artificial intelligence, exchange-traded funds, and traditional assets are all vulnerable to significant disruptions in the system.

Table 4

The very lowest measure (q = 0.06) exhibits still ripples.
	DEEP	FET	AGIX	ROBT	IRBO	ROBO	Golden	BTC	Grease	Justness	REIT	Promise	USDI	FROM
DEEP	8.08	8.62	6.44	8.68	8.66	8.82	8.42	8.00	8.42	8.22	8.42	8.06	8.42	02.02
FET	8.24	8.62	6.68	8.86	8.66	8.80	8.06	8.02	8.46	8.24	8.40	6.04	8.42	02.48
AGIX	8.02	8.80	8.64	8.62	8.60	8.82	8.08	8.08	8.22	8.22	8.42	6.88	8.46	02.48
ROBT	6.60	8.44	6.28	8.66	8.20	8.66	8.06	8.44	8.44	8.82	8.66	8.20	8.28	02.44
IRBO	6.48	8.20	6.28	8.46	8.60	8.66	8.00	8.60	8.68	8.84	8.60	6.08	8.26	02.42
ROBO	6.42	8.24	6.48	8.44	8.40	8.80	8.22	8.40	8.62	8.80	8.66	8.24	6.06	02.22
Golden	6.62	8.24	6.24	8.86	8.48	8.68	20.24	8.60	8.82	8.20	8.60	8.66	8.62	80.88
BTC	6.08	8.80	6.60	8.00	8.86	8.02	8.28	8.82	8.40	8.06	8.42	6.04	8.48	02.20
Grease	6.48	8.04	6.26	8.82	8.62	8.62	8.60	8.48	0.80	8.20	8.42	8.28	8.26	00.40
Justice	6.66	8.24	6.28	8.24	8.80	8.60	8.28	8.64	8.20	0.28	8.84	8.00	8.40	00.84
REIT	6.68	8.66	6.28	8.60	8.84	8.06	8.20	8.64	8.48	8.60	0.84	8.24	8.46	00.28
Promise	6.66	8.08	6.08	8.04	8.60	8.00	8.66	8.40	8.64	8.46	8.64	8.66	8.64	02.44
USDI	6.82	8.40	6.44	8.66	8.46	8.68	8.48	8.48	8.82	8.22	8.44	8.26	0.62	00.40
NEAR	80.00	88.08	86.88	04.82	02.64	06.84	08.82	00.84	80.02	200.40	202.08	86.22	88.82	2282.84
Inc.Private	88.08	06.80	84.42	204.28	202.24	204.84	208.04	00.64	00.62	200.68	222.82	04.88	08.42	TCI
LEFT	−22.02	−4.40	−26.60	4.28	2.24	4.84	8.04	−0.46	−0.40	0.68	22.82

Table 5

A severe top measure (q = 0.06) exhibits stable ripples.
	DEEP	FET	AGIX	ROBT	IRBO	ROBO	Golden	BTC	Oil	Equity	REIT	Bond	USDI	FROM
SEA	8.82	8.64	8.62	8.64	8.22	8.88	8.26	8.06	8.22	8.64	8.64	8.02	8.20	02.20
FET	8.80	0.28	8.68	8.66	8.22	8.86	8.20	8.84	8.06	8.46	8.60	8.00	8.00	00.82
AGIX	8.64	8.60	20.64	8.24	6.00	8.60	8.22	8.64	8.20	8.44	8.62	8.02	8.88	80.46
ROBT	8.04	8.04	6.08	0.42	8.20	8.02	6.86	8.24	8.04	8.42	8.80	8.04	8.46	00.68
IRBO	8.22	8.22	6.08	8.68	8.66	8.82	6.00	8.20	6.00	8.22	8.66	8.24	8.48	02.44
ROBO	8.00	8.02	8.26	8.68	8.06	8.06	8.00	8.26	8.26	8.46	8.84	8.42	8.44	02.04
Golden	8.64	8.24	8.60	8.46	8.22	8.66	0.42	8.48	8.44	8.48	8.62	8.68	8.62	00.68
BTC	8.02	8.68	8.62	8.62	8.24	8.86	8.22	0.44	6.04	8.46	8.40	8.02	8.86	00.68
Grease	8.24	8.28	8.04	8.66	8.20	8.82	8.22	8.42	0.00	8.80	8.84	8.46	8.80	00.20
Justice	8.08	8.08	6.00	8.46	8.02	8.86	8.22	8.26	6.00	8.80	8.06	8.06	8.40	02.22
REIT	8.22	8.26	8.40	8.80	8.48	8.24	8.06	8.46	6.00	8.06	0.46	8.28	8.86	00.64
Promise	8.42	8.40	8.28	8.64	8.26	8.84	8.00	8.26	6.08	8.48	8.68	20.28	8.24	80.82
USDI	8.40	8.46	8.46	8.66	8.26	8.86	8.24	8.60	8.24	8.64	8.66	8.48	0.04	00.08
NEAR	88.26	88.44	88.66	04.02	88.60	06.68	86.84	80.20	84.88	02.68	02.86	08.80	02.88	2288.62
Inc.Own	06.08	06.62	08.40	204.24	08.26	206.64	06.26	08.62	04.68	202.66	202.22	208.08	202.82	TCI
LEFT	−4.04	−4.40	−2.80	4.24	−2.86	6.64	−4.84	−2.48	−6.44	2.66	2.22

On the one hand, the TO column's stated numbers show that the equities, a REIT, and Artificial EEFT’s areas all contribute significantly to the economy amid major recessions, highlighting the importance of these sectors in anticipating when various markets will plummet. The dependent connectivity measurements of the Nets are reported in the final row. The beneficial Positive measurements show that markets for artificial intelligence exchange-traded funds (ETFs), gold, equities, and real estate investment trusts (REITs) emit net shocks to other systems. As a whole, the system's shocks are felt most by AI coins and other related exchanges (R. Chen & Xu, 2019). Table 5 shows that beneficial changes to the economy equally influence all sectors as they get comparable quantities of spillover at high upper percentiles (q = 0.95). Good surprises are often transmitted via AI ETFs, equities, real estate investment trusts (REITs), bonds, and currencies. Under extreme shocks, at the top percentiles, the NET contingent connectivity results reveal that the following markets serve as net shock sources: bonds, equities, real estate investment trusts (REIT), AI ETFs (except IRBO), and USD currency exchanges. Surprisingly, markets for artificial intelligence coins, gold, vitality, and cryptocurrencies are the ones that absorb upsets the most (Fig. 7).

The severe measure connection outcomes show that total connection (TCI) increases significantly at both the top and bottom of the distribution exceeding 90%. The connection at the mean measure (DY unity measure) and the mean measure (q = 0.5) are only 62.25% and 61.04%, respectively. In addition, compared to the mean (DY connectivity metrics) and average measure (q = 0.5), the connection values at the extreme quantiles, which include contingent impacts FROM OR TO the system, are more significant. Several additional studies have reached similar conclusions, including (Bouri et al., 2017). According to the analysis, extreme optimism and minus shocks significantly impact AI tokens and AI ETFs.

The mean-based DY connection analysis results imply that traditional markets, including gold, energy, cryptocurrency, bonds, exchanges, and stock markets, need to be adequately diversified by AI ETF assets. The strong and positive relationships seen among AI ETFs and those markets, even under normal market conditions, as detailed (Singhal et al., 2019). and Bauer and Hoang (2021), might be the reason for this. Due to their weak link to other markets under typical market circumstances, AI tokens may mitigate the risk associated with other assets. During times of significant market fluctuations, these correlations stand out more. A market's quantity of spillovers, both received and sent out, tends to spike during market volatility, especially when the economy is experiencing steep declines. Table A1 shows the average value, and Table 3 offers the median value, so we can compare this increase to what we expect in a regular market. Research shows that artificial intelligence (AI) exchange-traded funds (ETFs) can't fully diversify the assets of conventional portfolios, particularly during recessions.

Connectivity levels vary among quantiles, as seen in Fig. 6. The relationship level between system variables increases significantly at the extreme quantiles, as seen in Fig. 6. Since shocks raise correlation coefficients across variables in proportion to their severity, very positive and highly negative shocks have comparable effects on the system. (Guan et al., 2021).At the lowest quantile (q = 0.5), the TCI level is much higher than 60%. However, the overall degree of system interconnection spikes after significant disturbances, going beyond 90% at the top and bottom quantiles. The symmetrical and balanced pattern of TCI fluctuations is seen in good and bad market conditions. (Y. J. Zhang & Wei, 2010). also came to similar findings; this trend makes sense.

The discovery that AI ETFs are a significant assets sector and positive emitter in any market and situation is essential for diversity techniques and portfolio planning. A portfolio that includes assets related to artificial intelligence might be an excellent way to protect oneself from potential losses in connection with stock, property, oil, gold, and cryptocurrency markets. In addition, (Cui et al., 2023). suggest that a shift in the economy terms of AI ETFs could be a precursor to changes in the other market sectors that were part of this study. They also note that investing in fresh AI resources could be an excellent way to gauge the impact of creativity driven by problems. To get a good look at risk diversity, it is essential to look at connections at different degrees of economic trouble, including severe tail-ends, as net receivers and emitters differ across the quantiles. This is especially true for assets linked to the internet, which are subject to the most significant levels of instability and volatility in this developing market because of the fast and constant technical development and upheaval.

Prior work (W. Huang & Wu, 2021) has shown that tail-related risk might appropriately indicate system risk; this study's conclusions support that idea. When studying market connectivity, it is essential to incorporate tail-end hazards. Therefore, the tail-end distributions of impacts caused by severe either-or shocks will go unnoticed if conditioned mean-based connectivity measures or the median of dispersion are the only metrics considered. Furthermore, there is a difference between the nature of dynamic impacts at the median and the extreme tails. When evaluating investments and reducing risk, risk management and buyers should consider the unique qualities and interdependencies of traditional assets and commodities associated with artificial intelligence. Investors may precisely build diverse portfolios and discover prominent signs pointing to changes in market circumstances by recognizing the interconnection and ripple tendencies across various resources.

4.2. Dynamic quantile connectedness

According to prior empirical research, connections among Many financial developments, global issues, and recessions may cause securities to fluctuate over duration and make them insecure. The quantile connectedness approach will analyze the links among wealth, including AI ETFs or signs, plus normal. We will examine the correlations between these variables, paying close attention to the median and the top and lower quantiles in light of various economic crises and events that transpired during the given time frame. Our sample period encompasses the beginning of the COVID-19 problem and the times immediately following the commencement of vaccination campaigns.

Figure 9 shows how the total connectivity index (TCI) changes at various quantiles. For the mean quantile (q = 0.5), the blue line shows how the TCI varies near the midpoint of the distribution (Elie et al., 2019). Under typical market conditions, the degree of connectivity fluctuates between 45 and 80%. For example, just around the beginning of the COVID-19 pandemic in the first quarter of 2021, Eighty percent of TCI was reached. Afterward, the following year had a dramatic decline, even getting 50% in that last year. As the COVID-19 vaccination was widely disseminated and used, the financial markets started to seem more normal again, reducing the extreme connections among investments to more typical levels. The relationships between system variables vary within normal ranges during this time, reaching a second high of more than 75% in the fourth quarter of 2022. Lines in green and orange show a dynamic relationship in the top and bottom tails. Over the complete observation period, the system's connectivity level varies somewhat, ranging from 85–92%. This shows that the system variables are always highly interdependent, regardless of whether the market is falling or rising. This exemplifies how traditional assets, AI ETFs, and tokens are susceptible to good and bad shocks. We adopt the Quantile-Frequency Connection (M. L. Liu et al., 2013).to ensure that the Total A Connection Index (TCI) remains valid after using the quantile connectivity technique. Using this approach, we can determine the total price of Investing (TCI) for different quantiles at various rates (short, medium, and long term).

Number five is the value. As a result of using the quantile connectivity method, TCI-Total provides a thorough evaluation of connection. Using the quantile-frequency connectivity method, the TCI-long term, TCI-medium term, and TCI-short duration are computed at the center, extreme upper measure, and extreme lower quantiles, respectively. Frequency varies across several quantiles (median, higher, and lower) and time intervals (overall, short, medium, and long term). Because of this, we tell our customers to change their portfolios based on how long they want to keep their money in the market (Jain & Ghosh, 2013). One possible explanation for the discrepancies in the short-, medium-, and long-term frequencies across all quantiles is that the quantile-frequency connection model and the quantile connection model vary in their underlying assumptions. Without taking time frequency into account, our quantile connectivity model evaluates changes in connection across different quantiles. However, quantiles and temporal frequency are both included in the quantile-frequency model. The changing net directional connection at various quantiles is shown in Figs. 5, 6, and 7, respectively. These quantiles are the median (q = 0.5), the lower (q = 0.05), and the more significant (q = 0.95). According to the available data, the degree to which assets are interdependent varies and changes throughout time (Atri et al., 2021). Throughout the study period and under varying market situations, the roles of AI ETFs, AI tokens, and traditional assets as receivers or senders of spillovers vary.

During most of the research, we found that all AI ETFs were net emitters of value impacts to the system, especially under normal price conditions at average quantiles. This highlights the significant role of the new electronic securities in driving and anticipating other markets. Under severe market circumstances, all assets move from net emitter to net recipients of returns ripples in both the left and right tails. This includes AI tokens along with all adjacent markets. Typically, these assets behave as net receivers of impacts from another market. Additionally, the degree of profit carryover is much more significant in severe market situations compared to typical market conditions (Bouri et al., 2017). Because net direction connectivity changes over time, traders, politicians, and investors should keep an eye on net spillage trends to swiftly modify their investments and hedge against optimistic and bearish trends in the case of severe occurrences.

The bilateral impacts at the center, severe lower and excessive higher quantiles of a network are shown in Fig. 10. At the lowest quantile (q = 0.5), the pairwise network reveals that stock and all AI ETFs are the most critical parts of the system since they release the most impact on other sectors. In addition, conventional markets and AI tokens end up as net recipients of spillovers. The prices for bonds and USD exchanges (AI coins) are the ones that stand to gain the most (least) from consequences. The fact that gold, energy, and cryptocurrencies are the least affected by transference indicates that these markets are relatively unconnected to the structure when exchanges usually function.

The net pathways undergo a slight transformation in the face of very adverse market circumstances at the very bottom tails. When we contrast AI ETFs to other asset classes, we show that gold, equities, and real estate investment trusts (REITs) send the most impact into the system. Every market is a net recipient of impacts; the ones with the most vital connections to the ecosystem are securities and AI signs, while energy and coin markets remain weak links. Pairs share spillover analysis also reveals that swap markets, fairness, securities, REITs, AI ETFs (excluding IRBO), and AI ETFs are all net sources of spillovers under favorable markets at significantly lower percentiles (q = 0.95). Bonds and AI ETFs are the most significant participants in the method's impacts. Transfers from the structure mainly benefit every other market, including AI tokens (Mensi et al., 2017). As shown in a relative tail dependency (RTD) is good when the value is for the correct tail and negative when it is for the left tail. The findings show that RTD changes with time and becomes low in 2020, the second part of 2022, and 2022. The majority of the material collected time, nevertheless, shows adverse RTD. It shows that the left tail is more dependent on the reverse tail, which means that connections are less fragile.

Over many periods (short-term, medium-term, and long-term), this study examines the dynamic total connectivity index (TCI) for different quantiles (Cui et al., 2023). The Median quantile total connection index, which fluctuates, is shown in Panel A. In Panel B, we can see the lowest 5th percentile total connection score as a function of time (q = 0.05). At the most extreme quantile (q = 0.95), Panel C displays the overall connection index as it varies over time (Fig. 11).

Finally, the research results on dynamic quantile connectedness indicate that, on average, the degree of dynamic total connection is higher at the top and bottom quantiles than in the center. During extreme market volatility, there is a high link between AI-driven assets and conventional markets. Furthermore, the highest and lowest quantiles of the overall connectedness score exhibit variable oscillations. An asymmetrical relationship may exist since the correlation is rather persistent over time and quantiles. How these markets respond to unusual occurrences is contingent upon the kind and timing of the shocks, and AI and traditional assets react differently to positive and negative shocks, respectively (R. Chen & Xu, 2019). We find that across different quantiles and periods, AI and traditional assets both show a variation between net emitters and net recipients of return spillover. In addition, the research reveals that dynamic network connection indicators are more volatile during extreme market events compared to normal market conditions (Fig. 12). The volatility of these indicators is probably due to factors beyond our control, such as economic cycles and geopolitical events (Xu et al., 2023).Because of the expected changes in net spillover patterns throughout time, investors and portfolio managers should rebalance their assets(Dutta et al., 2020)

We may summarize the study's key points about static and dynamic connections. Many assets, such as AI tokens, AI exchange-traded funds (ETFs), gold, Bitcoin, equities, and bonds, are interconnected during severe events. Portfolio asset allocations are known to undergo significant shifts during market volatility, which aligns with the results of earlier research (Adekoya et al., 2022; Tiwari et al., 2022). When contrasted with the intermediate quantiles, there is a more unambiguous indication of disruption spread at the top quantiles. During times of crisis, the advantages of diversifying a portfolio using AI tokens and AI ETFs will likely be significantly reduced. Our study shows that AI exchange-traded funds (ETFs) are a powerful tool for system direction, and they are the best at forecasting the returns of traditional financial markets.

Furthermore, in normal market conditions, AI tokens and exchange-traded funds (ETFs) are reliable hedging vehicles for portfolios that include gold, Bitcoin, oil, and bonds. However, during economic downturns, their capacity to reduce risk diminished. Investors may use these innovative assets for hedging and diversification purposes, considering the results of (Kang et al., 2023). our study aims to improve the existing empirical research on the risk management of AI assets. We discover that AI tokens, AI ETFs, and other significant assets are highly related, which aligns with prior studies. In addition, our results are consistent with other research suggesting that during heavy market volatility, this link got larger at the extremes of the distribution. Our research shows that, excluding extreme cases, there are very few relationships among AI tokens, AI ETFs, and Bitcoin. We have yet to determine how this discovery will affect our efforts to diversify and hedge. Our study's findings suggest that the AI asset markets were unrelated to the early stages of cryptocurrency's development. The many advantages of investing across the markets for artificial intelligence assets and cryptocurrencies are supported by our empirical results, which align with (H. H. Huang et al., 2023)

In addition, disruptions in the oil, gold, and other financial markets significantly affect AI tokens, particularly during times of severe market downturn. This highlights how limited AI tokens are in their ability to provide effective hedging in emergencies. The results of our analysis show that the AI coins & CFDs provide variety and hedge benefits. change drastically depending on regular and extreme market conditions. Traditional markets and the AI business are inseparable. Therefore, investors should investigate these markets' interdependencies and transmission effects under various market conditions (Bildirici & Turkmen, 2015). In order in order to adopt well-informed investments and modify investments in response to current markets and financial conditions circumstances, it is essential to have this information.

Investors and portfolio managers may benefit from the insights we have obtained from the empirical data. During market instability, asymmetry becomes more apparent. Thus, investors should exercise caution when using AI assets to diversify their portfolios. The market for AI-generated assets is also still in its early stages. Consequently, investors must consider the market expansion potential of new assets, such as AI tokens and AI ETFs, and the risk-return analysis of asset behavior. In addition, the expanding impact of existing markets and the ever-changing patterns of development in new assets may cause changes in the structure of the financial market (Le & Chang, 2012).

4.3. Portfolio implication

Here, we calculate the ideal weight, overlay ratio, and hedged efficacy for different asset-AI coin combinations employing DCC-GARCH, and we look into client portfolio diversity. Table 6 displays the findings of this exercise, which demonstrate that most resources (IRBO, ROBO IS A, gold, capital, amid water, (MACHINE, to be optimal, the sum of (IRBO, A ROBOT, money, equality, and bond)-AGIX or (IRBO, ROBO IS A, gold, equity, and bond)-FET must be 0.99 percent. This means buyers ought to allot 99 cents of 1 dollar towards these resources and 1 cent to AI tokens (M. Arfaoui & Ben Rejeb, 2017). On the other hand, the oil-AI token combination has the lowest ideal ratio (0.85 for oil THE SEA, 0.83 for oil-EFT, plus 0.84 for oil-AGIX), suggesting that investors should put around 15 cents into the OCEAN token and 85 cents into the fuel space for every $1 invested in the oils-OCEAN portfolio.

Table 6

The ideal ratio of masses with hedges.
	Optimal weights	Hedge ratio	Hedging Effectiveness (HE)
ROBT/DEEP	1.08	1.06	2.06
IRBO/SALTY	1.00	1.06	2.06
ROBO/OCEAN	1.00	1.06	2.06
Golden/ABYSMAL	1.00	1.02	2.08
BTC/OCEAN	1.08	1.26	2.86
Grease/SEA	1.86	1.06	2.64
Fairness/MARINE	2.00	1.04	2.08
REIT/NAVAL	2.08	1.04	2.06
Promise/SEA	0.00	1.00	2.00
USDI/OCEANIC	1.08	1.00	2.00
ROBT/FET	1.00	1.06	2.06
IRBO/FET	1.00	1.06	2.06
ROBO/FET	1.00	1.06	2.06
Golden/FET	1.08	1.02	2.08
BTC/FET	1.08	1.26	2.86
Fat/FET	1.84	1.06	2.88
Fairness/FET	1.00	1.04	2.08
REIT/FET	1.08	1.04	2.06
Pledge/FET	1.00	1.00	2.00
USDI/FET	1.08	−1.02	2.00
ROBT/AGIX	1.08	1.06	.08
IRBO/AGIX	1.00	1.06	2.08
ROBO/AGIX	1.00	1.06	2.08
Gilt/AGIX	1.00	1.02	2.00
BTC/AGIX	1.06	1.24	2.04
Lard/AGIX	1.84	1.08	2.06
Impartiality/AGIX	1.00	1.14	1.00
REIT/AGIX	1.08	0.24	1.08
Bond/AGIX	1.00	0.12	1.00
USDI/AGIX	1.08	−1.02	1.00

To mitigate risk and make the most of diversity, investors should think about incorporating AI tokens into your holdings of precious metals. cryptocurrencies, stocks, bonds, real estate investment trusts (REITs), currencies, and AI exchange-traded funds (ETFs). A hedging ratio of 0.01 for gold-AI tokens suggests that a one- A cent-short AI coin option may balance out a dollar-long gold stake. An investment of $1 in AI ETF should be offset by a temporary position of $5 in AI tokens, according to the hedging ratio for AI ETF-AI tokens, which is around 0.05.

Thus, this specific type of hedge has nearly zero hedging ratio depending on the degree of risk management. In particular, starting from the 25th, the richest benefits can be seen in combining the BTC-AI token. From the avowed observations, it is discerned that AI tokens are an unreasonable means to preserve Bitcoin. However, the protection from using a combination of gold and AI tokens is the lowest; thus, gold can be hedged using AI tokens for a relatively small amount (Sajjad et al., 2022). The most successful hedging pairings are Navy connect, USDI-OCEAN, Feet connect, USDI-EFT, & AGIX USDI, the gold, equity, & bond. This means that portfolios that do not have investments in gold, fixed-income equities, or USD-related indexes may benefit the most from AI tokens.

Besides, the best pairings are the AI ETFs and AI tokens, especially AGIX. Therefore, AGIX can be a perfect diversifier that perfectly works for all the assets that were examined in this study (Soeder, 2021). The couplings with the lowest efficacy values were Equations 0.6 for oil-OCEAN, 0.76 for BTC-OCEAN, 0.78 for oil-FET, & 0.75 with BTC-FET. There is no benefit for energy and cryptocurrency market investors to diversify their portfolios with AI tokens (OCEAN and FET).

To build diversified and hedged assets, it is crucial to recognize the interconnections across monetary commodities. To diversify and hedge a portfolio, choosing products that need more connectivity is best. Most published works on the subject have dealt with somewhat generalized groups of assets. However, the directionality of the connection between commodity and industry exchange-traded funds needs to be better understood. Since many investing strategies rely on sector ETFs, this is a crucial area to investigate. Three key issues concerning the interconnectedness of money are examined in this paper. Since the US category, Etf is the backbone of the strategies of many local and foreign financiers; they are the initial ones to undergo examination. The next stage is quantifying connections and impacts utilizing a time-frequency method that allows for studying the medium and medium-term findings. Thirdly, investigating hedges and investment diversifying is done using the knowledge of repercussions and connections.

We find that the 30-day forecasting anticipation of US shares market fluctuation (the VIX) or oil price fluctuation (OVX) have the most significant impact on the cost and goes back of US business ETFs over the short and extended runs, respectively, when we analyze directional feeling connected. VIX is significant in transmitting spillovers to equities. The ETFs are the least affected by US economy ambiguity (USEPU). Oil's short- and long-term effects on US sector ETFs are more significant than gold's. Unknown indicators (VIX, OVX, USEPU), US sector ETFs, gold, oil, stocks, and impacts are asymmetrical in the medium and long term. During financial instability and recession periods, the repercussions are more pronounced and have a higher short-term impact. Looking at the big picture, the ETF pairings XLI-XLB (2.59, 2.34) and XLB-XLE (2.67, 2.07) have the most repercussions. From a brief and long-term perspective, oil outperforms other US sector ETFs regarding portfolio protection. Regarding hedges, a Consumer Goods ETF is the most efficient.

Some real-world consequences stem from the paper's findings. There are still financial concerns, even if oil is the best over time and short-term hedging for US industry ETFs—platinum insuring costs about the same as oil insurance in the short run. Since oil has a better-hedged efficacy because the cost of oil swaps is comparable to that of gold fencing, traders looking for hedge short-term holdings in US sectoral ETFs should choose oil as a trading vehicle. Hedge costs for gold are $0.10 to $0.23 over the long run, whereas for oil, they are $0.43 to $0.68. For investors looking for long-term insurance, the best option will depend on how they weigh the expense of insuring against the efficiency of protecting. On the other hand, investors could use the S&P 500 as a long-term hedge for US sector ETFs, given that its hedging costs are comparable to gold's and its hedging efficacy is more significant. This data must be helpful for both local and foreign investors in US category ETFs.

Policy implications

One of them is focused on the comparison of the efficiency and cost of hedging with the help of such tools as oil and gold for sector risks inside the US ETFs. The government should encourage the expansion of information in relation to the finances so that helpers can make sound financial decisions. Strengthening market protection standards and transparency measures can guarantee that the participants of the market know the opportunities and risks related to the application of various kinds of hedging instruments, including oil swaps and gold futures. This could be helpful in the development of strong financial markets, thus minimizing the sources of systematic risks and enhancing market stability. Since it is evident that oil offers a better hedge ratio when used for shorter periods for the US sectoral ETFs, the regulatory authorities may formulate policies that enhance optimal hedging strategies. It could also involve encouraging the application of oil swaps and other commodities as a way of offsetting the risks appropriately within the respective sector. Also, the paper showed insurance was comparable to oil and platinum. Government policies could promote competition in hedging instruments and possibly bring down the cost. Therefore, introducing more varied forms of hedging can increase market stability because investors can choose the form of hedging that meets their aims as well as the current market situation and needs, thus acting as a driver of improvements in the financial market ecosystem. The results of the study also relate to investor education and risk management procedures. Instruments that promote financial literacy for consumers and supply adequate information to the investors for efficient hedging may produce better results in risk management policies. This goes without saying, especially for international investors who may need to gain adequate knowledge of the operation of the American market. In turn, by providing the investors with credible information on how to use such tools for long-term hedging and risk management, the policymakers contribute to the enhancement of the inclusiveness of the investment landscape as well as to the development of efficient investment tools. They could include awareness campaigns, information-sharing centers, and consultancy services that help investors avoid making bad choices depending on their ability to take risks and the duration of their investment period. The information regarding the cost and reliability of long-term hedge stocks, that is, gold and S&P 500, indicates the need to come up with policies that foster stability and long-term growth in the financial markets. Thus, the more attractive government regulations for the creation of relatively cheap long-term options for hedging could lead to increased interest in FPIs in US sector ETFs, as well as contribute to sustainable economic growth.

Furthermore, when adjusted for the fact that the costs of hedging cannot fall on investors’ heads in the long run, policymakers contribute to a more stable environment for investments. This stability has great significance for the reinforcement of investors’ confidence and the development of the environment for the formation and circulation of capital and other factors of production. Finally, the conclusion of the study for both domestic and international investors is that there should be policies that favor both domestic and external investments. The accessibility and availability of hedging instruments when investing in sectors can also be caused by other regulatory measures that make it easy for foreign investors to access these hedging instruments. This determines the attractiveness of the US sector ETFs in the global markets. This could include reducing the stringency of measures restricting foreign entry into US financial markets and guaranteeing that risk-management tools are easily accessible and cheap. Thus, with the help of the proposed measures to facilitate economic liberalization for the domestic market and improve the conditions for foreign investors, it will be possible to introduce the principles of a more open and interdependent world financial system.

The following policy directions, based on the study’s insights, can assist in managing these risks and innovation costs and improve the stability and efficiency of the market for sectoral ETFs to the advantage of investors of all categories.

Limitation and Future recommendations

The study about frequency spillovers and connectedness of oil and gold on US sector ETFs also has some limitations that need to be considered when generalizing the results of the research and increasing their reliability. Secondly, the data used in the analysis with regard to this paper may be time-bound, and thus, the study could not be done across different economic periods or conditions. Here, the concentration lies in certain time intervals and market situations that could not reflect the entire dynamics of oil and gold prices and their effects on various cycles more completely. Secondly, the study majorly incorporates historical values, which may fail to capture new market events or changes that may occur in the future driven by other factors, such as advances in technology or changes in laws regarding the market. However, it is important to take note of the limitation in the methodology used in this study of the fact that it needs to take complex interdependencies or nonlinearities into account that are usually present in real-world financial markets. Last, the analysis focuses only on the US market, and it does not include the possible effects of the global economic section or the use of cross-border financial relationships. Thus, it is less valuable in the context of the increasingly globalized financial markets.

Future studies should provide more richness of data with respect to a greater variety of economic conditions and periods, including recent fluctuations in the market and cyclic patterns in the future. It would be even more worthwhile to broaden the study focus by including the effects of cross-border markets and segment consequences to comprehend how global aspects and the dynamics of such sensitive commodities as oil and gold put pressure on the concept of sectoral investment. In terms of the method, the use of more complex econometric techniques that allow for curvature and interaction effects between different types of financial products might be useful. However, incorporating the macroeconomic variables and the factors pertaining to specific sectors might have provided further understanding of the factors that influence frequency spillovers and connectedness. In addition, future research should also incorporate the effects of changes to regulation and the developments in the domain of financial technology, as they may considerably disrupt the patterns identified within the current and future organizational relations of the market and sector ETFs. Such an approach would extend confidence in the findings beyond what is currently possible while also offering practical guidance to policymakers and investors who are keen on engaging with the delicate ballet of global markets.

Author Contribution

We, the undersigned authors of the manuscript titled “The Dynamic Influence of Uncertainty on Sector Equity Funds: A Time-Frequency Analysis of Oil, Gold, and Market Volatility” hereby declare and affirm our individual contributions to the work as detailed below:1. Shahzaib Khan * (corresponding author) led the project administration and supervision, and wrote the original draft of the manuscript.2. Umair Khan contributed to the conception and design of the study, as well as the interpretation of data.We confirm that all authors listed have made a significant contribution to the work, and have reviewed and approved the manuscript for submission.No author has any conflict of interest to disclose.We are grateful for the support and contributions of our respective institutions and colleagues.Sincerely,Shahzaib Khan

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No competing interests reported.

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The Dynamic Influence of Uncertainty on Sector Equity Funds: A Time-Frequency Analysis of Oil, Gold, and Market Volatility

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1. Introduction

2. Literature review

3. Methodology

3.1. Frequency spillovers method

3.3. Data and preliminary analysis

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4.1. Static quantile connectedness

4.2. Dynamic quantile connectedness

4.3. Portfolio implication

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