Shoaib Ali, Muhammad Naveed, Hasan Hanif, Mariya Gubareva
This study investigates the return spillover between the Islamic gold-backed cryptocurrencies and equity markets of the Gulf Cooperation Council (GCC) countries. The study utilizes the QVAR method to determine the quantile connectedness among the asset classes and identify optimal portfolio weights across different economic conditions. The results show that the GCC economies have stronger connections with each other than with the cryptocurrencies. However, there is an increase in connections between the GCC economies and cryptocurrencies during extreme events. This suggests that extreme news can amplify the relationship between the Islamic cryptocurrencies and GCC markets. The findings suggest that asymmetric tails exist in the connectedness between the asset classes, meaning that the relationship between them is stronger during extreme market conditions. Accordingly, the dynamic connectedness analysis reveals varying patterns of connectedness across different periods, outlining pivotal portfolio implications. The study also suggests optimal weights for portfolio managers and investors and outlines the least expensive hedging strategy. The research proposes that investors in the GCC region could potentially mitigate the risk of their Islamic equity portfolios by incorporating the Islamic Shariah-compliant gold-backed cryptocurrencies in their portfolio. Further studies could explore the role of other factors such as liquidity, market volatility, and investor sentiment in the relationship between asset classes. Future research could examine the effects of other types of news, such as macroeconomic news, on the relationship between asset classes. Additional research could focus on the implications of incorporating Islamic gold-backed cryptocurrencies in a portfolio for investors beyond the GCC region.
Bu çalışmanın amacı kripto para birimi olan Bitcoin ve küresel bir etki gücüne sahip olan BRENT petrol fiyatlarının gelişmiş ve gelişmekte olan ülkelerin borsa endeksleri üzerindeki dinamik bağlantılılığının analizini gerçekleştirmektir. Analizi gerçekleştirmek amacıyla 12.11.2017 ile 19.11.2023 tarihleri arasındaki Bitcoin, BRENT petrol, Amerika Birleşik Devletleri’nden S&P500 borsa endeksi, Fransa’dan CAC borsa endeksi, Almanya’dan DAX borsa endeksi, Japonya’dan NIKKEI225 borsa endeksi, İspanya’dan IBEX35 borsa endeksi, Türkiye’den BIST100 borsa endeksi, Meksika’dan S&PBMV borsa endeksi, Endonezya’dan IDX borsa endeksi ve Suudi Arabistan’dan TADAWUL borsa endeks değişkenlerine ait haftalık veriler TVP-VAR yöntemi ile analiz edilmiştir. Çalışma sonucunda elde edilen bulgular kriz dönemlerinin varlıklar arasındaki dinamik bağlantılık ilişkisini artırmakta olduğunu ve Bitcoin ve BRENT petrol değişkenlerinin diğer borsa endeksleri tarafından etkilendiğini ortaya koymuştur. Ayrıca incelenen gelişmiş ülke borsa endekslerinin tüm dönemler itibariyle diğer değişkenleri etkilediğini bunun yanında Suudi Arabistan borsa endeksinin de diğer gelişmekte olan ülkelere göre borsa endekslerini daha fazla etkileyen bir görünüme sahip olduğunu ortaya koymuştur.
The autoregressive integrated moving average (ARIMA) model is a widely used technique for capturing past dependencies and trends in order to generate future predictions. This study presents a comparative analysis of the ARIMA model’s forecasting capabilities as applied to gold and Bitcoin prices. The methodology employed consists of obtaining historical price data, implementing machine learning techniques, fitting the ARIMA model, then validating its predictive ability using multiple error metrics. Our results indicated that the optimal ARIMA parameters for Bitcoin and gold are different, which emphasizes their different price behaviors. Additionally, the study examined implications for policy, including issues such as prices for CPUs and GPUs, the role of market dynamics, as well as the possibility for price manipulation, which is of special relevance for cryptocurrencies that exist outside the mainstream. The study also suggests potential directions for future research, such as applying advanced machine-learning techniques and adopting cross-validation. This research offers important insights regarding Bitcoin and gold price dynamics and demonstrates the applicability of the ARIMA model for financial forecasting while demonstrating the necessity of further investigation into more refined predictive models.
Modern Portfolio Theory (MPT) has long been a cornerstone in the realm of finance, aiding investors in navigating the complex terrain of risk and reward associated with diverse assets. This theory, formulated by Harry Markowitz in the 1950s, has traditionally guided investment decisions by optimizing the balance between different assets to achieve the desired level of risk and return. However, with the meteoric rise of cryptocurrencies as a new asset class, there is an increasing curiosity surrounding the applicability of MPT to this digital phenomenon. In response to this curiosity, this article undertakes the task of comprehensively assessing the compatibility of MPT with cryptocurrencies. To accomplish this, the research aggregates and analyzes the existing body of knowledge, thereby offering insights into the intersection of modern portfolio theory and the age of cryptocurrencies. A systematic literature review is conducted, encompassing 21 pertinent studies that explore various facets of this confluence. The findings of this article underscore an emerging trend in research, one that showcases the adaptability of MPT to innovative financial instruments like cryptocurrencies. These studies collectively illuminate the ways in which MPT can be employed to optimize portfolios that include digital assets, shedding light on strategies that account for the unique risk-return dynamics inherent in the crypto market. As the cryptocurrency landscape continues to evolve, it is evident that Modern Portfolio Theory is not only relevant but also adaptable, providing valuable tools to guide investors through the exciting yet volatile terrain of digital finance.
With the advent of the Web3.0 era, virtual assets have gained prominence in individuals’ asset portfolios, making Non-Fungible Tokens (NFTs) increasingly significant within the financial trading landscape. To address the issue of multicollinearity in regression analysis, this paper employs Principal Component Analysis (PCA) to perform dimensionality reduction on five correlated foundational sectors. Moreover, to enhance the accuracy and reliability of predictive outcomes, the study combines the Long Short-Term Memory (LSTM) model with the Autoregressive Moving Average-Generalized Autoregressive Conditional Heteroskedasticity (ARMA-GARCH) model. Through the application of these methods and practical implementation, the study forecasts the NFT index of the Hong Kong stock market for the next 30 days. This forecasting of return volatility contributes vital insights for investment decision-making. The research complements and offers application recommendations in financial innovation, deepening, and regulation. By devising novel products and tools to meet investor demands, providing risk management and investment opportunities, the model’s predictive outcomes can be utilized in regulatory and risk management strategies within the national financial trading market. This study provides regulatory guidance, policy formulation insights, and envisions further refinements of the research methodology by integrating information shock effects.
This paper explores the impact of sentiment on return spillovers among seven major Non-Fungible Tokens (NFTs). Using daily sentiment data from Thomson Reuters MarketPysch Indices and controlling for uncertainty factors and NFT sales, we examine the relationship between media sentiment and NFTs return spillovers using a TVP-VAR model. Our findings show that individual NFTs sentiment is important for spillover dynamics and the effect of sentiment changes based on market uncertainty. The study highlights the need for NFTs investors to focus on market sentiment themes rather than overall sentiment
Fathin Faizah Said, Raja Solan Somasuntharam, Mohd Ridzwan Yaakub, Tamat Sarmidi
Abstract Advanced digitalization and financial technology have of recent times become among the most crucial tools. Data mining and sentiment analysis have revealed the importance of digitalization in modern times. This study examines the influence of Google search activity on the volatility of digital assets. We analyzed six digital asset prices for Bitcoin, Bitcoin Cash, Ethereum, Ethereum Classic, Litecoin, and Ripple from the Coinmarketcap database. We used tweets on Twitter to survey users’ sentiment by using the Twitter search Application Programming Interface and Google trend search from web searches, news searches, and YouTube searches data using RStudio software. The study spanned 1 September 2019 to 31 January 2020 and employed the Vector Autoregression (VAR) approach for analysis. The VAR estimation revealed that Google search variables have significantly influenced the volatility of Bitcoin, Ethereum, Litecoin, and Ripple, as supported by the Granger causality test and impulse response function. The results of this study could be useful for investors and policymakers in drawing up strategies to reduce market volatility. These results should thus be useful to investors in developing profitable investment strategies to mitigate the impact of market turbulence.
The issue related to the quantification of the tail risk of cryptocurrencies is considered in this paper. The statistical methods used in the study are those concerning recent developments in Extreme Value Theory (EVT) for weakly dependent data. This research proposes an expectile-based approach for assessing the tail risk of dependent data. Expectile is a summary statistic that generalizes the concept of mean, as the quantile generalizes the concept of the median. We present the empirical findings for a dataset of cryptocurrencies. We propose a method for dynamically evaluating the level of the expectiles by estimating the level of the expectiles of the residuals of a heteroscedastic regression, such as a GARCH model. Finally, we introduce the Marginal Expected Shortfall (MES) as a tool for measuring the marginal impact of single assets on systemic shortfalls. In our case of interest, we are focused on the impact of a single cryptocurrency on the systemic risk of the whole cryptocurrency market. In particular, we present an expectile-based MES for dependent data.
The rapid proliferation of digital assets and the emergence of Central Bank Digital Currencies (CBDCs) are reshaping the global financial landscape, with significant implications for cross-border capital flows and the stability of capital markets. This review paper explores the dynamics of cross-border digital asset movements, analyzing how decentralized finance (DeFi), stable coins, and CBDCs influence liquidity, market volatility, and regulatory oversight. It investigates the potential risks posed by CBDCs to financial stability, including currency substitution, capital flight, and systemic vulnerabilities in interconnected markets. Furthermore, the paper assesses the readiness of global regulatory frameworks to address these challenges and examines the roles of interoperability, digital identity verification, and cross-jurisdictional cooperation in mitigating associated risks. Drawing from recent developments, policy reports, and empirical studies, this review provides a comprehensive analysis of how digital transformation in finance may disrupt traditional monetary mechanisms and market structures. It concludes by offering policy recommendations for ensuring resilient capital markets amid evolving digital asset ecosystems and central bank innovations.
Since the advent of Bitcoin, the cryptocurrency landscape has seen the emergence of several virtual currencies that have quickly established their presence in the global market. The dynamics of this market, influenced by a multitude of factors that are difficult to predict, pose a challenge to fully comprehend its underlying insights. This paper proposes a methodology for suggesting when it is appropriate to buy or sell cryptocurrencies, in order to maximize profits. Starting from large sets of market and social media data, our methodology combines different statistical, text analytics, and deep learning techniques to support a recommendation trading algorithm. In particular, we exploit additional information such as correlation between social media posts and price fluctuations, causal connection among prices, and the sentiment of social media users regarding cryptocurrencies. Several experiments were carried out on historical data to assess the effectiveness of the trading algorithm, achieving an overall average gain of 194% without transaction fees and 117% when considering fees. In particular, among the different types of cryptocurrencies considered (i.e., high capitalization, solid projects, and meme coins), the trading algorithm has proven to be very effective in predicting the price trends of influential meme coins, yielding considerably higher profits compared to other cryptocurrency types.
We investigate the benefits of using intraday realized volatility (RV) commonality, and propose a novel non-parametric framework for forecasting one-day ahead intraday RV (1D-ahead intraday RV). Specifically, we train multiple models using machine learning (ML) techniques under various training settings (single-asset, cluster-driven, and cross-asset), where commonality gradually enters model dynamics as training schemes become more complex. We conclude that models that leverage the cryptocurrency commonality outperform models that do not explicitly account for it, regardless of the market regime considered. The source code of this project is available at: github.com/edjanga/crypto_volatility_commonality.
Vincent Gurgul, Stefan Lessmann, Wolfgang Karl Härdle
We introduce novel approaches to cryptocurrency price forecasting, leveraging Machine Learning (ML) and Natural Language Processing (NLP) techniques, with a focus on Bitcoin and Ethereum. By analysing news and social media content, primarily from Twitter and Reddit, we assess the impact of public sentiment on cryptocurrency markets. A distinctive feature of our methodology is the application of the BART MNLI zero-shot classification model to detect bullish and bearish trends, significantly advancing beyond traditional sentiment analysis. Additionally, we systematically compare a range of pre-trained and fine-tuned deep learning NLP models against conventional dictionary-based sentiment analysis methods. Another key contribution of our work is the adoption of local extrema alongside daily price movements as predictive targets, reducing trading frequency and portfolio volatility. Our findings demonstrate that integrating textual data into cryptocurrency price forecasting not only improves forecasting accuracy but also consistently enhances the profitability and Sharpe ratio across various validation scenarios, particularly when applying deep learning NLP techniques. The entire codebase of our experiments is available via an online repository: https://anonymous.4open.science/r/crypto-forecasting-public . • NLP data from social media improve the accuracy of cryptocurrency forecasting models. • As a target variable, local extrema are a valid alternative to daily price changes. • Deep learning language models substantially outperform dictionary-based methodologies. • Both pre-trained and fine-tuned language models effectively quantify market sentiment.
Purpose: The article aims to investigate the relationship between the returns of the NASDAQ Composite stock index and the Bitcoin cryptocurrency. Theoretical framework: According to the literature, it is obvious that cryptocurrencies are very volatile, especially during the economic instability period. There is a belief that when uncertainty is in the economy, investors prefer alternative investment opportunities. There is a need to prove that. Design/Methodology/Approach: The study employs two different models, the ARMAX and the GARCH, to analyze the data from March 2018 to March 2023. The results of the analysis suggest a significant relationship between the returns of the NASDAQ Composite and Bitcoin. These results have important implications for investors and policymakers. Findings: The findings suggest that investors need to be aware of the potential risks and benefits associated with investing in both assets, particularly in times of economic uncertainty. Policymakers may also need to consider the impact of traditional stock markets and the overall economy on cryptocurrencies. Research, Practical & Social implications: The research suggests that investors should be careful with cryptocurrencies. Originality/Value: The results are based on the time series analysis that makes the research original. Because there are few examples of time series and volatility analysis of cryptocurrencies.
This study aims to comprehensively review a recently emerging multidisciplinary area related to the application of deep learning methods in cryptocurrency research. We first review popular deep learning models employed in multiple financial application scenarios, including convolutional neural networks, recurrent neural networks, deep belief networks, and deep reinforcement learning. We also give an overview of cryptocurrencies by outlining the cryptocurrency history and discussing primary representative currencies. Based on the reviewed deep learning methods and cryptocurrencies, we conduct a literature review on deep learning methods in cryptocurrency research across various modeling tasks, including price prediction, portfolio construction, bubble analysis, abnormal trading, trading regulations and initial coin offering in cryptocurrency. Moreover, we discuss and evaluate the reviewed studies from perspectives of modeling approaches, empirical data, experiment results and specific innovations. Finally, we conclude this literature review by informing future research directions and foci for deep learning in cryptocurrency.
Research in recent years has shown that Bitcoin is a virtual asset that is used as a medium of exchange and investment tool other than shares and bonds, the development of the digital era has opened up opportunities for Bitcoin to be chosen as part of an investor’s portfolio. The focus of this study is to examine the impact of nine key determinants on Bitcoin price. The data used in the study are daily data starting from January 1, 2018 to January 1, 2022. The main data source is taken from Investing.com, and the estimation method applied is the Vector Error Correction Model (VECM). The main finding shows that Bitcoin Volume impacts Bitcoin Price negatively, which is in line with the demand theory. Another finding is related to the substitute effect of Ethereum Volume, Litecoin Volume, and Gold Volume, each of which influences Bitcoin Price positively, suggesting that these three commodities are substitutes to Bitcoin. In contrast, whereas Oil Volume has an insignificant effect on Bitcoin price in the short term, it has a negative significant impact in the long term. In addition, LQ45 stock index Volume influences Bitcoin Price positively in the short term, suggesting that LQ45 stock index and Bitcoin substitute for each other. Moreover, Google Trends impacts Bitcoin price positively in the long term. In terms of the income effect, either the Indonesian GDP or US GDP has a strong positive effect on Bitcoin price in both the short and long term.
On 16th March 2022, U.S. Federal Reserve increased the interest rate the first time, and in the whole year, U.S. Federal Reserve made seven increments on interest rate. As this will affect the value of dollar, many American financial assets were also affected by it, including ETH, one of the most famous cryptocurrencies. This paper uses the history data of ETH price from January 2018 to July 2023 and constructs ARIMA model without Federal Reserve increasing the interest rate to compare with the reality in order to comprehend how the increasing interest rate affected the price of ETH, and use the model to predict the trend of Ethereum’s price. With the influence of increasing interest rate, the price of Ethereum should decrease. However, after the U.S. Federal Reserve increased interest rate, the price of Ethereum went up for a while then dropped dramatically. And the reasons why this delay appears are the delay of policy and the first increment of interest rate is not attractive enough for investors to change their strategies. That can bring some inspirations to policymakers. They should acknowledge that there will be a delay in the market after the policy is released and they could give some potential signs or preferences on the new policy to reduce the shock to market. For investors, they could pay more attention on relevant policy and make use of the delay to make more money.
What do we know about the interrelations between economic inequality, ecology and the increased use of Bitcoin? The aim of the paper was to empirically test the relationship between economic and ecological effects related to the increase in Bitcoin’s network hashrate in a selection of countries that have the highest influx of crypto-mining. To test these three types of relationships, I collected a dataset concerning Bitcoin indicators, economic indicators and ecological indicators that were obtained from multiple trustworthy sources: OECD, World Bank, Fred Data, World Inequality Database (WID). Handling the data challenges, I used this unique panel dataset to explore the relationship between Bitcoin’s hashrate and two types of outcomes: (i) economic outcomes (such as the GDP which as we know relates to inequalities through the Kuznets curve) or direct measures of inequality (such as, income inequality (GINI) and the share of people with top 1% of income and 1% of wealth), and (ii) ecological outcomes (such as carbon emissions, carbon footprint and electronic waste). I found that the Bitcoin currency associates with certain redistribution of wealth, but the accumulation of crypto-currency-related wealth itself remains still concentrated in the wealth of the top 1%. Also, there is evidence for certain nonlinearities in the relationships with the ecological degradation, echoing the concept of the Kuznets curve.
Purpose This study analyzes the static and dynamic risk spillover between US/Chinese stock markets, cryptocurrencies and gold using daily data from August 24, 2018, to January 29, 2021. This study provides practical policy implications for investors and portfolio managers. Design/methodology/approach The authors use the Diebold and Yilmaz (2012) spillover indices based on the forecast error variance decomposition from vector autoregression framework. This approach allows the authors to examine both return and volatility spillover before and after the COVID-19 pandemic crisis. First, the authors used a static analysis to calculate the return and volatility spillover indices. Second, the authors make a dynamic analysis based on the 30-day moving window spillover index estimation. Findings Generally, results show evidence of significant spillovers between markets, particularly during the COVID-19 pandemic. In addition, cryptocurrencies and gold markets are net receivers of risk. This study provides also practical policy implications for investors and portfolio managers. The reached findings suggest that the mix of Bitcoin (or Ethereum), gold and equities could offer diversification opportunities for US and Chinese investors. Gold, Bitcoin and Ethereum can be considered as safe havens or as hedging instruments during the COVID-19 crisis. In contrast, Stablecoins (Tether and TrueUSD) do not offer hedging opportunities for US and Chinese investors. Originality/value The paper's empirical contribution lies in examining both return and volatility spillover between the US and Chinese stock market indices, gold and cryptocurrencies before and after the COVID-19 pandemic crisis. This contribution goes a long way in helping investors to identify optimal diversification and hedging strategies during a crisis.
Non-fungible tokens (NFTs) are decentralized digital tokens to represent the unique ownership of items. Recently, NFTs have been gaining popularity and at the same time bringing up issues, such as scams, racism, and sexism. Decentralization, a key attribute of NFT, contributes to some of the issues that are easier to regulate under centralized schemes, which are intentionally left out of the NFT marketplace. In this work, we delved into this centralization-decentralization dilemma in the NFT space through mixed quantitative and qualitative methods. Centralization-decentralization dilemma is the dilemma caused by the conflict between the slogan of decentralization and the interests of stakeholders. We first analyzed over 30,000 NFT-related tweets to obtain a high-level understanding of stakeholders' concerns in the NFT space. We then interviewed 15 NFT stakeholders (both creators and collectors) to obtain their in-depth insights into these concerns and potential solutions. Our findings identify concerning issues among users: financial scams, counterfeit NFTs, hacking, and unethical NFTs. We further reflected on the centralization-decentralization dilemma drawing upon the perspectives of the stakeholders in the interviews. Finally, we gave some inferences to solve the centralization-decentralization dilemma in the NFT market and thought about the future of NFT and decentralization.
This study examines Bitcoin's potential as an inflation hedge in different countries, including the United States, the Eurozone, the Philippines, Ukraine, Canada, India, and Nigeria. The study reveals varying results across countries using the Vector Error Correlation Model (VECM) with secondary monthly data from January 2012 to June 2023 for Bitcoin prices and inflation rates. Bitcoin exhibits an insignificant short-term relationship in the United States but a significant long-term negative correlation, suggesting it may not be a reliable inflation hedge. Similarly, no significant relationship was found in the Eurozone, the Philippines, Ukraine and Nigeria, indicating Bitcoin's limited effectiveness as an inflation hedge. Contrastingly, the study identifies a significant positive relationship between Bitcoin and inflation in Canada and India, indicating potential hedging against inflation within these economies. Therefore, investors, portfolio managers, and policymakers should consider these country-specific findings when evaluating Bitcoin's role as an inflation hedge. Furthermore, this study contributes valuable insights into cryptocurrencies and their potential in financial risk management.
V. Anandhabalaji, M. Babu, J. Gayathri, J. Sathya · 7 authors
The present study proposes to investigate the influence of the covid-19, on the adjusted closing price of the digital currency based on energy consumption during the process of mining. The study employed the secondary data analysis of top ten market capitalization of cryptocurrencies with the combination of high energy consume mechanism (proof of work) and low energy consume mechanism (proof of stake). Statistical tools like Descriptive analysis,Augmented Dickey-Fuller (ADF) test, ARCH, and GARCH models were used in the study. The present study finds that the prices of cryptocurrencies were highly volatile. This study could assist investors towards better understanding of the dynamics of the cryptocurrency market based on energy consumption which helps them to make more effective decisions, on investing cryptocurrencies with a scientific approach.
This article examines the causal relationship between stock indices and cryptocurrencies during the ongoing Russia-Ukraine war. The econometric investigation covers the period from February 24, 2022 to April 12, 2023, and focuses on seven stock market indices (S&P 500, DAX, CAC40, Nikkei, TSX, MOEX, and PFTS) and seven cryptocurrencies (Bitcoin, Ethereum, Litecoin, Dash, Ripple, DigiByte, and XEM). In this article, we investigate how investors react to fluctuations in financial assets and whether they seek safe havens in cryptocurrencies. We use dynamic causality in the Granger (1969) sense to detect a possible causal relationship in the short term, and seven models to estimate the long-term relationship between cryptocurrencies and financial assets. Our results show that in the short term, three famous cryptocurrencies (Bitcoin, Ethereum, and Ripple) and two digital assets with minor popularity (XEM and DigiByte) are impacted by the German, Russian, and Ukrainian stock markets. In the long term, we find a positive and significant effect of the American, Canadian, French and Ukrainian stock market indices on Bitcoin. These findings suggest that the stability of traditional financial markets during the current war period can be explained on the one hand by investors' fears of an unstable business climate, and on the other hand, by speculators' interest in new electronic products that are perceived as hedging instruments and safe havens in times of crisis.
Open access
Market Dynamics and Volatility
Economic Sanctions and International Relations
Environmental and Biological Research in Conflict Zones
This research investigates the function of price discovery between the Bitcoin futures and the spot markets while also analyzing the impact of investor sentiment and attention on these markets. This study utilizes various statistical models to examine the short-term and long-term relations between these variables, including the bivariate Granger causality model, the ARDL and NARDL models, and the Johansen cointegration procedure with a vector error correction mechanism. The results suggest that there is no statistical evidence of price discovery between the Bitcoin spot price and futures, and the term structure of the Bitcoin futures neither enriches nor impairs this lead lag relation. However, the study finds robust evidence of a long-run cointegrating relation between the two markets and the presence of asymmetry in them. Moreover, this research indicates that investor sentiment exhibits a lead lag relation with both the Bitcoin futures and the spot markets, while investor attention only leads to the Bitcoin spot market, without showing any lead lag relation with the Bitcoin futures. These findings highlight the crucial role of investor behavior in affecting both Bitcoin futures and spot prices.
Muhammad Naveed, Shoaib Ali, Mariya Gubareva, Anis Omri
Using an event study approach, we examine how the forex, metal, energy, and cryptocurrency markets responded to the SVB collapse. We observe that the forex and metal markets respond positively on event and post-event days. In contrast, the cryptocurrency market reacts negatively but generates positive abnormal returns, indicating that investors may seek refuge in these purported safe-havens. However, the energy market responded adversely to the event, and the trend continued in the aftermath. The study advocates the need for monitoring and minimizing financial contagion risk due to the increased interconnectedness of the financial markets. Our findings highlight the perilous consequences of the SVB collapse, as it triggered contagious effects that may spread throughout the global financial markets. Therefore, investors and financial institutions must diversify their portfolios across various asset classes, which can help mitigate the risks of such events.