Bu çalışmanın amacı COVID-19 pandemisi döneminde yatırımcı kararlarında meydana gelen değişimleri pandemi öncesi ve sonrası dönemler şeklinde ortaya koyarak finansal sistem içerisinde yer alan ve etkilenen tarafların kararlarında yol gösterici veriler ortaya koymak ve literatüre katkıda bulunmaktır. Çalışma Türkiye örneği üzerinden COVID-19 pandemisi öncesi ve sonrasını içerecek şekilde ve en son güncel değerlerle 01/01/2018-24/02/2023 dönemini kapsamaktadır. Analizler Toda-Yamamoto prosedürünü Fourier fonksiyonu (FTY) ile zenginleştiren bir nedensellik testi kullanılarak yapılmıştır. Çalışma yapılan dönem Chow yapısal kırılma testi ile dört alt döneme ayrılmıştır. Çalışmada USD, Altın (AU) ve Bitcoin değişkenleri ile BIST 100 endeksi arasındaki nedensellik ilişkisi analiz edilmiştir. Yapılan analiz sonuçları pandemi öncesi ve sonrası dönemin birbirinden oldukça farklı nedensellik ilişkileri ortaya koyduğunu, pandeminin ilk şok dalgasında altının güvenli liman özelliğinin ortaya çıktığını, devam eden pandemi sürecinde ise altının bu özelliğini kaybettiği ve ele alınan tüm değişkenler arasındaki nedenselliklerin belirginleştiği görülmüştür. Pandemi sonrası dönemde ise pandemi öncesi döneme kıyasla sadece altının aynı şekilde tek taraflı nedensellik ilişkisine sahip olduğu diğer değişkenler olan USD ve Bitcoin’in BIST100 değişkeniyle nedensellik ilişkisinin tamamen kaybolduğu görülmüştür. Çalışma kriz dönemlerinin her bir aşamasında yatırımcı davranışlarının analiz edilmesi açısından literatüre önemli bir katkı sunmaktadır.
Sitara Karim, Brian M. Lucey, Muhammad Abubakr Naeem, Larisa Yarovaya
Abstract The current study investigates the extreme risk dependence between green bonds and financial markets by employing the dual approaches of time‐varying optimal copula and extreme risk spillover analysis of dynamic conditional Value‐at‐Risk. We report significant symmetric (asymmetric) tail‐dependent copulas in the upper (lower) tails characterizing independent regimes. Green bonds offer sufficient diversification, safe‐haven, and hedging opportunities during stable and distressing times to financial markets. The extreme risk spillovers revealed that COVID‐19 transformed the spillovers between green bonds and financial markets except Bitcoin. We proposed insightful implications for policymakers, governments, investors, and portfolio managers to relish the findings for their investment avenues.
Using daily closing price observations between November 2017 and February 2023, this paper documents how the shocks of a cryptocurrency ETF resonate with ETFs representing traditional asset classes in terms of price and volatility. We find price transmission from the cryptocurrency ETF into the ETFs of several currencies, small-cap equities, and inflation. Risk propagation from the cryptocurrency ETF flows toward ETFs constituted of equities of various sizes, oil prices, high-yield corporate bonds, and inflation. There is scant evidence of transmission from ETFs with underlying conventional assets into the cryptocurrency ETF. The findings bear implications for low-cost risk management strategies.
Abstract The ınvestment decisions of institutional and individual investors in financial markets are largely influenced by market uncertainty and volatility of the investment instruments. Thus, the prediction of the uncertainty and volatilities of the prices and returns of the investment instruments becomes imperative for successful investment. In this study we seek to identify the best fit model that can predict the volatility of return of Bitcoin, which is in high demand as an investment tool in recent times. Using the opening data of weekly Bitcoin prices for the period of 11.24.2013–03.22.2020, their logarithmic returns were calculated. The stationarity properties of the Bitcoin return series was tested by applying the ADF unit root test and the series were found to be stationary. After reaching the average equation model as ARMA (2.2), it was tested whether there was an ARCH effect in the ARMA (2,2) model. As a result of the applied ARCH-LM test, it is reached that the residuals of the average equation model selected have ARCH effect. Volatility of Bitcoin return series after detection of ARCH effect has been tried to predict with conditional variance models such as ARCH (1), ARCH (2), ARCH (3), GARCH (1,1), GARCH (1,2), GARCH (1,3), GARCH (2,1), GARCH (2,2), EGARCH (1,1) and EGARCH (1,2). While the obtained findings indicate that the best model is in the direction of GARCH (1,1) according to Akaike info criterion, it was found that GARCH (1,1) model does not have ARCH effect as a result of the applied ARCH-LM test. Thus, our empirical findings highlight an ample guide on appropriate modeling of price information in the Bitcoin market.
Abstract This study investigates the herding behavior in the cryptocurrency market during the period of the Russia and Ukraine conflict using intraday cryptocurrency price data of the five largest cryptocurrencies in terms of market capitalization. The empirical results indicate an anti-herding behavior during the whole period of the conflict, especially after the conflict officially happens. The research contributes to the growing literature on herding behavior in the cryptocurrency market by using intraday data and examining the Russia–Ukraine conflict period.
Leonardo H.S. Fernandes, JOSÉ W. L. SILVA, Aurelio F. Bariviera, Kleber E S Sobrinho · 5 authors
This paper sheds light on the changes suffered in cryptocurrencies due to the COVID-19 shock through a non-linear cross-correlations and similarity perspective. We have collected daily price and volume data for the seven largest cryptocurrencies considering trade volume and market capitalization. For both attributes (price and volume), we calculate their volatility and compute the Multifractal Detrended Cross-Correlations (MF-DCCA) to estimate the complexity parameters that describe the degree of multifractality of the underlying process. We detect (before and during COVID-19) a standard multifractal behaviour for these volatility time series pairs and an overall persistent long-term correlation. However, multifractality for price volatility time series pairs displays more persistent behaviour than the volume volatility time series pairs. From a financial perspective, it reveals that the volatility time series pairs for the price are marked by an increase in the non-linear cross-correlations excluding the pair Bitcoin vs Dogecoin (í µí»¼ í µí±¥í µí±¦ (0) = −1.14%). At the same time, all volatility time series pairs considering the volume attribute are marked by a decrease in the non-linear cross-correlations. The K-means technique indicates that these volatility time series for the price attribute were resilient to the shock of COVID-19. While for these volatility time series for the volume attribute, we find that the COVID-19 shock drove changes in cryptocurrency groups.
Given the volatile nature of cryptocurrencies, accurately forecasting cryptocurrency volatility and understanding its determinants are crucial. This paper applies machine learning (ML) techniques to forecast cryptocurrency volatility using internal determinants (e.g., lagged volatility, previous trading information) and external determinants (e.g., technology, financial, and policy uncertainty factors). Both Random Forest and Long Short-Term Memory (LSTM) networks significantly outperform traditional volatility models such as GARCH. Furthermore, we explore two optimization models—Genetic Algorithm and Artificial Bee Colony—to tune the hyper-parameters of LSTM. Our results indicate that the application of these optimization models substantially improves forecasting performance. Moreover, using SHapley Additive exPlanations, an interpretation method, we find that internal determinants play the most important roles in volatility forecasts. Finally, our results show that models trained with determinants from multiple cryptocurrencies outperform those trained with determinants from a single cryptocurrency, suggesting that considering a broader range of determinants can capture the complex dynamics in the cryptocurrency market.
Abstract This study measures the convergence and divergence of major cryptocurrencies by applying two distance measures used in machine learning. Particularly, the time-varying Euclidean distance measure was constructed by combining the first four moments (i.e. mean, variance, skewness and kurtosis) of the return distributions of cryptocurrencies following the $$\ell ^{2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>ℓ</mml:mi> <mml:mn>2</mml:mn> </mml:msup> </mml:math> -normalisation. It was found that major cryptocurrencies converged to the centroid during the 2018 market crash, but diverged before and after the crash. Their divergence could be due to the uncertainty arising from market news and regulatory events. In addition, Bitcoin cosine similarity measure was developed to provide further insights into the relationship between Bitcoin and other cryptocurrencies. This cosine similarity shows how each cryptocurrency moves relative to Bitcoin, which is not captured by the Euclidean distance. More importantly, it was demonstrated that the divergence of major cryptocurrencies from their centroids can improve Markowitz’s efficient frontier and provide more diversification benefits to investors and portfolio fund managers. Finally, a profitable trading strategy was provided based on the Euclidean distance.
Bitcoin is a peer-to-peer form of digital currency proposed in 2008. Unlike other currencies, Bitcoin does not rely on a specific institution to issue it, it is based on a specific algorithm that generates it through a large number of calculations. In some countries, government agencies, central banks and academia regard Bitcoin is a virtual currency rather than a currency. This is because of Bitcoin’s high volatility, which does not have the two basic functions of the unit of account and the store of value that are unique to the currency. In recent years, Bitcoin has seen increasing media coverage and other cryptocurrency portfolios, as well as the significant capital gains have been seen in the high volatility environment. In this paper, we shed light on the low correlation of Bitcoin with traditional investment assets, making Bitcoin a potentially high-quality source of portfolio diversification. The results of the finding suggest that Bitcoin investments offer significant diversification benefits and should be included in optimal portfolios. In addition to this, we find that hedging strategies involving gold, oil, stocks and Bitcoin significantly reduce portfolio risk.
This study investigates the co-movement patterns of Asia technology stock indices and cryptocurrencies during the COVID-19 pandemic. The analysis examines Bitcoin and Ethereum, China’s Tech index (XA90), and India’s Tech index (NSEIT) from 2017 to 2021, representing both before and during COVID-19. To visually explore the co-movement between these variables, a bi-wavelet method is employed. This approach allows for an examination of how these variables move together over time coherently. There were noticeable changes in the co-movement patterns between technology stock indices and cryptocurrencies during COVID-19 compared to before the pandemic. The duration of co-movements decreased significantly after the emergence of COVID-19. The previous financial crisis had a longer time horizon for joint movement, lasting 256 days. However, during the pre-COVID-19 period, XA90 exhibited a strong co-movement with Bitcoin over this extended period but weakened afterward when COVID-19 emerged. Conversely, NSEIT showed a significant co-movement with both Ethereum and Bitcoin in the initial stages of the pandemic. Before that period, NSEIT had muted price movements along with BTC. These changes in price co-movements suggest shifts in herding behavior due to the pandemic. Notably, cryptocurrency markets have demonstrated faster recovery compared to technology stock markets.
In today's world, with the internet and finance closely integrated, the global-ization of cryptocurrency has deepened, prompting people to pay more atten-tion to the impact of cryptocurrency on the entire financial market. This pa-per provides a comprehensive review of relevant literature from five aspects: the development of cryptocurrency, price changes, the impact of cryptocur-rency on the stock market, the impact of cryptocurrency on the banking in-dustry, and the currency and commodity characteristics of cryptocurrency. The author also compares the price curves (in USD) of the S&P 500, ETH, and BTC from March 21, 2022, to March 21, 2023, and hopes to gain in-sights into the relationship between stocks and cryptocurrencies. Our re-search shows that the emergence of cryptocurrency has had a significant im-pact on the financial market, particularly in the banking industry. Although the emergence of cryptocurrency has added vitality to the financial market and increased investment options, the instability of cryptocurrency has also brought risks to investors. As the cryptocurrency market continues to ex-pand, policymakers and investors need to understand its potential impact on traditional financial instruments and the broader economy.
Abstract The cryptocurrency crash on the 5th of September, 2018, resulted in price decreases in 95 of the 100 leading digital currencies. We obtained millisecond data of some of the more prominent cryptocurrencies–bitcoin, ethereum, ripple, bitcoin cash and eos–and some of the smaller cryptocurrencies–neo, nem, omg, tezos and lisk–that were most affected in the crash and investigated what caused the digital market to collapse. We find that the behaviour of the more prominent cryptocurrencies and bitcoin, in particular, was the dominant factor behind the crash. We also find that smaller cryptocurrencies followed the behaviour of the larger ones in the crash. Furthermore, our empirical findings show that the trading behaviour of cryptocurrency traders (CTs) did not trigger the digital market crash. We propose the introduction of a single-cryptocurrency circuit breaker most prominent largest cryptocurrency–bitcoin–that will halt trading during market disruptions.
This study explores the stylized facts, volatility clustering, other highly irregular behaviour, and risk measures of cryptocurrencies’ returns. By analysing bitcoin, ripple, and ethereum daily data we establish evidence of strong dependencies among analysed cryptocurrencies. This paper provides new insights about cryptocurrency behaviour and the main measures of risk and detailed comparative analysis with tech-stocks. Comprehensive research on stylized facts confirmed high risk for both cryptocurrencies and tech-stocks with cryptocurrencies being even riskier. Empirical research findings are useful in developing dependence and risk strategies for investment and hedging purposes, especially during more volatile periods in the markets as there was confirmed existence of volatility clusters when high volatility periods are followed by low volatility periods. Sensitivity analysis and measures of Value-at-Risk (VaR) and Expected Shortfall (ES) show the amount of losses investors can expect in the worst case scenario. Our results confirm the existence of predictability, volatility clustering, and possibilities for arbitrage opportunities. Findings could be beneficial for investors and policymakers as well as for scientific purposes as findings give us a better understanding of the behaviour of cryptocurrencies.
Ethereum is a major public blockchain. Besides being the second-largest digital currency by market capitalization for its cryptocurrency, the Ether (Ξ), it is also the foundation of Web3 and decentralized applications, or DApps, that are fuelled by Smart Contracts. At the time of this writing, Ethereum still uses Proof of Work (PoW) consensus algorithm to ensure the integrity of the blockchain and to prevent double spend. PoW requires the participation of miners, who are incentivized to assemble blocks of transactions by being rewarded with cryptocurrency paid by transaction originators and by the blockchain network itself via newly minted Ξ. Network fees for transaction submissions are called gas, by analogy to the fuel used by cars, and are negotiable. They are also highly volatile and hence it is critical to predict the direction they are heading into, so that one can time transaction submissions, when feasible. There have been several efforts to predict gas prices, including usage of large Mempools, analysis of committed blocks, and more recent ones using Facebook's Prophet model [Taylor, S. J., & Letham, B. (2017). Forecasting at scale. PeerJ Preprints, 5, e3190v2. https://doi.org/10.7287/peerj.preprints.3190v2]. In this study, we introduce an innovative approach that employs the DeepAR [Salinas, D., Flunkert, V., Gasthaus, J., & Januschowski, T. (2020). Deepar: Probabilistic forecasting with autoregressive recurrent networks. International Journal of Forecasting, 36(3), 1181–1191. https://doi.org/10.1016/j.ijforecast.2019.07.001] model, known for its superior forecasting accuracy over conventional methods by virtue of its ability to learn from multiple related time series. This methodology not only offers immediate advantages but also holds promise for ongoing enhancements. We substantiate our claims through empirical testing, utilizing data extracts from the Ethereum blockchain and cryptocurrency price feeds. This document is an extended version of our ICCS 2022 paper on the same topic. In this paper, we dive deeper into the internals of DeepAR forecasting algorithm [Salinas, D., Flunkert, V., Gasthaus, J., & Januschowski, T. (2020). Deepar: Probabilistic forecasting with autoregressive recurrent networks. International Journal of Forecasting, 36(3), 1181–1191. https://doi.org/10.1016/j.ijforecast.2019.07.001], analyse the correlation between the on-chain/off-chain sample data, and describe additional experiments that empirically prove our findings and, finally, perform a comparison of our outputs with those from the Prophet [Taylor, S. J., & Letham, B. (2017). Forecasting at scale. PeerJ Preprints, 5, e3190v2. https://doi.org/10.7287/peerj.preprints.3190v2] model.
Muneer Shaik, Mustafa Raza Rabbani, Youssef Tarek Nasef, Umar Nawaz Kayani · 5 authors
In this study, we investigate the dynamic volatility connectedness of fintech, innovative technology communication, and cryptocurrency indices for the period from June 2018 to June 2022. We investigate the connectivity and risk spillovers before and after the COVID-19 period to understand the volatility fluctuations by employing the dynamic connectedness measures based on TVP-VAR methodology. We find that volatility connectedness is strong among the Fintech, and cryptocurrency indices and it increase further during uncertainty caused due to COVID-19 pandemic & also during escalations of Russian-Ukraine war period compared to the pre-pandemic levels. We identify the net transmitters and net recipients of volatility among the fintech, innovative technology communication, and cryptocurrency indices. We observe that spillovers among the variables under study are dynamic in nature and shift from net recipients to net transmitters of volatility and viceversa during different time periods Our study has beneficial implications for policymakers, regulators, investors, and financial market constituents to redevelop their existing strategies and understand the fourth industrial revolution and new economies' indices to avoid financial losses during the financial markets’ turmoil.
Non-fungible tokens are a revolutionary concept that combines art, authenticity, proof of ownership and enables large-scale commerce. Their value does not come from their use in financial transactions, but from the fact that they are linked to specific assets, whether digital or real. Non-fungible tokens thus represent a new driving force in the areas of digital ownership. Owners of these tokens can earn huge sums at a time when the art market is on the verge of revolution. The non-fungible token market experienced enormous growth in 2021 with traders investing a huge amounts of billions of dollars worth of cryptocurrencies into digital collectibles. Since 2021 the transaction activity in this area cooled, although the number of active investors continued to grow in 2022. While the popularity of certain non-fungible token collectibles can fluctuate depending on market conditions, some traders may try to manipulate the prices of certain non-fungible tokens to make them appear more valuable. The token will be sold at a higher price to a new wallet, which is also controlled by the original owner. Transactions between wallet addresses are saved on a blockchain and can be accessed publicly, so that anyone can see when the token was traded and for how much it was sold. However, wallet addresses contain no identifying information making it very difficult to discern who is behind a transaction and whether two addresses are owned by the same individual. This process is called wash trading and its analysis is a goal of this contribution.
Abna Ajeesh, Lekshmi Prakash, Mohammad Ali Moni, V. Sreeraj
This study used the CMC 200 Index as a cryptocurrency market benchmark to examine complex volatility patterns of cryptocurrencies. The growing interest in cryptocurrencies and the necessity to analyse their market dynamics, especially in the face of external inputs like news, prompted the study. The study examined market responses and causes to diverse stimuli using rigorous analytical models including GARCH, EGARCH, FIGARCH, and News Impact Curve. The asymmetricvolatility or “leverage effect” showed that negative events or news have a greater impact on market volatility than positive developments of similar magnitude. Symmetric volatility indicated large price shifts regardless of news direction. The left-skewed news effect curve emphasises this asymmetric volatility, demonstrating that negative news has a greater impact on market dynamics. The curve’s leftward skew shows the market’s increased susceptibility to pessimism. This suggests that negative news might undermine investor confidence in the crypto market more than favourable news. Beyond these initial reactions, the research revealed a “long memory” in market volatility, suggesting that prior shocks continue to affect its volatility over time. These studies emphasise the importance of investor sentiment in crypto market. Investors in this volatile market need honest communication and strong risk management due to the leverage impact and prior experience.
Bitcoin has received a lot of attention as a cryptocurrency in recent years. The paper focuses on determining if Bitcoin would replace world currency in the future, which compares Bitcoin with the US dollar and gold. The article considers qualitative analysis to discuss the bitcoin's characteristics based on Karl Marx's five different categories in his book Das Kapital: measure value, means of circulation, means of hoarding, means of payment, and universal currency. The paper presents the advantages and disadvantages related to Bitcoin compared to the US dollar and gold. The article finds that Bitcoin is more secure in saving and privacy. Also, it wouldn't be affected by inflation. However, disadvantages are also present when applying Bitcoin in the market. The volatility and value of storing would be a challenge to solve. The lack of population using it and the limitations of transactions would also be problems. The research illustrates that Bitcoin can't replace world currency in the short term, but in the long term, Bitcoin would be the mainstream currency to use in real life due to the performance of people, governments, and the world economy. The article is the first research to utilize Marx's theory to analyze whether Bitcoin could replace world currency in the existing literature. This paper recommends that governments of all countries establish a unified regulatory policy and security mechanism on a global scale to ensure the legitimacy, stability, and security of Bitcoin so that Bitcoin can truly become a world currency.
Methodologies to infer financial networks from the price series of speculative assets vary, however, they generally involve bivariate or multivariate predictive modelling to reveal causal and correlational structures within the time series data. The required model complexity intimately relates to the underlying market efficiency, where one expects a highly developed and efficient market to display very few simple relationships in price data. This has spurred research into the applications of complex nonlinear models for developed markets. However, it remains unclear if simple models can provide meaningful and insightful descriptions of the dependency and interconnectedness of the rapidly developed cryptocurrency market. Here we show that multivariate linear models can create informative cryptocurrency networks that reflect economic intuition, and demonstrate the importance of high-influence nodes. The resulting network confirms that node degree, a measure of influence, is significantly correlated to the market capitalisation of each coin ($ρ=0.193$). However, there remains a proportion of nodes whose influence extends beyond what their market capitalisation would imply. We demonstrate that simple linear model structure reveals an inherent complexity associated with the interconnected nature of the data, supporting the use of multivariate modelling to prevent surrogate effects and achieve accurate causal representation. In a reductive experiment we show that most of the network structure is contained within a small portion of the network, consistent with the Pareto principle, whereby a fraction of the inputs generates a large proportion of the effects. Our results demonstrate that simple multivariate models provide nontrivial information about cryptocurrency market dynamics, and that these dynamics largely depend upon a few key high-influence coins.
In this article, we delve into the challenging problem of forecasting cryptocurrency prices using mathematical extrapolation techniques. We highlight the scarcity of research in this domain, underlining the necessity for in-depth investigation. The article outlines the unresolved issues related to extrapolation-based cryptocurrency price prediction, such as market volatility and non-linearity. It primarily aims to showcase the potential of extrapolation for predicting bitcoin prices. The analysis involves a year-long bitcoin price trend, with the application of linear and polynomial extrapolation methods. While some correlation exists, notable discrepancies, especially during abrupt price changes, are evident. The conclusion emphasizes the limitations of extrapolation and advises a diversified approach to cryptocurrency investment decisions, considering various factors beyond mathematical data. In this article, we delve into the challenging problem of forecasting cryptocurrency prices using mathematical extrapolation techniques. We highlight the scarcity of research in this domain, underlining the necessity for in-depth investigation. The article outlines the unresolved issues related to extrapolation-based cryptocurrency price prediction, such as market volatility and non-linearity. It primarily aims to showcase the potential of extrapolation for predicting bitcoin prices. The analysis involves a year-long bitcoin price trend, with the application of linear and polynomial extrapolation methods. While some correlation exists, notable discrepancies, especially during abrupt price changes, are evident. The conclusion emphasizes the limitations of extrapolation and advises a diversified approach to cryptocurrency investment decisions, considering various factors beyond mathematical data.
Timothy Kayode Samson, Christian Elendu Onwukwe, Adedoyin Isola Lawal
With escalating public interest in the cryptocurrency market, largely driven by its perceived potential for rapid wealth accumulation and various advantages over traditional currencies, there is an imperative to understand its inherent volatility.This study addresses the dynamic behaviour of cryptocurrencies by utilizing skewed error innovation distributions to model the volatility of five key cryptocurrencies.Data was sourced from Yahoo Finance, encompassing daily closing prices from September 11, 2017, to April 8, 2022.The significance of the skewness parameter in all optimal volatility models (p<.05) substantiates the application of skewed error innovation distributions.Notably, the observed influence of past negative events on volatility was consistently greater than that of positive events across most examined cryptocurrencies.While Value at Risk (VaR) models are frequently used for risk measurement in this domain, this study's findings suggest that their reliability is not universal across all cryptocurrency cases.Consequently, caution is advised when employing VaR models for risk assessment associated with cryptocurrencies.
Muhammad Mahmudul Karim, Md Hakim Ali, Larisa Yarovaya, Md Hamid Uddin · 5 authors
Implied volatility has consistently demonstrated its reliability as a superior estimator of the expected short-term volatility of underlying assets. In this study, we employ the newly constructed robust model-free implied volatility (MFIV) indices for Bitcoin and Ethereum (BitVol and EthVol) to explore the asymmetric return-volatility relationship of these cryptocurrencies through the lens of behavioral finance theories. Utilizing the asymmetric quantile regression model (QRM) and the Non-linear ARDL (NARDL) approach, our results reveal a notable difference from equities. Both positive and negative return shocks in the cryptocurrency market lead to an increase in volatility. However, during high volatility regimes, positive (negative) return shocks exert a more substantial impact on positive innovations of volatility for Bitcoin (Ethereum) compared to negative (positive) return shocks. The degree of asymmetry steadily intensifies as we progress from medium to uppermost quantiles of the volatility distribution. These observed phenomena can be attributed to behavioral aspects among market participants, including noise trading, behavioral biases, and fear of missing out (FOMO). Our findings hold significant implications for various aspects of cryptocurrency trading, portfolio hedging strategies, volatility derivatives pricing, and risk management.
The study investigates the relationship between the returns of Non-Fungible Tokens (NFT) and its categories; and fear indices during times of crisis. The fear indices considered are Global Fear Index (GFI), Global Economic Policy Uncertainty Index (GEPU), Twitter based Economic Uncertainty Index (TEU), Global Consumer Confidence Index (CCI), Infectious Diseases Equity Market Volatility Index (IDEMV) and Crypto Volatility Index (CVI). Employing Granger Causality Test, Autoregressive Distributed Lag technique and ARDL Bounds test on data for the period starting 1st February 2020 and ending 28th February 2022, it is found that short run association exists between TEU, CVI and NFT returns. Further, GFI leads NFT Art returns while TEU leads NFT Metaverse returns by lag 5 and lag 2 respectively. No association between fear metrics and NFT Collectible, NFT Game and NFT utility is observed. No long run association in found between NFT returns and fear indices except TEU which influences NFT returns. It is concluded that NFT, NFT Art and NFT Metaverse returns have positive association to at least one fear index during times of turmoil, especially for the short run.