This study introduces closed-form formulas for valuing European call options, assuming that Bitcoin follows a compound Poisson process. Additionally, instantaneous forward interest rates are considered in the Heath-Jarrow-Morton model, which includes a jump component. To address the impacts of systematic risk on Bitcoin price and interest rate, we model two stochastic processes using a correlated bivariate jump-diffusion model to capture individual jumps and systematic co-jumps. This study provides analytic formulas for pricing Bitcoin call options and zero-coupon bonds under the correlated jump-diffusion Heath-Jarrow-Morton model. Numerical analysis shows how co-jump intensity affects the prices of both zero-coupon bonds and Bitcoin call options. We specifically look at how these prices change in response to co-jump intensity across three different instantaneous forward rate term structures. The findings show that the prices of Bitcoin call options are contingent on the term structure types of zero-coupon bonds. In addition, the interaction of co-jump intensity and types of term structure also affects Bitcoin option prices. The practical significance of this study is to provide a comprehensive model to evaluate Bitcoin call options and enhance risk management strategies in the Bitcoin market when the Bitcoin market encounters changes in monetary policy or changes in macroeconomic conditions.
Since the creation of Bitcoin in 2008, these digital currencies have not only attracted widespread attention from the public and economists, but have also triggered a rethinking of the nature of money, the store of value, and the modes of exchange. This paper explores the transformative impact of Bitcoin and digital currencies on global finance, emphasizing their emergence as a challenge to the traditional concept of money and a paradigm shift. Furthermore, the paper delves into the birth of Bitcoin, its decentralized nature and its pioneering role in the field of digital currencies, discusses the historical background, technological underpinnings, and monetary functions of digital currencies, and highlights the potential and challenges of their integration into the financial system. It aims to examine the characteristics and functions of bitcoin and digital currencies in the contemporary financial landscape, focusing on how they can challenge traditional monetary policy as an emerging financial asset, as well as their potential impact and integration challenges in the global economic system.
Mohammad Ali Al-Afeef, Raed Walid Al-Smadi, Arkan Walid Al-Smadi
This study aims to analyze the link between Perceived Volatility Reduction (PVR), Risk Perception, Stablecoin Usage Frequency, Market Confidence, and Stablecoin Adoption (SA). The primary goal is to determine if and to what degree these variables impact stablecoin adoption. We created a questionnaire to gather information from 198 Malaysians. To analyze the research model and test the hypotheses, the Structural Equation Modeling-Partial Least Squares (SEM-PLS) method was utilized. According to the findings, there is a strong and positive association between Perceived Volatility Reduction (PVR) and Stablecoin Adoption (SA). Market players are more likely to adopt stablecoins if they perceive them as useful instruments for mitigating the severe price volatility inherent in traditional cryptocurrencies. This finding emphasizes the importance of risk perception and market stability in driving market behavior. Trust in stablecoin systems, transparency, and regulatory, compliance influenced PVR and SA. The study's findings underscore the significance of perceived volatility reduction (PVR) in driving stablecoin adoption (SA), highlighting the importance of risk perception and market stability. Trust in stablecoin systems, transparency, and regulatory compliance emerge as crucial factors influencing PVR and SA. These insights offer valuable guidance for investors navigating the cryptocurrency market, governments managing stablecoin supply, and scholars studying trust dynamics in the cryptocurrency ecosystem.
Ijaz Younis, Himani Gupta, Anna Min Du, Waheed Ullah Shah · 5 authors
Decentralized finance (DeFi) has become of significant interest for investors in both the financial and digital sectors. We use a time-varying parameter vector autoregression (TVP-VAR) approach to estimate the static and dynamic connections between and within DeFi, G7 banking, and equity markets. We focus on critical events such as the COVID-19 pandemic, the cryptocurrency bubble, and the Russia-Ukraine conflict. The results highlight interconnectedness and significant spillovers within and between the markets, especially during the COVID-19 pandemic. Notably, there were significant spillover effects from the G7 banking and equity markets to Japan and DeFi assets. The findings demonstrate a robust connection between DeFi platforms, G7 banking, and stock markets throughout these tumultuous periods. Policymakers, investors, and entrepreneurs are recommended to keep a close eye on changes in traditional banking and equity markets to adjust the risk of DeFi assets.
Bu çalışmanın temel amacı küresel ekonomi politika belirsizliği (GEPU) endeksinin bitcoin üzerindeki etkisini incelemektir. Değişkenler arasındaki ilişkiyi ortaya koymak için Ağustos 2010 – Mart 2023 dönemine ait veriler kullanılmıştır. Küresel ekonomi politika belirsizliği (GEPU) endeksi ile bitcoin arasındaki ilişkiyi açıklamak için normal dağılmama durumunu dikkate alan RALS eşbütünleşme testleri kullanılmıştır. Değişkenlerin I(1) düzeyinde durağanlaştığı saptanmış, daha sonra RALS-ADL ve RALS-EG2 testleri uygulanmıştır. RALS-ADL ve RALS-EG2 eşbütünleşme testleri sonuçlarına göre GEPU endeksi ile bitcoin arasında eşbütünleşme ilişki olduğu tespit edilmiştir. Modelin uzun dönem katsayısına göre GEPU endeksindeki yüzde bir birimlik artış bitcoini 0.092 oranında artırdığı saptanmıştır.
Ahmed Bossman, Mariya Gubareva, Samuel Kwaku Agyei, Xuan Vinh Vo
Abstract We provide empirical evidence supporting the economic reasoning behind the impossibility of diversification benefits and the hedge attributes of cryptocurrencies remaining in force during the downside trends observed in bearish financial markets. We employ a spillover connectedness model driven by time-varying parameter vector autoregressions on daily data covering January 2018 to November 2022 to analyze spillover transmissions between conventional and digital markets, focusing on the role of stablecoin issuances. We study the stock, bond, cryptocurrency, and stablecoin markets and find very high connectedness, which varies over time in response to up/down trends in financial markets. The results show that during financial turmoil, cryptocurrencies amplify downside risks rather than serve as diversifiers. In addition to risky assets from conventional financial markets, cryptocurrencies champion the transmission of spillovers to digital and conventional markets. In contrast, changes in stablecoin issuances produce few shocks because of their pegged prices, but they facilitate investors’ switch from volatile cryptos to more stable digital instruments; that is, we observe a phenomenon designated by us as the “flight-to-cryptosafety.” We draw insightful conclusions, provoking new thinking regarding portfolio hedge strategies that could potentially benefit investors when searching for less volatile investment performance.
Bitcoin and cryptocurrencies have recently rekindled discussions in financial circles, both due to their technologies and price movements. The increasing inclination of investors who seek returns and embrace risk towards cryptocurrency markets is evident, driven by sudden price fluctuations. The potential of cryptocurrencies to serve as alternatives to traditional investment instruments continues to be debated within the financial framework. Researchers are persistently exploring financial instruments associated with the price fluctuations of Bitcoin and cryptocurrencies. This study investigates the interest in Bitcoin in Türkiye within the scope of Bitcoin trading volume and the "Bitcoin" search results on Google Trends. Bitcoin trade volume of BTCTurk and Paribu, two cryptocurrency exchanges operating in Türkiye, and Bitcoin search data on Google were included in the study. In this context, the long-term relationship between Bitcoin trading volume and Google Trends results is examined using the Engle-Granger cointegration test, and the existence of causality is explored through the Toda-Yamamoto causality test. According to the findings of the study, a cointegration relationship among the variables is identified. It is revealed that there is no bidirectional causality between Bitcoin trading volume and Google Trends search results. However, it is established that Google Trends is the cause of Bitcoin trading volume.
Purpose We aim to examine the impact of COVID-19 on the efficiency of Gold and Bitcoin returns. In particular, our efficiency tests are based on the popular calendar anomaly, the turn-of-the-month (TOM) effect in these markets. Design/methodology/approach We define the TOM days as the final trading day of a month and initial three trading days of the immediate next month. To understand the TOM effect, we estimate the typical Ordinary Least Squares (OLS) regression model using the Heteroskedasticity and Autocorrelation Consistent (HAC) standard errors and covariances. Findings Though in the full sample, a positive and significant TOM effect is observed only for Bitcoin, during COVID period, the TOM effect appears in Gold returns and becomes stronger for Bitcoin, implying that the considered securities become inefficient during COVID period. Practical implications Based on these results, we create a trading strategy which is found to surpass the buy-and-hold strategy for both the full sample as well as the COVID period for Bitcoin while only during the COVID period for Gold. Our results provide useful implications for investors and policymakers as the Gold and Bitcoin markets can be timed by taking positions especially based on the behavior of the TOM effect. Originality/value We examine the TOM effect in the two important securities – Gold and Bitcoin. Though, a few studies have examined this anomaly in currency, equity and cryptocurrency markets, however, they have not considered the Gold market. Additionally, no study has examined the impact of COVID-19 on the TOM effect in these markets, and hence, market efficiency. We believe that our study is the first to examine the TOM effect in these markets simultaneously.
Molla Ramizur Rahman, Muhammad Abubakr Naeem, Larisa Yarovaya, Sabyasachi Mohapatra
This study explores the systemic risk within thirty-four diverse cryptocurrencies, analyzing the commonality across different groups. In light of the cryptocurrency market's significant downturn following the FTX collapse in 2022, this research uniquely examines systemic risk commonality. Interestingly, it reveals no distinct risk-reducing traits in sharia-compliant and gold-backed coins, suggesting asset backing does not mitigate inherent cryptocurrency risks. Moreover, a notable common trend in systemic risk among cryptocurrencies is identified, driven by their complementary characteristics. This insight into common systemic risk trends enables investors to make informed hedging decisions across various cryptocurrency groups, providing a safeguard against severe market downturns.
The system proposed in this paper aims to predict cryptocurrency prices using Bi-Directional Long Short- Term Memory (LSTM), leveraging historical data obtained from Yahoo Finance and CoinGecko APIs. The goal is to assess LSTM models effectiveness in forecasting cryptocurrency prices and offer an interactive interface for users to visualize historical and forecasted prices. Several research works have been conducted on the prediction of cryptocurrency prices through various Deep Learning (DL) based algorithms. This project comprises two main approaches : one involves data analysis, LSTM modeling, and change point detection using Yahoo Finance data, while the other focuses on LSTM model training and price prediction using CoinGecko API data. The paper suggests that the prediction models it presents are useful for traders, investors, [6] and finance academics and are close to accurate at predicting the values of cryptocurrencies. Future research will examine more advanced deep learning architectures, primarily Transformer-based models like the GPT series, to improve pattern detection in bitcoin data. Integrating other data sources, such as sentiment analysis or blockchain measurements, may increase the accuracy of forecasting. With further research into cutting-edge techniques, cryptocurrency forecasting will get better and provide stakeholders with more information to help them make informed decisions. Keywords— Cryptocurrency ; forecasting ; Bi-Directional LSTM Model ; Time-series forecasting ; Machine learning
Purpose This paper aims to investigate the effect of the economic policy uncertainty (EPU), geopolitical risk (GPR) and climate policy uncertainty (CPU) of USA on Bitcoin volatility from August 2010 to August 2022. Design/methodology/approach In this paper, the authors have adopted the empirical strategy of Yen and Cheng (2021), who modified volatility model of Wang and Yen (2019), and the authors use an OLS regression with Newey-West error term. Findings The results using OLS regression with Newey–West error term suggest that the cryptocurrency market could have hedge or safe-haven properties against EPU and geopolitical uncertainty. While the authors find that the CPU has a negative impact on the volatility of the bitcoin market. Hence, the authors expect climate and environmental changes, as well as indiscriminate energy consumption, to play a more important role in increasing Bitcoin price volatility, in the future. Originality/value This study has two implications. First, to the best of the authors’ knowledge, the study is the first to extend the discussion on the effect of dimensions of uncertainty on the volatility of Bitcoin. Second, in contrast to previous studies, this study can be considered as the first to examine the role of climate change in predicting the volatility of bitcoin. This paper contributes to the literature on volatility forecasting of cryptocurrency in two ways. First, the authors discuss volatility forecasting of Bitcoin using the effects of three dimensions of uncertainty of USA (EPU, GPR and CPU). Second, based on the empirical results, the authors show that cryptocurrency can be a good hedging tool against EPU and GPR risk. But the cryptocurrency cannot be a hedging tool against CPU risk, especially with the high risks and climatic changes that threaten the environment.
Background: In recent years, investors' interest in cryptocurrencies has increased due to their notable price volatility and rapid price increases. These investors view cryptocurrencies as suitable financial assets for portfolio rebalancing strategies. Purpose: The main objective of this study is to examine the multifractality of the cryptocurrencies Bitcoin (BTC), Lisk (LSK), Quantum (QUA), Litecoin (LTC), Ripple (XRP), Augur (REP), Darkcoin (DASH), EOS, IOTA (MIOTA). Methods: The Detrended Fluctuation Analysis (DFA) econophysics model supports the methodology. Results: The results suggest that during the 2020 pandemic period, the digital currencies LSK, QUA, MIOTA, XRP, REP, BTC, ETH, LTC and DASH showed very significant persistence, indicating that price formation is not random. However, validating that cryptocurrency prices are predictable based on historical time series was impossible. On the other hand, the digital currency EOS proved to be in equilibrium; in other words, price formation follows the random walk pattern, suggesting that prices are not autocorrelated over time. During the 2022 geopolitical conflict, long-term memory patterns shifted significantly towards short-term memories, i.e. anti-persistence. The digital currencies ETH, MIOTA, EOS, LTC, REP, LSK and DASH showed anti-persistence slopes, indicating that prices were less influenced by past events and more by recent events. On the other hand, the cryptocurrencies BTC (0.50), QUA (0.50), and XRP (0.50) demonstrate that prices contain a significant random component and that the residuals are independent and identically distributed (i.i.d.), supporting the idea that white noise might be present. Conclusion: From a risk management perspective, these findings are highly relevant to investors, traders and market participants.
This article primally explores the short-term fluctuation and long-term implications of the international Bitcoin price (BP) on the Chinese green bond (GB) market, within the sample period of 2014:M10–2023:M07. Bitcoin is the most important cryptocurrency and has a carbon-intensive feature, and its price suffers from great volatility and is closely related to the green finance market. Meanwhile, although China is the largest bitcoin mining state, it is pursuing a dual carbon target, which promotes its green bond market’s development. Thus, it is valuable to investigate the influence of BP on GBs in China. Based on the quantile autoregressive distributed lag approach, this paper indicates that the positive and negative impacts of BP on the GB market are significant in the long-term but not apparent in the short-term. These results emphasize the importance for market participants to obtain a better understanding of how BP affects GB under various market circumstances. Implementing specific policies, such as regulatory mechanisms for Bitcoin trade, market-oriented reform for the bond market, and information disclosure, can alleviate shocks from BP and accelerate the development of the GB market.
Abstract This paper investigates the volatility connectedness and dynamic time–frequency relationship between Bitcoin (BTC) and 15 major agricultural commodity markets during the COVID‐19 and 2022 Russia–Ukraine war periods. We employ the TVP‐VAR‐based extended joint connectedness method, minimum connectedness investment portfolio, and wavelet coherence (WC) method. The results indicate that the sudden outbreaks of the two crises brought about increased volatility connectedness between BTC and agricultural commodity markets. Throughout the entire sample period, BTC remained a net transmitter of volatility. Moreover, in terms of the total connectedness index (TCI), the overall volatility correlation surged rapidly after the outbreak of COVID‐19 and the 2022 Russia–Ukraine war. The portfolio results demonstrated that BTC exhibited a low correlation with the agricultural commodity markets, suggesting diversification potential. Additionally, only Feeder Cattle served as an effective hedging asset for BTC throughout all periods. The WC analysis confirmed that during the COVID‐19 period and the 2022 Russia–Ukraine war, most of the linkages were primarily concentrated at medium‐ to long‐term frequencies. Our analysis will contribute to a deeper understanding of the interconnection between these markets, enabling market participants to consider risk mitigation measures and support portfolio diversification when formulating policies and regulations involving relevant markets in the future.
The fintech sector has been booming for the past decade, especially with the unprecedented expansion in cryptocurrency innovation. Many countries and their central banks are working to accommodate cryptocurrency in a regulated format into their financial system anywise. This research paper investigates the long-run and short-run relationship between Bitcoin (INR) and the macroeconomic variables of the Indian economy, such as two major stock indices (NSE and BSE), money supply M1, foreign exchange rate (INR/US dollar), and indicators of inflation rate (CPI and WPI). For this purpose, monthly data of the variables from October 2014 to December 2020 are considered. The Johansen co-integration approach depicts the long-run association between Bitcoin and the economic variables, whilst VECM and the Wald coefficient reveal no short-run causality between the variables. The Granger Causality test shows a one-way causal relationship of NSE, BSE and WPI to Bitcoin. Hence, it concluded that stock indices and inflation have a cogent effect and exert on bitcoin prices. The findings will be helpful for policy-makers and investors alike, for an outlook to strategize and explore this everchanging digital instrument.
Purpose The purpose of the study is to analyze the hedging abilities of the cryptocurrencies vis-à-vis gold against macroeconomic shocks in four emerging economies, India, China, Brazil and Russia. Design/methodology/approach Using the monthly data from January 2013 to April 2023, the paper analyses the response of Cryptocurrencies vis-à-vis gold prices to three different macroeconomic shocks, namely, the economic policy uncertainty shock, the financial uncertainty shock and the inflation shock, within a VAR framework with the help of the Generalized Impulse Response Function. Findings Both gold and cryptocurrencies have limited hedging abilities against macroeconomic shocks across countries. In India, bitcoin has become the new digital gold, while in China, it is not bitcoin but rather gold that retains its hedging abilities. Neither bitcoin nor gold, Binance Coin or Cardano, are found to be the new digital gold in Brazil and Russia. Originality/value The paper compares the top nine cryptocurrencies with the traditional asset gold in terms of their hedging potential against macroeconomic shocks in emerging countries.
Mailinda Tri Wahyuni, Endrizal Ridwan, Dwi Fitrizal Salim
This study aims to determine the impact of macroeconomic variables on bitcoin prices in the United States. Bitcoin is one of the cryptocurrencies that has the highest price and the most users in the United States in recent years. This study uses monthly data on inflation, interest rates, USD/EUR rates, gold prices, and bitcoin prices. To achieve the objectives of this study, Dynamic Conditional Correlation (DCC) and Multivariate Generalized Autoregressive Conditional Heteroscedasticity (MGARCH) were used. The results showed that there is a negative and significant relationship between the variables of inflation, interest rates, and USD/EUR rates affecting the price of Bitcoin in that period. Conversely, there is a positive and significant relationship between the price of gold and the price of Bitcoin in the United States during that period. An in-depth understanding of how macroeconomic factors such as inflation, interest rates and the USD/EUR rates affect Bitcoin price is key to making smart investment decisions in an increasingly complex crypto market. The findings of this analysis confirm that the significant relationship between macroeconomic variables and Bitcoin price provides deeper insights for investors to anticipate market movements and design adaptive investment strategies.
Joy Dip Das, Ruppa K. Thulasiram, Christopher J. Henry, A. Thavaneswaran
This work addresses the intricate task of predicting the prices of diverse financial assets, including stocks, indices, and cryptocurrencies, each exhibiting distinct characteristics and behaviors under varied market conditions. To tackle the challenge effectively, novel encoder–decoder architectures, AE-LSTM and AE-GRU, integrating the encoder–decoder principle with LSTM and GRU, are designed. The experimentation involves multiple activation functions and hyperparameter tuning. With extensive experimentation and enhancements applied to AE-LSTM, the proposed AE-GRU architecture still demonstrates significant superiority in forecasting the annual prices of volatile financial assets from the multiple sectors mentioned above. Thus, the novel AE-GRU architecture emerges as a superior choice for price prediction across diverse sectors and fluctuating volatile market scenarios by extracting important non-linear features of financial data and retaining the long-term context from past observations.
This paper aims to explore the complex interrelationships between the prices of cryptocurrency, specifically Ethereum (ETH), and five top Non-Fungible Token (NFT) collections: Bored Ape Yacht Club, Mutant Ape Yacht Club, Azuki, Moonbirds, and Otherdeed. Motivated by the intertwining dynamics of these digital assets and the unexplored nature of their interdependencies, this study employs a Vector Autoregressive (VAR) model and utilizes Granger Causality to dissect the multifaceted interactions. The analysis period ranges from April 2021 to January 2023, a critical window of exponential growth and fluctuation in the digital asset market. The results demonstrate a statistically significant impact of ETH prices on NFT collection prices, but not vice versa, revealing the strong dependence of the NFT market on cryptocurrency volatility. Specifically, the research finds that changes in ETH’s value are predictive of shifts in NFT prices, whereas NFT price fluctuations lack predictive power for ETH prices. In conclusion, this research represents an advancement in understanding price dynamics in the rapidly evolving digital economy. By innovatively analyzing the co-movement of cryptocurrencies and NFTs, it not only enriches existing knowledge but also paves the way for further exploration, offering practical insights for diverse stakeholders navigating this exciting, ever-changing field.