The Impact of Political Speech on Cryptocurrency Prices: An Analysis of Bitcoin Price Non-Synchronization In Turbulent Time of Socioeconomic Challengers

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.

Bitcoin is a relatively new and interesting asset. It is used for peer-to-peer transactions and is built upon an exciting system called the Blockchain which allows for fast and secure transactions between users. Although Bitcoin and its underlying infrastructure show a lot of potential for growth and innovation, many users of the so-called "cryptocurrency" are wary of holding it in lieu of other currencies such as the U.S. dollar because of the high volatility exhibited in the price of Bitcoin. The goal of this paper is to examine the use of theoretically priced put options, "protective puts", in order to hedge against price decreases that Bitcoin may experience. The user of this protective put strategy is considered to be an investor with an optimistic view on the price of Bitcoin and wants to own some, but is uncomfortable with the potential for large losses due to price decreases. In implementing the protective put strategy, the price of Bitcoin that the investor owns has a floor at the strike price of the options purchased to hedge the risk of price decreases. If the price increases enough, then the options are sold and those with the new strike price are purchased to lock in a higher protected price for the investor. The investor's goals are to reduce the risk of losses by owning Bitcoin while its price decreases and to decrease the volatility that his portfolio experiences at the expense of the cost of purchased options eating into potential profits. Upon analysis of both historical and simulated data, utilizing protective puts as a hedging mechanism against decreases in the price of Bitcoin has proven effective at reducing expected volatility and

This paper explores the interactions between the Bitcoin (BTC) prices in the US and Chinese markets, by employing the bootstrap rolling window causality test. The results reveal that BTC prices behave differently across markets, and also vary with time, which subjects to the theory of price discovery. In other words, the BTC price in one market could precede the other, and vice versa, based on the information advantage. Markets that are more flexible (US) respond sensitively to information, thus, in order to induce the price changes in Chinese markets. The improvements in the economic conditions of the emerging markets have exerted an influential role in global markets. Since the Chinese market possesses a considerable amount of trading volumes, the BTC price in the US can be assumed to chase the BTC price in China. The lead–lag relationship between these two markets also reflects the acknowledgement of the aversion towards the risks involved in accepting BTC as a currency. However, knowing which market reacts the most quickly to new information could prove to be beneficial to regulators who aim to implement a particular BTC price, and, as a result, prevent any arbitrary prices, and eventually stabilize the financial market.

Does global economic uncertainty matter for the volatility and hedging effectiveness of Bitcoin?

Shock transmission in the cryptocurrency market. Is Bitcoin the most influential?

The aim of this study is to gauge the impact of global economic policy uncertainty and natural resource prices, that is, oil prices and gold prices, on Bitcoin returns by using monthly data spanning from May 2013 to December 2021. The study applies ARDL and nonlinear ARDL for evaluating the symmetric and asymmetric effects of Global Economic Uncertainty (GU), oil price (O), and natural gas price on Bitcoin volatility investigated by using the ARCH-GARCH-ERAGCH and non-granger causality test. ARDL model estimation establishes a long-run cointegration between GU, O, G, and Bitcoin. Moreover, GU and oil price exhibits a negative association with Bitcoin and positive influences running from gold price shock to Bitcoin in the long run. NARDL results ascertain the long-run asymmetric relations between GU, oil price, gold price (G), and Bitcoin return. Furthermore, GU’s asymmetric effect and positive shock in gold price negatively linked to Bitcoin return in the long run, whereas asymmetric shock in oil price and negative shocks in gold price established a positive linkage with Bitcoin. The results of ARCH effects disclose the volatility persistence in the variables. The causality test reveals that the feedback hypothesis explains the causal effects between GU and Bitcoin and unidirectional causality running from Bitcoin to gold price and oil price to Bitcoin.

Natural resources commodity prices volatility and economic uncertainty: Evaluating the role of oil and gas rents in COVID-19

Decentralized Finance (DeFi) is a blockchain-based financial system that utilizes smart contracts to increase efficiency and transparency, while overcoming the limitations of conventional financial systems. In Indonesia, there is still little research on blockchain, especially on the DeFi, so investors have very little information. The lack of previous research on the DeFi in Indonesia creates a knowledge gap, given that the local DeFi market has unique characteristics influenced by investor preferences, evolving regulations, and specific levels of technology adoption. Cryptocurrency prices, including the DeFi, are influenced by public information that reflects market efficiency. For example, on October 10, 2020, the price of Yearn Finance (YFI) increased 36% following the rise in Bitcoin prices, demonstrating the link between information, transaction volume, and fluctuations in the DeFi market value. This research aims to identify the factors that influence the DeFi price changes, focusing on the influence of price liquidity and market efficiency. Using a quantitative approach, 65 DeFi coins were selected through purposive sampling, and analysis was carried out using multiple regression using EViews 13. The research results show that partially, price liquidity and market efficiency do not have a significant effect on the DeFi price changes. However, simultaneously, these two variables have a significant effect, with a contribution to price changes of 54.521%. The insignificance of the influence of market efficiency on the DeFi prices suggests that regulations focused on improving efficiency may not be enough to control price volatility or promote price stability in the DeFi ecosystem.

In recent years, Bitcoin is rising and become an attractive investment for traders. Unlike stocks or foreign exchange, Bitcoin price is fluctuated, mainly because of its 24-hours a day trading time without close time. To minimize the risk involved and maximize capital gain, traders and investors need a way to predict the Bitcoin price trend accurately. However, many previous works on cryptocurrency price prediction forecast short-term Bitcoin price, have low accuracy and have not been cross-validatedThis paper describes the baseline neural network models to predict the short-term and the long-term Bitcoin price change. Our baseline models are the Multilayer Perceptron (MLP) and the Recurrent Neural Networks (RNN) models. Data used are Bitcoin's blockchain from August 2010 until October 2017 with 2-days period and the total amount of 1300 data. The models generated are predicting both for short-term and long-term price change, from 2-days until 60-days.The result shows that long-term prediction has a better result than short-term prediction, with the best accuracy in Multilayer Perceptron when predicting the next 60-days price change and Recurrent Neural Networks when predicting the next 56-days price change. Multilayer Perceptron outperforms Recurrent Neural Networks with accuracy of 81.3 percent, precision 81 percent, and recall 94.7 percent.

Since the rise of the first cryptocurrency in 2008 until 2022, we can observe significant price volatility of cryptocurrency coins, periodic decline in prices for cryptocurrency coins and their growth, changes in the volume of cryptocurrency coin trading. Investors perceive cryptocurrency as an alternative (hedging) investment, a means of circulation and a measure of value. They increasingly use it in global socio-economic and military-political crises. Studying the price dynamics of cryptocurrencies during the active phase of the Covid-19 pandemic in 2020-2021 (the previous pandemic was a hundred years ago – the so-called "Spanish flu") and the conventional Russian-Ukrainian war of 2022 (and a similar scale of conflict after World War II 77 years), we will try to conduct an economic analysis to examine how these crisis factors affected the dynamics of prices for major cryptocurrencies – Ether and Bitcoin. Highlighting previously unresolved parts of the overall problem. The influence of various global factors on the price of cryptocurrencies has been insufficiently studied due to the short historical period of cryptocurrencies existence. We will try to identify the dependence of cryptocurrencies' values dynamics on the presence of global positive or negative factors (pandemics, wars, etc.). The purpose and objectives of the study. The main objectives of the article are to prove that the presence of global positive or negative factors (pandemics, wars, etc.) has a significant impact on the structure of investment, in general, and the price dynamics of cryptocurrency units in particular. Methods. In the research process, in particular, the following methods were used: analysis – to reveal the object and subject of research; abstract-logical (theoretical generalizations and formulations of conclusions concerning the impact of the Covid-19 pandemic in 2020-2021 and the conventional Russian-Ukrainian war in 2022 on the value of the cryptocurrency Bitcoin). Results. The significant impact of the Covid-19 pandemic in 2020-2021 and the conventional Russian-Ukrainian war in 2022 on various components of the financial market has been revealed. It was found that extreme socio-economic events provoke a fall in the stock market, do not provoke significant changes in the gold market and stimulate the major cryptocurrencies value growth. Bitcoin and Ether price dynamics have been shown to be highly correlated and are more suitable as short-term safe investment havens for pandemic or conventional war events than gold for economic operators who want to save and increase their savings. Conclusions. Due to the significant increase in the price of Bitcoin and Ether cryptocurrencies during the peak periods of the Covid-19 pandemic in 2020-2021 and the conventional Russian-Ukrainian war in 2022, cryptocurrencies are an effective and appropriate tool for investing and saving financial resources in the short term.

Bitcoin, the most popular cryptocurrency around the world, has had frequent and dramatic price changes in recent years. The price of bitcoin reached a new peak, nearly $65,000 in July 2021. Then, in the second half of 2022, the bitcoin price begins to decrease gradually and drops below $20,000. Such huge changes in the bitcoin price attract millions of people to invest and earn profits. This research focuses on the predictions of bitcoin price changes and provides a reference for trading bitcoin for investors. In this research, we consider a method in which we first apply several traditional machine learning regression models to predict the Changes of Moving Average in the bitcoin price, and then based on the predicted results, we set labels for bitcoin price changes to get the classification results. This research shows that the method of transforming regression results to the classification analysis can achieve higher accuracy than the corresponding machine learning classification models and the best accuracy is 0.81. Besides, according to this method, this research constructs a Machine Learning Trading Strategy to compare with the traditional Double Moving Average Strategy. In a simulation experiment, the Machine Learning Trading Strategy also has a better performance and earns a 68.73% annualized return.

Are cryptocurrencies indeed currencies? Anecdotal evidence on the volatility of cryptocurrency prices suggest that these “currencies” are not a good store of value, and similarly the time delays in validating and publishing crypto-based transactions suggest that they are not a good medium of exchange either. Due to the context it is defined in, it seems to not follow social conventions of fiat currencies. In this thesis, we undertake a systematic evaluation of how much do cryptocurrency prices behave like fiat currency prices, focusing on the predominant cryptocurrency — Bitcoin. We test the usefulness of various time series and structural models to predict future changes in Bitcoin prices and conclude that when predicting out of sample, its price is as unpredictable as fiat currency prices. Since cryptocurrencies generally have no central authority and hence receive no regulation, we explore its country-dependent characteristics, and find that the overall conclusions apply. We also examine if nominal interest rate differentials denominated in fiat currencies versus Bitcoin predict exchange rate movements, and find that in addition to the persistent violation in short-run, interest parity suggest that Bitcoin price has not been rising fast enough. We conclude that we have to refine the definition of monetary parameters on cryptocurrencies to better capture its properties, but as far as our examination indicates, the price of the predominant cryptocurrencies behaves similarly to most fiat currencies. In our point of view, Bitcoin is a currency.

Purpose This paper aims to deepen the understanding of the relationship between global economic uncertainty and price volatility, specifically focusing on commodity, industrial materials and energy price indices as proxies for global inflation, analyzing data from 1997 to 2020. Design/methodology/approach The dynamic conditional correlation generalized autoregressive conditional heteroscedasticity model is used to study the dynamic relationship between variables over a while. Findings The results demonstrated a positive relationship between commodity prices and the global economic policy uncertainty (GEPU). Except for 1999–2000 and 2006–2008, the results of the energy price index model were very similar to those of the commodity price index. A predominant positive relationship is observed focusing on the connection between GEPU and the industrial material price index. The results of the pairwise Granger causality reveal a unidirectional relationship between the GEPU – the Global Commodity Price Index – and the GEPU – the Global Industrial Material Price Index. However, there is bidirectional causality between the GEPU – the Global Energy Price Index. In sum, changes in price indices can be driven by GEPU as a political factor indicating unfavorable economic conditions. Originality/value This paper provides a deeper understanding of the role of global uncertainty in the global inflation process. It fills the gap in the literature by empirically investigating the dynamic movements of global uncertainty and the three most important groups of prices.

This study compares the effectiveness of the ARIMA and GRU models in predicting Bitcoin price movements, addressing the need for reliable predictive tools amidst the high volatility of the cryptocurrency market. Previous research has highlighted the strengths of each model in financial forecasting: ARIMA for short-term, stationary data and GRU for capturing complex temporal patterns. The purpose of this study is to evaluate which model performs better in the context of Bitcoin price prediction, offering insights for investors to minimize risks and enhance decision-making in this unpredictable market. The research methodology involves applying both models to Bitcoin price data and comparing their accuracy using the Mean Absolute Percentage Error (MAPE) across various forecasting intervals. Results indicate that GRU achieves higher accuracy in long-term forecasts, while ARIMA performs optimally for shorter time frames. However, both models demonstrate limitations, especially as the prediction horizon extends, underscoring the inherent challenges of cryptocurrency price forecasting. These findings suggest that GRU may be better suited for longer investment horizons, while ARIMA remains effective for short-term predictions. The conclusions affirm the potential of using these models selectively to align with specific investment strategies in cryptocurrency markets, although further research is recommended to improve predictive accuracy under evolving market conditions.

In this study, spectral Granger causality analysis is employed to investigate the spectral dynamics of uncertainty transmission and its impact on economic growth and financial development in Saudi Arabia from 1993 to 2020. We examine the relationships between crude oil volatility, geopolitical risk, global economic uncertainty, financial development (measured by market capitalisation, MCGDP, Financial Institutions Access Index, FIAIX) and economic growth. The empirical results show that negative shocks in crude oil volatility have a significant impact on financial development as measured by the MCGDP, with capital flight and reduced domestic investment playing key roles, despite the effective contribution of increased government spending to mitigate these effects. In this study, spectral Granger causality analysis is employed to investigate the spectral dynamics of uncertainty transmission and its impact on economic growth and financial development in Saudi Arabia from 1993 to 2020. We examine the relationships between crude oil volatility, geopolitical risk, global economic uncertainty, financial development (measured by market capitalisation, MCGDP, Financial Institutions Access Index, FIAIX) and economic growth. The empirical results show that negative shocks in crude oil volatility have a significant impact on financial development as measured by MCGDP, with capital flight and lower domestic investment playing a key role, despite the effective contribution of increased government spending in mitigating these effects. However, looking at the FIAIX, crude oil volatility has a different impact, with positive shocks having no significant impact on financial development. In contrast, negative shocks show long-term causal effects, which emphasises the vulnerability of the banking sector to oil price fluctuations. Geopolitical risk has a significant long-term impact on the MCGDP due to uncertainty shocks caused by regional and global geopolitical measures. For geopolitical risk, the results are mixed with a significant causality of negative shocks on the FIAIX over certain frequencies, emphasising the sensitivity of the banking sector to geopolitical tensions. In contrast, the world economic uncertainty has only a limited direct impact on the FIAIX, indicating the resilience of the Saudi financial sector to fluctuations in global uncertainty. In terms of economic growth, positive shocks have a greater impact than negative shocks due to the volatility of crude oil. However, shocks resulting from geopolitical risks, whether positive or negative, have little effect on economic growth. The industrial production index, a measure of the resilience of the Saudi economy, indicates that it is susceptible to positive shocks from fluctuations in oil prices over a range of time periods. Our study’s findings can assist Saudi Arabia’s authorities in fortifying the country’s financial and economic resilience against the spread of uncertainty. For strong financial development and sustainable economic growth, these uncertainty elements must be well monitored.