This paper aims to study the investment strategy of gold and bitcoin markets and look for a better solution in the investment market to obtain more profits. We first analyze and forecast the gold and bitcoin markets by establishing several models, and find out the model that can best fit gold and bitcoin. Then, based on the optimal models, the most appropriate investment algorithm is proposed to help investors make decisions on every day's investment in order to reap the greatest rewards.
How to predict the change trend of asset prices in the future and decide different operation modes in advance to obtain the maximum benefits is the concern of investors. Taking gold and bitcoin as examples, this paper develops an appropriate mathematical model that uses only the past daily price stream to help traders determine whether to buy, hold or sell assets in their portfolio every day. At the same time, the robustness of the model is analyzed by robustness. The study found that holding US $1000 on September 11, 2016 will eventually maximize profits on September 10, 2021.
Maximizing returns has always been people's investment goal. Gold and bitcoin are popular with investors because of their hedges and volatility. However, markets are risky and can be influenced by different economic, political and environmental factors. As a result, bitcoin and gold prices fluctuate wildly, leading to uncertain investment and uncertain returns. In order to maximize the profit, this paper completes the data processing and model construction to make decisions. Based on the Markov decision process of avoiding risk avoidance, reducing transaction cost and maintaining liquidity, and assuming that the stock market is not affected by enhanced trading agent, deep reinforcement learning (DRL) is used to simulate stock trading. The application of the model is helpful to forecast the return of investment portfolio and brings strong application value to the relevant practitioners.
On February 24, 2022, Russia’s invasion of Ukraine marked a full-scale escalation of the Russian-Ukrainian conflict into war. The global economy and finance were affected by the Russian-Ukrainian conflict, which most obvious is that crude oil prices continued to rise rapidly. With the development of the times, cryptocurrencies are becoming more and more important and cannot be ignored. Cryptocurrency may serve as an effective alternative or balancing asset to cash, which may depreciate over time due to inflation. In addition to the real commodity market, the Russian-Ukrainian conflict would certainly have a certain impact on the cryptocurrency market. Bitcoin is the largest cryptocurrency and can represent the changes in the entire cryptocurrency market to a certain extent. This paper examines the dynamic impact of the Russian-Ukrainian conflict on Bitcoin returns and volatility. There are two main results in this paper: the increase in the futures crude oil price has a significant dynamic correlation with the Bitcoin yield, but this relationship is short-term and will disappear over time; the increase in futures crude oil prices will not lead to greater fluctuations in Bitcoin yields. This result can be generalized to the entire cryptocurrency market, which means the Russian-Ukrainian conflict would have a short-term impact on the entire cryptocurrency, but this effect won’t continue for the long-term. Also, this research shows that the cryptocurrency market is independent to some extent.
Alessandro Cremaschini, Antonio Punzo, Eliano Martellucci, Antonello Maruotti
This study provides an empirical analysis on the main univariate and multivariate stylized facts iin return series of the two of the largest cryptocurrencies, namely Ethereum and Bitcoin. A Markov-Switching Vector AutoRegression model is considered to further explore the dynamic relationships between cryptocurrencies and other financial assets. We estimate the presence of volatility clustering, a rapid decay of the autocorrelation function, an excess of kurtosis and multivariate little cross-correlation across the series, except for contemporaneous returns. The analysis covers the pandemic period and sheds lights on the behaviour of cryptocurrencies under unexpected extreme events.
An Pham Ngoc Nguyen, Tai Tan, Marija Bezbradica, Martin Crane
We analyze the correlation between different assets in the cryptocurrency market throughout different phases, specifically bearish and bullish periods. Taking advantage of a fine-grained dataset comprising 34 historical cryptocurrency price time series collected tick-by-tick on the HitBTC exchange, we observe the changes in interactions among these cryptocurrencies from two aspects: time and level of granularity. Moreover, the investment decisions of investors during turbulent times caused by the COVID-19 pandemic are assessed by looking at the cryptocurrency community structure using various community detection algorithms. We found that finer-grain time series describes clearer the correlations between cryptocurrencies. Notably, a noise and trend removal scheme is applied to the original correlations thanks to the theory of random matrices and the concept of Market Component, which has never been considered in existing studies in quantitative finance. To this end, we recognized that investment decisions of cryptocurrency traders vary between bearish and bullish markets. The results of our work can help scholars, especially investors, better understand the operation of the cryptocurrency market, thereby building up an appropriate investment strategy suitable to the prevailing certain economic situation.
Rukhsana Rasheed, Mazhir Nadeem, Mahnaz Muhammad Ali
This study is conducted to investigate the potential volatilities and interdependencies among cryptocurrencies during global health crises, i.e., during COVID-19. The top five cryptocurrencies have been selected to assess their interdependencies. These currencies have been ranked as the top five due to their highest market capitalization. These top five currencies are Bitcoin, Ethereum, Tether, USD coin, and BNB. Monthly data for these currencies from the first month of 2019 to eighth month of 2022 is taken from online sources by investing.com. Squared deviations from mean values have been taken as measures of volatilities. These measures are simple but more precise to capture the possibility of potential interrelations in the volatilities of cryptocurrencies through regression analysis. The results of this study showed that the volatility of each cryptocurrencies involved is interlinked with the volatility of at least one other cryptocurrencies.
Energy production is a phenomenon that has always preserved its importance for the history of humanity, as well as where the energy is spent and its consumption are also important. This study examined the causality relationship between Bitcoin energy consumption and Apple, Dell Technologies, Lenova Group, HP, Quanta Computer, Compal Electronics, Canon, Wistron and Hewlett Packard Enterprise has been taken into account to represent technology companies’ stock market. In the analysis, daily price data for the period 12.02.2017-07.02.2021 were used. Toda-Yamamoto (1995) symmetric causality test and Hatemi-J (2012) asymmetric causality test were used for used to determine the relationship between Bitcoin energy consumption and technology companies’ stock values. According to the results of the Toda-Yamamoto (1995) causality test, it has been found that there is a causality from Bitcoin energy consumption to Apple's stock value; according to the Hatemi-J (2012) asymmetric causality test results, it has been determined that there is a causality from Bitcoin energy consumption positive shocks to Apple, Dell Technologies, Lenova Group, HP, Quanta Computer, Compal Electronics, Canon, Wistron and Hewlett Packard Enterprise stock values negative shocks and from Bitcoin energy expenditure negative shocks to Hewlett Packard Enterprise negative shocks. According to the results of the study in general, it is seen that the change in Bitcoin energy consumption has an effect on the firm returns of the companies that sell the necessary tools for bitcoin energy production. From this, it can be commented that bitcoin mining is also effective on the stock returns of technology companies as well as many financial factors.
The aim of the article: The purpose of this study is to provide a thorough review of the current state of cryptocurrency market and how governments perceive and deal with the threats and opportunities brought by the block chain technology. Cryptocurrencies were certainly the most popular investment in the last decade with a skyrocketing trading volume. However, cryptocurrency abilities in money laundering, financing terrorism and tax evasion overshadow the great opportunities and potential of this new technology. Therefore, the major economies in the world have been working on an efficient and effective strategy to control and tax the cryptocurrency market. In this study, the current state in Turkey regarding cryptocurrency taxation is analysed and a tax system is proposed. The authors claim that the Tobin tax, or in other words, low tax rates would be the best tax system to be applied in Turkey.
 Methodology: The study is based on a detailed literature review on the subject, academic papers, news releases and legal acts of the USA, Europe and Turkey. Different attitudes of varied groups are discussed and proposed solutions in the subject are being considered.
 Results of the research: Cryptocurrency market has a great potential and block-chain technology is full of opportunities. However, it is essential to control this market without harming the appeal of cryptocurrencies, yet this is not an easy task. Therefore, we argue that Turkey should extend the usage of cryptocurrencies, create a tax strategy with low tax rates and we claim that a regulation similar to the Tobin tax application would be effective here.
Iqra Sadia, Atif Mahmood, Miss Laiha Mat Kiah, Saaidal Razalli Azzuhri
The rapid growth of cryptocurrencies has gained much attention by media, investors and scholars, since it is widely used for investment purposes as an alternative to regular currencies. Therefore the intelligent management and under-standing the characteristics of cryptocurrencies are becoming more interesting. The price of cryptocurrencies are characterized by linear and nonlinear trend, seasonality and high volatility, which increases the risk factors for investors. This study ex-periments with three different time series forecasting methods, specifically considered for Cryptocurrencies price such as Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), Monero (XMR) and Cardano (XRP), and devises a procedure to evaluate their performance. Time series data are collected and examined using descriptive statistics. In next step, the White Neural Network is used for Non-Linearity and Dickey-Fuller for nonstationary and correlation among different settings of datasets. Based on these analyses, we evaluate efficient financial forecasting models such as Autoregressive Integrated Moving Average (ARIMA), Trigonometric, Box-Cox transformation, ARMA errors, Trend and Seasonal (TBATS) and Neural Network Autoregressive (NNAR) with reference to different parameters configuration of these models. The performance is evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) criterion and models are ranked by statistical mean and standard deviation of MAPE values. The NNAR model gives minimum MAPE of 2.823 while the minimum convergence time of 4.9835s is observed with TBATS and hence, these are ranked at top amongst other models respectively. These results underpin that neural network-based models perform equally well on both types of nonlinear and linear financial data and, thus, have the potential to improve the impact of financial transaction and cryptocurrencies price bringing more innovation in the decision making process.
Md. Bokhtiar Hasan, Md. Naiem Hossain, Juha-Pekka Junttila, Gazi Salah Uddin · 5 authors
This study analyses the impact of different uncertainties on commodity markets to assess commodity markets' hedging or safe-haven properties. Using time-varying dynamic conditional correlation and wavelet-based Quantile-on-Quantile regression models, our findings show that, both before and during the COVID-19 crisis, soybeans and clean energy stocks offer strong safe-haven opportunities against cryptocurrency price uncertainty and geopolitical risks (GPR). Soybean markets weakly hedge cryptocurrency policy uncertainty, US economic policy uncertainty, and crude oil volatility. In addition, GSCI commodity and crude oil also offer a weak safe-haven property against cryptocurrency uncertainties and GPR. Consistent with earlier studies, our findings indicate that safe-haven traits can alter across frequencies and quantiles. Our findings have significant implications for investors and regulators in hedging and making proper decisions, respectively, under diverse uncertain circumstances.
The purpose of our paper is to analyze the main factors which influence fiscal balance’s evolution and thereby identify solutions for configuring a sustainable fiscal policy. We have selected as independent variables some of the main macroeconomic measures, respectively public debt, unemployment rate, economy openness degree, population, consumer goods’ price index, current account balance, direct foreign investments and economic growth rate. Our research method uses two econometric models applied on a sample of 22 countries, respectively 14 developed and 8 emergent. The first model is a multiple regression and studies the connection between the fiscal balance and selected independent variables, whereas the second one uses first order differences and introduces economic freedom as a dummy variable to catch the dynamic influences of selected measures upon fiscal result. The time interval considered was 1999-2013. The results generated using the two models revealed that public debt, current account balance and economic growth significantly influence the fiscal balance. As a consequence, the governments need to plan and implement a fiscal policy which resonates with economy priorities and the phase of the economic cycle, as well as ensure a proper management of the public debt, stimulate sustainable economic growth and employment.
Shafiqah Azman, Dharini Pathmanathan, A. Thavaneswaran
During the COVID-19 pandemic, cryptocurrency prices showed abnormal volatility that attracted the participation of many investors. Studying the behaviour of volatility for the prices of cryptocurrency is an interesting problem to be investigated. This research implements the state space model framework for volatility incorporating the Kalman filter. This method directly forecasts the conditional volatility of five cryptocurrency prices (Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), Litecoin (LTC) and Bitcoin Cash (BCH)) for 10,000 consecutive hours, i.e., approximately 417 days during the COVID-19 pandemic from 26 February 2020, 00:00 h until 18 April 2021, 00:00 h. The performance of this model is compared to the GARCH (1,1) model and the neural network autoregressive (NNAR) based on root mean square error (RMSE), mean absolute error (MAE) and the volatility plot. The autocorrelation function plot, histogram and the residuals plot are used to examine the model adequacy. Among the three models, the state space model gives the best fit. The state space model gives the narrowest confidence interval of volatility and value-at-risk forecasts among the three models.
Bu çalışmada, piyasa istikrarı ve yatırımcı ufkunu açıklayan, finansal zaman serilerinin normal dağılmadığını ve finansal zaman serilerinde kendine benzerlik özelliği olduğunu ifade eden fraktal piyasa hipotezinin iki gelişmekte olan, iki gelişmiş piyasada ve iki kripto varlıkta geçerliliğinin Hurst Üsteli- Yeniden ölçeklendirilmiş aralık (R/S) Analizi yöntemi aracılığıyla araştırılması amaçlanmıştır. MSCI sınıflamasına göre gelişmiş piyasalar olarak SP500 ve FTSE, gelişmekte olan piyasalar olarak Borsa İstanbul 100 ve Shanghai Endeksi incelemeye dahil edilmiştir. Kripto varlıklarda ise işlem hacmi en yüksek olan Bitcoin ve Ethereum değişkenleri incelemeye dahil edilmiştir. Çalışma bulgularına göre incelenen tüm endekslerde fraktal piyasa hipotezinin varlığı kabul edilirken, uzun hafızanın rolü ise değişmektedir. Tüm değişkenlerde Hurst üsteli değeri 0.5 değerinden yüksektir. Hurst üsteli sonuçlarına göre tüm değişkenlerde zaman serisinin kalıcı davranış gösterdiğine ilişkin hipotez kabul edilmiştir. Uzun hafızanın kalıcılığın en düşük olduğu değişken FTSE’dir. Gelişmekte olan borsalarda uzun hafıza ve kalıcılık gelişmiş borsalara göre daha yüksekken tüm değişkenler içerisinde uzun hafızanın en güçlü olduğu ve kalıcılığın en yüksek olduğu değişken ise Bitcoin’dir.