This research paper introduces a digital asset trading system, detailing its design and operational mechanisms within a block chain framework. It explores the intricacies involved in the design and trading of digital assets, as well as strategies to address the associated challenges. Additionally, the paper examines a recent advancement in digital assets known as Smart Contracts, focusing on their automation processes and the simplification of transaction execution and engagement on digital platforms. Furthermore, it highlights a software solution called Digital Asset Management Software, which automatically verifies the authenticity of transactions, thereby enhancing transparency in the process. The paper comprehensively addresses all significant facets of digital assets and their associated processes.
Background: This paper analyses the influence of fluctuation in gold market on bitcoin prices. Based on previous studies, in present market conditions, volatility in gold prices have caused price changes in several other major assets in the market, such as crude oil. Gold fluctuations are likely to stimulate uncertainty in some other major assets. As bitcoin is becoming an alternative tool to hedge against inflation likewise to gold, the degree of uncertainty in bitcoin market is relatively high. Therefore, the study of causal relationship between gold and bitcoin markets has become appropriate since bitcoin has tremendous growth in its returns and shares many similarities with gold. Thereupon, this study reveals the evidence of Granger causality regression in different time spans to understand the relationship between gold and bitcoin. This relationship is beneficial to study since Granger causality hypothesis acknowledges whether goldâs historical prices are useful for forecasting the bitcoin market. Purpose: This study aims to analyze the relationship between gold and bitcoin market during an 8-year period from 2014 and 2022. Throughout this period, time spans which involves financial crises have been separated from the data set and tested separately to determine if there is a constant relationship between the variables. Through this, it has been intended to find the Granger causality link between gold and bitcoin market to see whether one is leading another one. Identifying the Granger causality correlation helps analyzing the patterns of correlation by using the empirical datasets, and to determine the strength of the Granger causal relationshipâs nature between gold and bitcoin. Since the correlation itself does not explain why or how, but only if both markets move together, the Granger causality correlation between gold and bitcoin is the quantification of the impact that gold market performance has on bitcoinâs future price performance. Method: Since the collected data is time-series data, Augmented Dickey-Fuller tests have been conducted initially to the chosen tests. Following the results from ADF tests, Spearmanâs Rho, iand Johansenâs Cointegration tests have been utilized to determine the long-term correlation between variables. Thereafter, Toda & Yamamoto and Dolado & LuÌtkepohl Granger Causality (TYDL-GC) method has been used to analyze the Granger causality link between the variables. Conclusion: The results of this study indicates that (i) no statistically significant correlation between gold and bitcoin market has been found according to the Spearmanâs Rho test results, (ii) no long-term relationship has been found between gold and bitcoin according to cointegration test, (iii) gold does Granger Cause bitcoin prices. The evidence of causality link is unilateral from gold towards bitcoin market. Furthermore, it was observed that the Granger causality link weakens in short term and is not constant over time. The results fail to support the semi strong Efficient Market Hypothesis form. Thus, gold and bitcoinâs markets are efficient in the weak form but inefficient in the semi strong form. Since Granger causality has been found from gold towards bitcoin, one can construct a prediction model for bitcoin by using goldâs historical prices.
Bitcoin, a leading cryptocurrency in the financial market, is full of non-linearity, non-stationarity and high volatility. To make risk management strategies, emphasis on cryptocurrency price predicting is truly needy. However, studies about cryptocurrency prediction are lacking. In this paper, a novel hybrid model combining long short-term memory (LSTM), a state-of-the-art sequence learning method, with singular spectrum analysis (SSA) was proposed to predict Bitcoin price. SSA was employed to decompose the original time series into independent signals in term of trend, market fluctuation and noise. A smoothed series with valid information was reconstructed with reduction of noise. By introducing the smoothed series sequence into LSTM, prediction value is obtained. Empirical analysis shows that the proposed hybrid SSA-LSTM model outperforms baseline single LSTM model, according to root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The result suggests that the proposed hybrid model has satisfactory ability to grasp pattern of Bitcoin price series since SSA can extract valid information from the original series and avoid overfitting.