This project aims to explore and analyze the relationship between Bitcoin (BTC) and gold prices using advanced time series tools in R. By leveraging techniques such as Engle-Granger causality tests and cointegration tests, we seek to uncover potential long-term relationships and dependencies between these two assets.
- Perform data collection and preprocessing: Obtain historical price data for BTC and gold from reliable sources, clean the data, and ensure consistency.
- Conduct exploratory data analysis: Visualize the price trends and statistical characteristics of BTC and gold to gain insights into their behavior.
- Perform Engle-Granger causality test: Determine if there is a causal relationship between BTC and gold prices, exploring whether one asset's price movements predict or influence the other.
- Conduct cointegration analysis: Investigate the presence of a long-term relationship between BTC and gold, examining whether their prices move together over time.
- Implement other relevant time series analyses: Utilize additional advanced techniques such as ARIMA modeling, Granger causality tests, or Vector Autoregression (VAR) models, as appropriate.
- Interpret and present findings: Analyze the results obtained from the analyses and provide meaningful insights into the relationship between BTC and gold prices.
- Install the necessary packages specified in the project's dependencies section.
- Execute the R scripts to fetch historical BTC and gold price data and preprocess the data.
- Run the analysis scripts, which include Engle-Granger causality tests, cointegration tests, and other relevant time series analyses.
- Interpret the results and visualize the findings using appropriate plots or graphs.
- Modify and customize the analysis as desired, experimenting with different models or techniques.
- Document your observations, insights, and conclusions based on the analysis.
- Feel free to contribute improvements, bug fixes, or additional analysis methods to enhance the project.
- R version 3.x or higher
- Required R packages:
tidyverse,stats,ggplot2,urca,vars, and any additional packages specified in the project's scripts.
This project is licensed under the MIT License.