├── var-granger-causality.py
└── README.md


/var-granger-causality.py:
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 1 | import pandas as pd
 2 | import numpy as np
 3 | import quandl
 4 | import matplotlib.pyplot as plt
 5 | import warnings
 6 | 
 7 | warnings.filterwarnings('ignore')
 8 | 
 9 | quandl.ApiConfig.api_key = 'tJKSQn1pHLbyo1mu1wYn'
10 | 
11 | gold = quandl.get('LBMA/GOLD')
12 | silver = quandl.get('LBMA/SILVER')
13 | oil = quandl.get('CHRIS/ICE_G6')
14 | dollar = quandl.get('CHRIS/ICE_DX1')
15 | dow_jones = quandl.get('BCB/UDJIAD1')
16 | treasury = quandl.get('FRED/DGS10')
17 | 
18 | 


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/README.md:
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 1 | # Forecasting-VAR-Granger-Causality
 2 | 
 3 | This project focuses on the Data Science, Statistics, as well as some financial theory revolving around how financial markets behalf. 
 4 | 
 5 | ![](https://kidquant.com/post/images/financial_markets.png)
 6 | 
 7 | ## Goal
 8 | 
 9 | Our goals involve the following:
10 | 
11 | - **Part 1**: Analyze the descriptive statistics and the trends of all of our variables, which involves testing for skewness, asymmetry, and kurtosis.
12 | - **Part 2**: Testing for autocorrelation which involves a process of using an autoregressive model.
13 | - **Part 3**: Testing for stationarity using the Augmented Dickey-Fuller test.
14 | - **Part 4**: Testing for causality with each individual variable using Granger Causality.
15 | - **Part 5**: Creating the model based on the relationship with the variables.
16 | 
17 | This analysis will allow us to assess the given relationship between specific financial markets and effectively forecast them.
18 | 
19 | ## Data
20 | 
21 | We used data from [Quandl](https://www.quandl.com/), which allows us to extract financial and economic data for analysis in Python. We also used some data from the [Federal Reserve Economic Database](https://fred.stlouisfed.org/) or FRED.
22 | 
23 | ## Enviroment and Tools
24 | 
25 | 1. Jupyter NoteBook
26 | 2. Numpy
27 | 3. Pandas
28 | 4. Matplotlib
29 | 5. Scipy
30 | 6. Statsmodels
31 | 7. Quandl
32 | 


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