Now, let us suppose we have access to a real-world database of an interbank market (randomized data for illustrative purpose), where banks lend to each other and lending banks report on their positions at the end of each day in the period of 2007-2010. The database consists of 50 banks and the maturity of the loans is one day. In order to manipulate the real-world networks in R, it is advisable to convert our data into a CSV file and save it into our working directory. The following table (Table 2) shows the top lines of our CSV file:
|
Bank |
Partner |
Amount |
Interest |
Year |
Month |
Day |
|---|---|---|---|---|---|---|
|
1 |
21 |
5 |
7,9 |
2007 |
1 |
3 |
|
1 |
42 |
3 |
7,9 |
2007 |
1 |
3 |
|
10 |
11 |
0,35 |
7,8 |
2007 |
1 |
3 |
|
18 |
24 |
2 |
8 |
2007 |
1 |
3 |
|
2 |
11 |
1,3 |
7,8 |
2007 |
1 |
3 |
|
21 |
11 |
0,8 |
7,8 |
2007 |
1 |
3 |
|
21 |
2 |
5 |
7,75 |
2007 |
1 |
3 |
|
3 |
24 |
4 |
7,95 |
2007 |
1 |
3 |
Table 2: Database of an interbank market
Source: The authors
Each row contains a transaction: the reporting bank (the lender...