The code provides estimation and inference for dynamic networks measures introduced in the following papers
Baruník, J. and Ellington, M. (2020): Dynamic Networks in Large Financial and Economic Systems, manuscript available here for download (July 2020)
Baruník, J. and Ellington, M. (2020): Dynamic Network Risk, manuscript available here for download (July 2020)
Note that current version of the codes works with 3 possible horizons of the user's choice
Functions dynamic_networks.m and get_dynnet.m estimates time varying total network connectedness as well as directional connectedness, timing for a 4 variable system, 1832 time observations, and 100 simulations is around 230 seconds. This is for a Desktop PC with 64GB RAM with 3.70 GHz 6-Core Intel Core i7 processor.
The toy data is daily and we provide an example of dynamic horizon specific network with horizons defined as
- short run: 1 - 5 days (up to one week)
- medium run: 5 - 20 days (week up to month)
- long run: 20 + days (more than month)
Dynamic_Nets_Master.m is the master file
dynamic_networks.m is the function with the TVP VAR estimation. See inputs and outputs in the file.
Within this function, you may want to change the variables:
- w which denotes the kernel width (default is set to 8).
- HO which denotes the horizon you compute the wold decomposition for (default set to 10+1 for speed, for applications you should set to large value such as 100. This will cause computation time to increase).
get_dynnet.m is the function that computes the time-frequency network measures. See inputs and outputs in the file. Within this function, you may want to change the variables:
- d1 which determines long-term definition (default is set to >20-days)
- d2 which determines medium-term definition (default is set to 5-days to 20-days)
- d3 which determines short-term definition (default is set to 1-day to 5-days)