Program for Dynamic Connectedness Approach

Financial and Macroeconomic Connectedness is important for a number of financial activities, including but not limited to, risk management, asset allocation, and investment. Since its introduction, the connectedness method proposed by Diebold and Yilmaz (2014) has become one of the most popular econometrics approaches to measuring the connectedness index among financial and macroeconomic variables. There have been a number of high-quality journal articles using this approach over the last five years. Luckily, you do not need to be an econometrician and programmer to use this method. This post gives you the information about two sources that you can exploit to perform the Diebold and Yilmaz method easily:

  1.  Dr. David Gabauer establishes an online platform to run the method for the VAR and TVP-VAR model. You just need to upload the excel file of the data and the online platform will run all the steps and return the outputs. Link.
  2. If you are familiar with R, you can exploit the R packages for estimating the Diebold and Yilmaz connectedness. You can use the Frequency Connectedness R package for this task. Link

R package for MIDAS-based VaR and ES forecast

Our member, Dr. Trung Le, provides an R package to perform MIDAS-based VaR and ES forecasting. This package helps to calculate and predict VaR based on the Conditional Autoregressive Value at Risk (CAViaR) model of Engle and Mnagnelli (2004), MIDAS quantile regression of Ghysels et al. (2016). The package can also help to calculate ES based on the Extreme Value Theory of Manganelli and Engle (2004), and Asymmetric Laplace of Taylor (2019). You can read more (in Vietnamese) and download the package at the link below. 

Read More

R package for Autoregressive Conditional Density Model

Our member, Dr. Trung Le, provides an R package to estimate the Autoregressive Conditional Density Model. There is an introduction to the package written in Vietnamese. You can read more and download the package following the link below.

Read More