The CheckRisk Network Risk System is a suite of agent-based macro-financial modelling techniques designed to visualise complex data easily. The system provides access to some of the most sophisticated econometric models ever created. These can be used to spot hidden risks or identify opportunities at the macro or financial level.
By using the latest advances in complex network theory, we can provide you with the visualisations that will best help you understand the problems you face. The beauty of the end result is that anybody in your organisation will be able to understand and use the outputs.
Rather than swamping you with hundreds of available options, we will listen to your needs and provide the best solutions for you on a consultancy basis.
CheckRisk BankFox is a ground-breaking application of the Network Risk System designed for corporate treasury managers and those interested in understanding counterparty risk. Traditionally this has been done via the use of credit ratings or CDS. However, BankFox gives you real-time information on the credit quality of financial institutions. It shows you that there can be a wide dispersion of credit quality within a single credit rating band. The question is whether you are being rewarded with a rate of interest that reflects these differences? The output that BankFox provides can help corporate treasurers ask the right questions and optimise their portfolio on a network basis to achieve better risk-adjusted returns.
By using a network based approach you can easily see the relationships and the importance of individual nodes.
It is easy to create stress tests based on these correlations to see the effects on the network.
The network could be a portfolio of holdings across asset classes. It could be a network of banks, for treasurers interested in counterparty risk; or it could be a network of sovereign countries, for those interested in credit and systemic risk.
As we all know, correlation is not the same thing as causation – this is why we offer a complementary suite of other methods that can address this problem. Together, we can help you get a much clearer picture of the true risks you face.
If you really want to see what is going on in a network you have to use causal techniques – implied correlations offer a quick solution that can narrow down scenarios quickly, but they offer no proof.
We solve this by applying various Bayesian methods and by using Granger causality. The end result is that you can see how contagion spreads through a network.
You can stress joint probabilities, for example, to understand the potential losses of a portfolio if the US raises rates, the oil price falls to $20 and the Chinese Yuan devalues by 20% within the same time period.
The beauty is in capturing complex non-linear effects that make a difference to how you manage your risk.