In recent years regulatory demands have permeated all types of financial institutions. Banks are finding themselves under increasing scrutiny to account for their actions with never seen before speed and accuracy. The likes of Dodd Frank, Basel III, and EMIR are necessitating an increasing need to assess, on a pre-trade basis, the credit impact resulting from new OTC transactions.
The most sophisticated methodology available is to use a simulation-based Credit Value Adjustment (CVA). Principally, the methodology involves using Monte Carlo simulations at a set of future time points for every trade. The simulations are then aggregated, netted by legal entity, and a final calculation is made using averages and maximums across all simulations. The number of future time points can vary between 150 and 300 and the number of simulations is typically 1,000 to 10,000 P&L simulations (or more). This methodology therefore encompasses probability of default, potential future exposures, full portfolio effect and netting node awareness.
The problem the banks are facing is that with current technology available, CVA calculations can take anywhere from 30 minutes to 30 hours or more to run for large over-the-counter (OTC) portfolios – clearly not something which fits into the required pre‐trade category. In a typical CVA model, 5 to upwards of 50 million simulated values are calculated per trade. Large sell side portfolios consist of hundreds of thousands, if not millions, of outstanding OTC transactions. The resulting data size alone is in excess of 10 terabytes of data, making effective CVA a true Big Data problem.
It is time for financial institutions seeking to tackle the 3Vs of Big Data – Volume, Variety and Velocity to look at the benefits that they could reap from using in-memory aggregation and analytics technology. Combined with distributed capabilities, in-memory analytics technology is now bringing immediate and tangible value to the complex discipline of CVA, by turning it into an operational tool used by trading desks to make the best trading and hedging decisions. This technology can provide an immediate and detailed understanding of risk exposure by counterparty, and revolutionises risk management by allowing users to hedge CVA precisely with up to date sensitivities. The technology also performs ‘what‐if’ analysis for both pre‐deal checking and counterparty credit change, in real-time.
The Basel III proposals for counterparty credit risk contain significant enhancements to CVA, including the need to account for variation in CVA more precisely than ever before once the legislation comes into effect in 2015. Financial institutions need to see compliance as a positive step, with the most effective technology elevating CVA from being a “after-the-fact” reporting routine, to something which can revolutionise the accuracy of tricky trading decisions.
Innovative data analytics that empower financial services firms to be ready for the unexpected.