Webinar with Intel

In 2015, OeKB selected Quantifi as its front-to-middle office solution for counterparty risk and IFRS 13. The bank has recently gone live on Quantifi for market risk. The key variable in the measurement and management of OeKB’s market risk is economic capital which is calculated using Value at Risk (VaR) over a one-month time horizon. OeKB was previously calculating HVaR on a quarterly basis using a legacy system. read more

by Quantifi & Intel

As noted, the Finance domain provides many good candidates for vectorization. A particularly good example is the aggregation of Credit Value Adjustment (CVA) and other measures of counterparty risk. The most common general purpose approach to calculation of CVA is based on a Monte-Carlo simulation of the distribution of forward values for all derivative trades with a counterparty. The evolution of market prices over a series of forward dates is simulated, then the value of each derivative trade is calculated at that forward date using the simulated market prices. Read More

This is the second in a series of blogs on Vectorization, which is a key tool for dramatically improving the performance of code running on modern CPUs. Vectorization is the process of converting an algorithm from operating on a single value at a time to operating on a set of values at one time. Modern CPUs provide direct support for vector operations where a single instruction is applied to multiple data (SIMD). Read More

New challenges in the financial markets driven by changes in market structure and regulations and accounting rules like Basel III, EMIR, Dodd Frank, MiFID II, Solvency II, IFRS 13, IRFS 9, and FRTB have increased demand for higher performance risk and analytics. Problems like XVA require orders of magnitude more calculations for accurate results. This demand for higher performance has put a focus on how to get the most out of the latest generation of hardware. Read More

Counterparty credit risk (CCR) is currently one of the most complex topics for financial institutions. This complexity comes from many different sources but is primarily related to the multiple definitions and uses of counterparty credit risk. Therefore, the first question to ask yourself before modeling counterparty credit risk is why do you want to measure it? Read More

In July 2015, the Basel Committee of Banking Supervision (BCBS) published a consultative paper on Credit Valuation Adjustment (CVA) risk to improve the current regulatory framework. In February 2016, the first improvements to this framework have been introduced based on the QIS based on December 2015 results. Read More

by Dmitry Pugachevsky, Rohan Douglas (Quantifi) and 
Searle Silverman, Philip Van den Berg (Deloitte)

With the introduction of the new accounting standard, IFRS 13, the requirement to calculate complex variables, such as CVA and DVA has renewed emphasis. IFRS 13 has significant implications for all entities, including corporates and those in the financial services sector that hold derivatives, which are measured at fair value. CVA and DVA also result in additional challenges when performing hedge effectiveness testing under IAS 39.  This whitepaper examines these challenges and also the different approached for testing hedge effectiveness.