This paper highlights the main changes being introduced by the new market risk standards and the related challenges in terms of data management, modelling and technology. The Fundamental Review of the Trading Book (FRTB) heralds a new era in bank risk management, making it one of the most critical items on a bank’s to-do list for the immediate future and beyond. This paper highlights the enormity of the data and modelling requirements imposed by FRTB as well as its impact on banks’ risk and systems architecture. read more
In January of 2016, the evolution of FRTB culminated in the Basel Committee on Banking Supervision (BCBS) publishing the finalised standards, titled Minimum Capital Requirements for Market Risk. The new standards replaced the existing regulatory framework for market risk and go beyond just dealing with quantitative measurement of risk. FRTB is set to revolutionise current market risk practices, placing emphasis on the coordination of operational, risk and data management processes as well as systems and technology. Read More
by Quantifi & Monocle
FRTB is set to revolutionise current market risk practices, placing emphasis on the coordination of operational, risk and data management processes as well as systems and technology. To best respond to these new demands, banks need to make the right strategic and technology decisions and assess the impact on operations and processes across risk, front office, finance and IT. This paper highlights the main changes being introduced by the new market risk standards and the related challenges in terms of data management, modelling and technology.
Quantifi today announced the release of Quantifi Version 15 (V15). This release leverages the latest technology and introduces a broad range of enhancements and support for the latest regulatory requirements including expanded product coverage, advanced data management and next generation analytics. With over 100 new features, this release is designed to further enhance front-to-middle performance, transparency and scalability. read more
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
Piraeus has played a pivotal role in supporting the recovery of the Greek economy and restoring trust in Greek banks. To keep pace with market conditions and ensure compliance with stringent regulation, Piraeus recognised the need to adapt their risk analytics infrastructure to enhance interoperability with other core systems and align front, middle and back office functions. Senior management also wanted to improve risk control, reduce operational inefficiencies and optimise total cost of risk by streamlining processes, IT and operating models.
Quantifi has been voted top Energy Trading and Risk Management (ETRM) platform for ‘Credit Risk’ in the Energy Risk 2017 Software Rankings. These rankings recognise technology providers that have helped the industry evolve with the challenges and opportunities in the energy markets. read more