Basel III

Risk Training: FRTB Course

Friday, January 19, 2018
As the FRTB implementation date looms ever closer, banks and regulators are still debating the rules and iterations of the regulations. Risk's training course returns to New York to help provide delegates with practical knowledge to better... read more

Risk Training: Fundamental Review of the Trading Book

Friday, July 14, 2017
The Fundamental Review of the Trading Book (FRTB) has been a difficult topic for both banks and regulators over the past few years. With implementation set for 2019, many companies are still establishing what their FRTB strategy will be, as well as... read more

Vectorisation: The Rise of Parallelism

Thursday, July 13, 2017

by Quantifi & Intel

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.

Vectorisation is a key tool for dramatically improving the performance of code running on modern CPUs. Vectorisation 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).

Vectorization, Part 3: Applying Vectorization to CVA Aggregation

Monday, July 10, 2017

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

Explaining the Two Key FRTB Frameworks

Wednesday, June 28, 2017

FRTB is intended to address the undercapitalisation of trading book exposures witnessed during the financial crisis. While the basic goals and ideas of FRTB are simple, it differs materially from the existing Market Risk regulations. FRTB is likely to have a substantial influence in the way firms are organised, and their approach to measuring and reporting risk. There will also be an overall business and operational impact.  Banks need to decide whether the costs associated with operational and IT change is justified.  Read More

Vectorization, Part 2: Why and What?

Thursday, June 22, 2017

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

Vectorization, Part 1: The Rise of Parallelism

Thursday, June 15, 2017

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

CVA Pricing Analysis for Global Financial Institution

Thursday, March 30, 2017
This global financial institution wanted to gain a better understanding of the mechanics of CVA pricing, especially on transactions involving multiple currencies. The firm’s widening credit spread dramatically increased CVA charges levied by dealers. Therefore the client wanted more transparency and a second opinion on these CVA charges. Quantifi generated a matrix of CVA prices and then analysed the differences between its results and the dealer quotes to help the client better understand the pricing dynamics.