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 can be extremely computationally expensive to solve accurately. This demand for higher performance has put a focus on how to get the most out of the latest generation of hardware.
In this article, Dmitry Pugachevsky, Director of Research, analyses the results of this survey and discusses whether banks are ready for counterparty risk elements of Basel lll. Basel III significantly changes the way in which financial institutions address counterparty credit risk (CCR) and credit value adjustment (CVA). Enhancing counterparty credit risk management practices is a key focus for banks. This is in response to changes in accounting rules and new prudential and market regulations, which have tightened substantially following the financial crisis. Collectively, these changes are having a deep impact on the market and the way banks price and manage the risk associated with derivatives.
In this article, David Kelly, Director of Credit Products at Quantifi, discusses how the credit crisis and regulatory responses have forced banks to update their counterparty risk management processes substantially. New regulations in the form of Basel III, the Dodd-Frank Act in the U.S. and European Market Infrastructure Regulation (EMIR) have dramatically increased capital requirements for counterparty credit risk. CVA desks have been developed in response to crisis-driven regulations for improved counterparty risk management. How do these centralized groups differ from traditional approaches to manage counterparty risk, and what types of data and analytical challenges do they face?
One of the key shortcomings of the first two Basel Accords is that they approached the solvency of each institution independently. The recent crisis highlighted the additional ‘systemic’ risk that the failure of one large institution could cause the failure of one or more of its counterparties, which could trigger a chain reaction.
Securitisation swaps are a critical, yet often neglected area of finance markets. This handbook provides an introduction to the basics, through to a detailed discussion of all the key risks and how a transaction is put together from start to finish. In Chapter 7, the authors offer some numerical examples to provide ballpark CFVA costs. These example use sophisticated Monte Carlo analytics developed by Quantifi. Quantifi has an established reputation as the market leader in analytics and is built on the latest technology and incorporating advanced numerical methods. Read More
Webinar with Intel
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).
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