In the aftermath of the 2007/2008 global financial crisis, financial institutions have shifted their focus to more actively manage the risks associated with over-the-counter (OTC) contracts. It is now standard practice to adjust derivative prices for the risk of the counterparty’s or one’s own default, by means of credit or debit valuation adjustments (CVA/DVA). Further, the cost of funding a trade has increased significantly after the crisis, as banks can no longer borrow money at the risk-free rate. This cost is accommodated for by the funding valuation adjustment (FVA). More such adjustments are in use in practice, for example for the cost of regulatory captial (KVA), for initial margin costs (MVA), or replacement costs (RVA). All these adjustments are commonly referred to as XVA.
This white paper gives an overview of the different XVA adjustments, shows how they are typically computed, and outlines where the computational challenges lie.
A Guide to xVA Algorithmic Optimisations
It is standard practice today to adjust the price of a traded financial instrument for the risk of the counterparty’s or one’s own default, by means of Credit Value Adjustments (CVA) and Debit Value Adjustments (DVA). Further adjustments are in use
in practice, for instance for the cost of funding (FVA), for the cost of capital requirements (KVA), or for the cost of initial margin (MVA). A common term covering all these value adjustments is XVA.
Calculating these XVAs is of paramount importance to financial institutions – for trade pricing, risk management, and regulatory capital. At the same time, it is a compute intensive calculation and quantitative analysts are striving to put efficient implementations in place. The first and obvious step is to carefully think about possible mathematical optimisations, before starting to put the software framework in place.
This paper describes major algorithmic optimisations for XVA and illustrates the possible computational savings.
Today Xcelerit announced the release of version 2.7 of its SDK, adding tight integration for IBM’s Platform Symphony and support for NVIDIA Tesla K80 GPU. A complete list of the updates and new features can be found in the Release Notes shipped with the package.
The Xcelerit SDK 3.0.0b now supports Intel’s Xeon Phi co-processor and offers improved vectorisation support for Intel’s CPU processors. The Xcelerit SDK can now generate efficient code for Nvidia GPUs, multi-core CPUs, Xeon Phi, and combinations of these – from a single C++ source code. In this post, we will show how to implement a super-fast financial application with minor changes to an existing sequential C++ implementation.
Today NVIDIA announced the release of their fastest ever accelerator for scientific computing – the Tesla K80. It has more raw compute power than any other GPU card available on the market, featuring two of the new GK210 GPUs. But what performance improvements can we expect for typical financial applications? Xcelerit have been given early access to the card and put it to the test, comparing it to their previous flagship, the Tesla K40.
Last month saw a gathering of who’s who in Fixed income circles at the Hotel Condes de Barcelona for the 10th Fixed Income Conference. Delegates were able to avail of a pre-conference workshop on Wednesday followed by two days of events where experts in every aspect of quantitative finance were represented.
In our previous blog post, we have seen that Haswell (Xeon E5 v3 series) achieves a significant performance boost compared to the previous generation Ivy Bridge processor (Xeon E5 v2 series). But let’s see how its performance compares to Intel’s flagship accelerator processor, the Xeon Phi, for a popular application in computational finance.
Intel just released its new Haswell server processor line (Xeon E5 v3 series), promising significant performance gains over previous-generation Ivy-Bridge processors. In this post, we will compare the two generations for a popular financial application – a Monte-Carlo LIBOR Swaption Porfolio pricer.
Today Xcelerit announced the release of version 2.6 of its SDK, increasing performance significantly and further improving the user experience. A complete list of the updates and new features can be found in the Release Notes shipped with the package.
Xcelerit were represented at last month’s Global Derivatives conference in Amsterdam’s Okura Hotel. This is the largest annual gathering in Quantitative Finance
We found the latest advances for reducing the complexity of xVA calculations particularly exciting. For example, Jesper Andreasen (Dankse Bank) presented a number of smart algorithmic optimisations for xVA, Adil Reghai (Natixis) showed how to use algorithmic differentiation cleverly to reduce CVA complexity, and Andrew Green (Lloyds Banking Group) presented a highly efficient method to compute FVA of VAR-based inital margin. Additionally, for the first time this year, the conference featured a stream about “Innovations in Computational & Numerical Efficiency,” with a number of implementation-centric talks on how to cope with the computational complexities of financial analytics.
The audience of more than 500 quants were revived at break time by Xcelerit’s crack team of baristas, who sent them back to the following sessions freshly caffeinated and ready to absorb more.