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"Graphics processing units (GPUs) represent an increasingly performant and cost-effective means of executing complex calculations, and many capital markets participants are running pilots with them for pricing instruments or analyzing their risk profile...

Software vendors and service providers ease GPU adoption

"...SciComp's flagship product, SciFinance, delivers code synthesis technology (i.e. software that generates software) for pricing complex derivatives. In essence, this approach masks the complexity of parallel programming from the end user, while leaving them free to define the characteristics of the pricing model that they want to run on GPUs. Users can experiment with different solvers, finite difference schemes, or interpolation methods by changing a few lines in the specification. Specifications can be modified to capture particular instrument features and developer's preferences for mathematical constructs and routines. SciFinance then automatically generates fully documented C/C++/CUDA pricing model source code (currently SciFinance automatically generates CUDA-enabled source code for any Monte Carlo pricing model, with support for PDE pricing models scheduled for release later this year)."

Excerpted from an article that appeared in Ovum.

"Reval expanded a relationship with an Austin, Texas, firm so that it can more quickly price over-the-counter derivatives. The New York-based risk management and hedge accounting firm said it will implement SciFinance, a product of SciComp, a provider of automated pricing technology.

The code generation product allows users to develop derivatives pricing and risk models in-house. That will mean companies who use Reval's online accounting services of Reval's software will be able to cut the time it takes to value a portfolio of OTC derivatives.

Instead of needing one hour to conduct a Monte Carlo simulation, financial firms will need only a few seconds or a minute at most, say Reval executives.

Monte Carlo simulation is a common form of pricing complex OTC derivatives. Monte Carlo simulations require running between 20,000 to 50,000 trial sets of conditions and use a great deal of computer processing time. Implementing a Monte Carlo framework over the Web adds extra stress to computer processing times at month-end closing and at times of high system usage, says Reval.

The SciFinance product uses the Computer Unified Device Architecture and high-end graphical processing units (GPUs) from NVIDIA, a Santa Clara, Calif . company whose technology sprung up from the demands of video game play. That means that for Monte Carlo simulations, 50,000 instructions don't have to be sent down a computing path 50,000 times in a row. Instead, 100 core processors will take 500 each. The results then get collated.

"We were looking for a cost-effective and easy-to-deploy solution to improve the pricing of complex derivative instruments,'' said, Ernest Bonsell, senior vice president of product engineering at Reval.

Reducing the time it takes for firms to value their portfolios allows firms to calculate their market risk more quickly as well. About 400 corporations and financial firms use Reval to price their OTC derivatives contracts."

Excerpted from an article that appeared in Securities Industry News.

SciComp, a leader in automated code generation software, is exhibiting at the annual ICBI Global Derivatives & Risk Management Conference in Paris this week. Attendees can learn more at SciComp's talk on May 20th at 12:10 pm, "Automatic GPU Computing For Derivative Pricing Models."

SciFinance leverages the power of high-end NVIDIA GPUs (Graphical Processing Units) to accelerate derivative pricing performance. It automatically generates GPU-enabled pricing model source code for any Monte Carlo-based model.

"We were looking for a cost-effective and easy-to-deploy solution to improve the pricing of complex derivative instruments using PDEs or Monte Carlo simulation in our SaaS product. We found it with SciFinance and GPU-enabled models, without having to become experts in parallel coding or CUDA(TM)," said Ernest Bonsell, Senior Vice President, Product Engineering at Reval.

Monte Carlo simulation requires running between 20,000 to 50,000 trials and uses a great deal of CPU time. Implementing a Monte Carlo framework in a SaaS environment adds extra stress to CPU usage at month-end closing and at times of high system usage. With over 400 clients and 1,325 users, processing speed is an important consideration for Reval.

"Reval benefited immediately from SciFinance's ability to generate GPU-enabled code," said Curt Randall, executive vice president of SciComp. "They can now quickly output code that delivers an immediate 50-300X increase in execution speed."

SciComp has integrated optimal CUDA coding paradigms into SciFinance to take advantage of the parallel processing power in GPUs. For Monte Carlo computations, instead of the CPU computing one path after another, 50,000 times in serial fashion, the CPU can send the GPU requests for each of 100 cores to compute 500 paths and then collate results.

"While SciComp has invested significant effort in producing highly-efficient parallel code for execution on NVIDIA GPUs, frrom Reval's point of view it couldn't be simpler. Financial engineers simply add the keyword "CUDA" to an existing serial specification and regenerate the parallel code in minutes," added Randall.

Excerpted from an article that appeared on Finextra.com

‘CUDA’ is the magic word to direct the newly enhanced SciFinance to CUDA-enable your Monte Carlo pricing models

"...Obviously, speed of model development is important, but so is the execution time of the pricing model. SciComp has partnered with NVIDIA, and we are now taking advantage of their graphics processing unit (GPU) technology. NVIDIA developed a programming language called CUDA that allows you to develop programs that efficiently run on the GPU card. Code that takes advantage of the GPU’s parallelism exhibits tremendous increases in execution speed compared to serial code.

SciFinance can now automatically create CUDA-enabled pricing and risk models, so a customer need only add the keyword ‘CUDA’ to an existing serial model specification. Depending on the type of pricing model, it can run from 30 to 120 times faster than the comparable serial code. One of the best parts from a customer’s perspective is that they do not need to have any CUDA or parallel computing experience, SciFinance takes care of all of that."

Excerpted from an article that appeared in Wilmott.

"...One of the tools NVIDIA has introduced, and a key driver behind the adoption of GPUs in financial markets, is a programming environment called CUDA, which was released two years ago. It is this CUDA architecture inside the chip (as well as a CUDA toolkit and a C-compiler) that enables software developers to tap into the parallel architecture of the GPU using the C programming language.

Based in Austin, Texas, SciComp, a developer of derivatives software, began using NVIDIA's CUDA technology in 2007 so that it could parallelize its code. The vendor's SciFinance product "actually generates the source code for the pricing and risk models," explains Curt Randall, EVP and head of financial engineering as well as a founder of SciComp.

The thousands of lines of C or C++ code generated by SciFinance can help a quant produce a new pricing model in a matter of hours, says Randall. "The compiler and the language make the hardware accessible to general programming as opposed to graphics programming," says Randall, who contends that the NVIDIA hardware can enable the code to run 30 to 50 times faster than standard PCs. "Programmers don't have to understand the nuances of how to parallelize code. All of that is done for them automatically."

Excerpted from an article that appeared in Wall Street and Technology

"..Traditionally, traders who imagined a deal talked to ‘quants’ (PhDs in mathematics or physics who are specialized in finance) and the mathematical equations they came up with for the pricing model then had to be transformed into a program through weeks of hand-coding. After this error-prone process, the program had to be run to estimate the price.

SciFinance’s approach is to automate program generation; instead of manual coding, the quants can ‘simply’ describe their equations in a specialized language. The process is much quicker and much more reliable. Therefore, the relative length of running the program increases and it becomes more of a bottleneck. The solution they came up with is, of course, FPGAs. Uhhh, wait, no, I meant CUDA obviously:

What may not be as obvious in that image is that the only thing you need to add to get GPGPU acceleration is literally ‘CUDA’; it’s a single keyword, not a fundamentally different way to formulate the math equations. This allows SciComp’s customers to save even more time while also improving accuracy (through a higher number of iterations), both of which result in obvious financial benefits.

The difference between merely being able to demonstrate the acceleration of a basic stock option pricing model and this is huge. The amount of work involved is likely ridiculously higher, and it’s a pretty good example of how much far along CUDA is in terms of software..."

Read more of this article  >>

SciComp, an Austin, Texas based software firm, has announced an upgrade to its flagship derivatives trading software package, SciFinance. The upgrade enables customers to utilize NVIDIA CUDA to accelerate their pricing models up to 100x.

The old aphorism 'time is money' has never been more true than in the pressurized world of OTC derivatives trading, where the constant flow of new contracts demands the ability to produce complex mathematical pricing models rapidly," said Curt Randall, executive vice president of SciComp. "This process used to take days if not weeks of error prone hand coding, but with SciFinance, model developers make a few changes to a model specification of a half page or less and then generate accurate C or C++ source code pricing models in minutes."

In order to take advantage of the underlying CUDA abilities, a user need simply to describe the derivative model using SciFinance's high-level financial and mathematical language. Adding the word "CUDA" to a model specifies to output the proper CUDA- enabled code.

SciComp is one of the first companies to fully embrace the potential that GPUs have in the field of computational finance," said Andy Keane, general manager of the GPU Computing business at NVIDIA. "The ability to not just deliver small and incremental increases in performance, but instead to deliver 100X and reduce weeks of hand coding to immediate, real-time results is incredibly powerful. We look forward to working closely with SciComp going forward to bring more defining improvements to the SciFinance generated pricing models and in turn their customersʼ businesses."

Excerpted from an article that appeared in InsideHPC.

To speed up the models produced by its SciFinance automated coding application for rapidly developing Monte Carlo or partial differential equation-based derivatives pricing and risk models, the company introduced the automatic generation of OpenMP or Nvidia CUDA-enabled parallel code. CUDA-based code runs on Nvidia's graphics processors 30-100 times faster than serial code on a standard PC, the company claims. It is also applicable to a range of Monte Carlo pricing models, including those with complex path dependency and Bermudan exercise features. SciCalibrator translates pricing model calibration specifications into C/C++ source code for use with a given model or as a stand-alone routine.

Excerpted from an article that appeared in Risk Magazine.

SciComp plans to introduce a more powerful and concise version of its Aspen specification language to shorten the code of complex partial differential equations and Monte Carlo pricing problems. It will be possible to hide technical and computational details and default to the company's SciFinance system, or expose details for user control. It will provide efficient serial versions, and the synthesised source code will also be structured and optimised for several multi-processing architectures. Generalised likelihood methods will enable computation of Monte Carlo sensitivities with reduced variance.

Excerpted from an article that appeared in Risk Magazine.

"...The simplest general approach for estimating the Greeks is based on finite differences, in which the Monte Carlo pricing function is called to revalue the derivative at perturbed parameters and a finite difference is applied to approximate the partial derivatives (price sensitivities). The advantage of such estimation lies in its independence of the underlying model and payoff structure, enabling a generic implementation with little additional programming..."

Excerpted from an article that appeared in Wilmott Magazine.

For its SciFinance automated pricing and risk model generator, the company introduced SciCalibrator, which allows users to specify calibration problems at a high level and then generate the corresponding source code in C or C++. Version 2.0 of the SciSTCDO pricing and risk engine for single-tranche collateralised debt obligations includes a large pool Gaussian copula model, additional calibration functionality and sensitivity to changes in interest rates and correlation. SciFinance 4.0 C++ code includes the ability to use C++ exceptions to signal errors in place of return codes, making the code more intuitive.

Excerpted from an article that appeared in Risk Magazine.

SciComp, the Austin, Texas based provider of scientific computing solutions to the financial markets, are no strangers to pioneering the development and advancement of technologies...The company has made a name for itself in software synthesis engines (software that writes software), high-level programming languanges, robust, state-of-the-art algorithms and the latest financial instruments, integration efficiency tools, and customzed, off-the-shelf models.

SciComp's philosophical approach to model development for the last 12 years has been to provide clients a set of flexible and robust tools that allow them to design and build pricing and risk models their way. This was first reflected in SciFinance, a cross-asset, in-house development environment for derivative instruments. Users create a high level specification by using keywords, defining arbitrary PDEs, PIDEs or SDEs, and specifying numerical algorithms. SciFinance does the rest by automating the programming task to produce fully documented C/C++ source code and Excel spreadsheets and add-ins, COMs, .NET solutions, or Java wrappers.

"We help our clients develop and implement innovative pricing and risk model strategies. Typical library-based approaches pose a high cost to innovation given that there are so many places in the code which require updating and modification. SciFinance users simply modify their specification to reflect any desired changes and a new pricing executable is created automatically. While SciFinance includes a few hundred model templates to facilitate development, at the end of the day the choices are the quant's to make and the SciFinance-produced code reflects those choice," says Curt Randall, Senior Vice President of the company."

Excerpted from an article that appeared in Wilmott Magazine.

Software is a messy business. Last March the U.S. Federal Bureau of Investigation publicly abandoned a $170 million software overhaul because of unforeseen technical problems. Even when big projects go well, they often take so long to complete that the software is out of date by the time it's rolled out. Corporations often don't bother to upgrade obsolete software for fear that they're opening a can of worms. As software gets more elaborate and complex, the problem only gets worse.

The situation has triggered interest in using computer programs to generate other programs automatically. The benefits of automatic software are compelling. Companies would need fewer programmers and could ratchet up productivity. Humans writing computer code are also prone to errors. "If a programmer can sit down, specify what you want and push a button, you end up much more productive," says Doug Smith, a researcher at the Kestrel Institute, a nonprofit RD center in Palo Alto, California. "It's the next stage in the evolution of computer programming."

One automatic programming tool has already made it into the financial marketplace. SciComp, based in Austin, Texas, has developed a product that helps investment banks design programs to price financial derivatives. It takes complex mathematical models and translates them into something a computer can solve, allowing banks to flexibly change pricing models as they introduce new products and guidelines.

Excerpted from an article that appeared in Newsweek International.

SciComp Inc. has been issued U.S. patent number 6,772,136 for its SciFinance software synthesis technology for financial instrument modeling using Monte Carlo simulations. This patent is in addition to a patent awarded to SciComp for the same problem solving environment, but using systems of partial differential equations.

"We're pleased to be granted this second patent for SciFinance" said Curt Randall, Executive Vice President of SciComp. "It underscores the uniqueness of SciFinance in the financial software marketplace. As more and more banks, hedge funds and other financial firms choose SciFinance as their modeling solution, our continued focus will be in helping them reduce development and market response time for developing exotic derivatives products."

SciComp's flagship product, SciFinance, applies both methods to the pricing of financial derivatives. The product automatically transforms a problem description into executable software code. Over the last ten years, SciFinance has been adopted by some of the world's leading investment banks to quickly prototype and validate option pricing models, as well as to generate production codes.

This article appeared in Wilmott Magazine.

Over the past year, SciComp extended the instrument coverage of its SciFinance automated pricing model generator to include, among others, cash collateralised debt obligations (CCDOs), CDO-squareds and credit-linked range accrual notes. A new generic short rate and hazard rate calibration module calibrates both the short and hazard rates (correlated), using models such as one-factor, Gaussian and extended exponential Vasichek.

SciSTCDO is a standalone pricing, risk and calibration engine for single-tranche CDOs, and provides both a broad, high-performance Monte Carlo simulation method that employs a factor copula approach for handling exotic variations, and a fast specialised semi-analytic approach.

This article appeared in Risk Magazine

The newly released enhancements in SciSTCDO version 2.0 include implementation of base correlations within the Large Pool Gaussian Copula Model (Large Pool Model) and additional calibration functionality. The Large Pool Model is a quick, user-friendly model for valuing CDO structures that requires a minimal amount of data; the newly released enhancements complement the existing analytic capabilities of SciSTCDO's high performance Monte Carlo method and a fast, specialized semi-analytic approach. The trio of pricing approaches provide users alternative valuation methodologies and cross-checking capabilities for both prices and risk measures.

Curt Randall, Executive Vice President for SciComp said, "The Large Pool Model with base correlations is a commonly used, fast and transparent pricing approach that lets both buy side and sell side clients quickly perform consistency checks on market prices."

The two new modules include the Base Correlation Calibrator, that calculates the implied correlation for the given tranche spreads, and the Base Correlation Pricer, a pricing and risk engine that calculates present value, implied spread and risky duration for both market observable and off-market tranches. Both new modules enjoy the speed and tractability of the Homogeneous Large Pool Gaussian Copula model.

This article appeared in Wilmott Magazine.

SciComp Inc., the Austin Texas based provider of automated software synthesis technology for the financial markets, today announced that BOC International Holdings Ltd (BOCI) has licensed SciFinance®, SciComp's flagship automated derivatives pricing software.

"Deciding factors for BOCI purchasing SciFinance included the ability to solve problems through a high level declarative language rather than hand coding," said George Advani, a quantitative analyst for BOCI. "We will use SciFinance to value and hedge equity derivative instruments including baskets and exotics."

Based on SciComp's software synthesis technology, SciFinance automatically generates custom source code from concise specifications of option structures. SciFinance prices the full gamut of derivatives instruments, including equities, credit, interest rate, foreign exchange and CBs.

"We're confident that SciFinance will provide much quicker turnaround time for traders to test out models, and shortened time-to-delivery for system development," added trader Ken Wang.

BOCI licensed SciFinance after a significant testing period. "We received very responsive and professional support from SciComp. Even though we are working in different time zones, our questions were always replied to within 24 hours, and by a qualified person. We are impressed by the support service," stated Hugo Cheng, Vice President of BOCI.

"The ability to automatically generate source code with Excel or .Net wrappers will simplify integrating the codes into our trading system," added NgaiHo Shum, IT manager for BOCI.

Elaine Kant, SciComp's CEO said, "SciComp's patented technology is being adopted by banks worldwide, and we're pleased to build a relationship with BOCI. We're confident that SciFinance will give BOCI a business advantage by reducing the time needed to price and integrate new derivative structures."

This article appeared in Wilmott Magazine.

"...the company plans to release a new examples catalogue for its automated pricing model generator SciFinance, which will enable users to code and price very exotic derivatives. An upgraded version of SciIntegrator will provide extra functionality for developers using Microsoft's .Net and Java..."

​This article appeared in Risk Magazine

SciComp Inc. has released SciCal software for calibrating stochastic volatility (SV) and stochastic volatility + jumps (SVJ) pricing models to market data. The modern search variant used in SciCal is very fast -- SciCal can calibrate from a handful to hundreds of options to market data in under a minute.

This article appeared online at Numa.web 

SciCal specifically calibrates  models that use stochastic volatility (SV) and stochastic volatility + jumps (SVJ).  SciCal was developed in response to the growing use of SV and SVJ models to price structures that are sensitive to forward volatility skew. SciCal can calibrate from a handful to hundreds of options to market data in under a minute.

"Like other calibrators, SciCal searches for parameters that minimize the model-to-market mismatch. What makes SciCal different is the way it searches and how fast it is accomplished," said Curt Randall, Executive Vice President for SciComp. Standard calibration algorithms start from an  initial 'best' guess for model parameters and run until reaching a minimum. Unfortunately, there are typically many local minima, and the initial guess may not lead to the global minimum. Small moves in the market may cause a jump from one minimum to another, leading to instability. SciCal performs a stochastic search of the entire parameter space, climbing out of local minima to find the global minimum.

"Finding the true global minima is the whole point of calibration. If your calibration has unstable parameters, model risk can increase as the market moves," added Randall.

Aimed at quantitative analysts, traders and risk managers can benefit from SciCal runs in Excel or as a standalone executable for Windows or Unix. SciCal allows expert users to adjust important search parameters for complete control over the calibration process, or SciCal can make optimal choices for the user.

This article appeared in Wilmott Magazine.

...Fortis originally used a Monte Carlo application developed in-house, but has now adopted SciMC from Texas-based SciComp, which will automatically generate Monte Carlo code from simple specifications the user makes in a special language called Aspen.

This enables the bank to retain the flexibility it had with its proprietary application while improving the performance of the simulations because, unlike the bank's own generalised Monte Carlo model, SciMC produces a program tailored to the instrument specified, says ter Rahe. SciMC also allows users to inspect the code it produces --an important feature because the degree of error or the bias in the simulations, often resulting from the numbers used being not truly random, are always a concern with Monte Carlo...

Excerpted from Risk Magazine

SciComp, based in Austin, Texas, is planning to introduce a product that will automatically transform brief specifications for Monte Carlo simulations into executable source code. It uses the company software synthesis technology already available for automatically generating option pricing code and spreadsheet add-ins, and SciComp claims it turns out fast and efficient code in minutes. SciComp was awarded a patent on its technology in February.

SciMC can handle exotic path dependencies, exotic underlying processes including jumps, high dimensionality, deterministic sequences and early exercise of American-style and Bermudan options. Users own the code the product produces, which does not need run-time licenses. Using the company's ASPEN language, users can specify any number of stochastic differential equations that describe underlying processes, a payout discount and sensitivity functions.

Other features include the ability to specify discrete events such as dividends, a variety of pseudo-random number generators and the ability to call user-defined random number generators and distribution functions, including deterministic sequences, for performance improvements.

This article appeared in Risk Magazine.

Texas-based SciComp has developed SciMC, a software product for automatically transforming brief specifications into executable C code for Monte Carlo derivative models.

SciMC combines SciComp's intelligent software synthesis technology with Monte Carlo techniques to automatically generate C code without manual programming. The software provides extremely fast and flexible Monte Carlo simulation codes, reducing programming and modeling time for derivative model development, claims the company.

Elaine Kant, president of SciComp, says: "We've found that the codes can run up to 100 times faster than other simulations and solve problems that are beyond library codes and general Monte Carlo engines." SciMC handles sophisticated mathematical and financial features including exotic path dependencies, jumps, high dimensionality, deterministic sequences, choice of variance reduction techniques, and American or Bermudan exercise.

This article appeared online at Finextra.com.

Derivatives pricing software vendor SciComp has licensed its SciFinance and SciXL products to ABN Amro's equity derivatives group.

ABN Amro is using software from the Texas-based firm to design and validate derivatives pricing codes. “We are confident that SciFinance will streamline our workflow for prototyping derivative pricing models. We will use SciXL to run models in Excel without extra programming steps,” said Andrew Greaves, managing director of equity derivatives at ABN Amro.

SciFinance automatically generates custom codes - called C codes - based on brief specifications of derivatives structures. ABN Amro believes the system will reduce both the time and costs involved in developing a new model, as it can automate the process as well as formalising the concepts that analysts and traders use.

“Some codes can be produced in a matter of minutes. More complex codes may take longer. The time depends on the speed of the computer as well as the complexity of the derivative product being priced,” said Stacy Formby, director of marketing communications at SciComp.

SciFinance believes its pricing tools will allow analysts to refine new algorithms without any manual programming, and can free-up time to focus on complex options. It claimed its approach is more accurate than traditional pricing methods, such as in-house coding.

“Because of the automation, SciFinance-generated codes are typically more reliable and consistent than manually generated codes written by different people over a long period of time… Manually generated codes often diverge from their original purpose and can be modified without documentation,” Formby said. 

The SciXL component was added to the product range in September last year - it automatically integrates the pricing models generated from SciFinance into Excel add-in programmes and spreadsheets.

However, the SciFinance system itself is not needed to run the codes, and can be used without the SciXL module. “It can generate C codes that can easily be interfaced to existing production systems,” said Formby.

SciFinance allows clients to generate codes with two different classes of numerical methods for pricing derivatives. ABN Amro has chosen the original method used in the SciPDE module, which uses finite difference techniques to solve systems of Partial Differential Equations. According to Formby, these methods are more accurate than traditional lattice methods but have not been as widely used as they are more difficult to programme.

Formby added that PDE methods can also be more accurate than Monte Carlo methods, but they cannot be used for all derivative types. SciComp is therefore releasing SciMC, a new module that will allow customers to generate codes using Monte Carlo methods. “We've found that the codes can run up to 100 times faster than other simulations and general Monte Carlo engines,” claimed Elaine Kant, president of SciComp.

This article appeared online at RiskNews.net.

ABN Amro has licensed SciFinance and SciXL software products from automated derivatives pricing technology vendor, SciComp.

The bank will use codes generated by SciFinance to rapidly prototype equity derivative instruments. SciFinance generates custom code from concise specifications of derivative structures without manual programming. The SciXL option automatically transposes pricing models into Microsoft Excel spreadsheets. SciComp claims the software decreases the turnaround time for derivative model prototyping and allows analysts to easily explore and refine new algorithms and complex options.

Andrew Greaves, managing director at ABN Amro, says: "We will use SciXL to run models in Excel without extra programming steps. Together, SciFinance and SciXL will reduce the time and cost involved with new model development."

This article appeared online at Finextra.com.

6,173,276

System and method for financial instrument modeling and valuation; Elaine Kant, Curt Randall.

This notice appeared in the Austin American-Statesman, May 21, 2001. Source: Locke Liddell & Sapp.

SciComp Inc. has released SciXL, a Microsoft Excel add-in generator for SciFinance, the company's flagship product. SciFinance uses software synthesis technology to enable financial analysts to quickly build complex option pricing models without manual programming. Now SciXL provides an easy-to-use tool for integrating code into Excel. SciXL automatically builds programs and custom spreadsheets, allowing programmers to concentrate on their core skills.

"Just as SciFinance enables you to produce custom pricing codes without programming," said Elaine Kant, president of SciComp, "SciXL automatically integrates SciFinance-produced pricing models into your Microsoft Excel spreadsheets." Quantitative analysts, traders and IT integrators can all benefit from having models in a familiar spreadsheet interface. Users can change input values and get updated output values instantly--no knowledge of C code is required to run new numbers.

SciComp Inc. co-developed SciXL with Planatech Solutions Ltd., a London-based software development and consulting company. "Working with SciComp to automate the integration of SciFinance code into Excel was a good fit, since we specialize in tools that interface between different languages and technologies," said Bruce Belson, Managing Director of Planatech Solutions. "SciXL is a breakthrough, since it builds the add-in for you, and creates and maintains all the code that links the add-in programs to Excel. In addition, add-ins written in C++ perform much faster and are more stable than those written in Visual Basic."

This article appeared in Financial Engineering News.

"SciComp, based in Austin, Texas, has produced an artificial intelligence package that will turn an analyst's options pricing model into a computer program."

Excerpted from Risk Magazine.

"Anything you can write in terms of a partial differential equation, the software can price."

Excerpted from Derivatives Strategy.

"Smiles, smirks, skews, sneers...these rather whimsical terms inthe volatility vernacular describe an important reality: implied Black-Scholes volatilities are not constant...In this article, we describe a very simple model of the local volatility type that nevertheless provides a rich framework for capturing volatility smiles and skews."

Excerpted from Risk Magazine.

SciComp Automates Algorithm Production at Merrill, Derivatives & Risk Technology

Merrill to Use SciFinance to Price Equity Derivatives Derivatives & Risk Technology