Sergey Maidanov

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In the past 20 years, computerization has driven explosive growth in the volume of financial markets and in the variety of traded financial instruments. Increasingly sophisticated mathematical and statistical methods and rapidly expanding computational power to drive them have given rise to the field of computational finance. The wide applicability of these(More)
A few recent processors allow very efficient double-precision floating point interval arithmetic for the basic operations. In comparison, the elementary functions available in current interval arithmetic packages suffer two major weaknesses. The first is accuracy, as intervals grow more in an elementary function than what is mathematically possible(More)
We present a validation and test methodology for a non-deterministic system, namely a True Random Number Generator (TRNG). The TRNG testing methods at Intel have matured over time, and what we present here is the 3rd generation methodology used in our latest chipset products. In addition to well known DFT and DFV techniques, testing of a TRNG requires(More)
A fundamental part of a system's quad floating-point precision support is its companion mathematical library. We developed a hierarchical C macro based methodology for implementing the quad precision elementary functions both portable and optimized for Intel® architectures. When two or three floating-point values natively supported in the hardware are(More)
Monte Carlo simulation is one of the recognized numerical tools for pricing derivative securities, particularly flexible and useful for complex models of real markets. The goal of this article is to compare performance advantages and simplicity of using random number generators available in some industrial numerical libraries. For that purpose a simple and(More)
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