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Random Neural Networks for Rough Volatility. [PDF]

open access: yesAppl Math Optim
Jacquier A, Žurič Ž.
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FPGA Implementation of Pseudo Random Number Generators for Monte Carlo Methods in Quantitative Finance

2008 International Conference on Reconfigurable Computing and FPGAs, 2008
FPGA based implementations of two classes of pseudo random number(PRN) generator, intended for use in Monte Carlo methods for finance, are presented. FPGA implementations potentially offer reduced cost and improved performance compared to general purpose processor (GPP) systems such as PCs or mainframes. The first class of PRN generator, which includes
Simon Banks   +2 more
openaire   +2 more sources

Capital Regulation and Shadow Finance: A Quantitative Analysis

The Review of Economic Studies, 2023
This paper studies the effects of higher bank capital requirements. Using new firm-lender matched credit data from South Korea, we document that Basel III coincided with a 25% decline in credit from regulated banks, and an increase of similar magnitude
Hyun-Joo Lee   +2 more
semanticscholar   +1 more source

Applied Quantitative Finance (3rd ed.)

Technometrics, 2019
This section will review those books whose content and level reflect the general editorial policy of Technometrics.
J. N. Orsini
semanticscholar   +1 more source

High performance computing in quantitative finance: A review from the pseudo-random number generator perspective

Monte Carlo Methods and Applications, 2014
Abstract. The great demand for high computational capabilities is omnipresent in every facet of modern financial activities, ranging from financial product pricing, trading and hedging at the front desk on the one end to risk management activities for in house monitoring and legislative compliance on the other.
Michael Mascagni, Yue Qiu, Lin-Yee Hin
openaire   +1 more source

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