HPCiD

Version 1.2.0

High Performance Computing in Derivatives

The engine design of HPCiD takes into account the different needs of front, middle and back-end users in use, and allows the operator to flexibly call the required relevant information during the implementation process. In response to the bank's current valuation and risk measurement needs, the engine provides relevant information around valuation and Greek letter risk sensitivity (Greeks) calculations, such as valuation results, cash flow, discount factor, probability interval, key term sensitivity, Delta, Multi-dimensional data such as Gamma ensures that the need for process transparency and detail is supported for business processes.


The engine architecture can be adapted to the infrastructure of the financial institution. Information security to ensure that sensitive data does not leave the intranet is a common requirement of domestic users for the system. HPCiD can be fully deployed in the hardware architecture and network of the industry, and can be connected with upstream and downstream databases, risk management, capital management, financial statements and other systems through the REST API interface.



Deployment of HPCiD

Local deployment
HPCiD can be embedded in local system of client's financial library
SaaS
HPCiD can also be access with SaaS service, which can use full scope of derivatives pricing library, FDDR, powered by FPGA accelerator

Interfaces of Administration Management

Screenshot of HPCiD

Administration Management system of HPCiD

XINTONGJIETU D BNGFM

Derivatives Library

  • Models
  • Details
  • Remarks
HW1F Hull White 1 FactorsValidated
HW2F Hull White 2 Factors Validated
LV Local Volatility Validated
The engine architecture can be adapted to the infrastructure of the financial institution. Information security It is a common requirement for domestic users to ensure that sensitive data does not leave the intranet. HPCiD can be deployed in the industry's hardware architecture and network as a whole, and can be connected with upstream and downstream databases, risk management, capital management, financial statements and other systems through REST API interfaces. On the other hand, for front-end structured derivatives product managers, traders and other users who are sensitive to the market and need to develop new products to achieve flexible pricing, HPCiD has a cloud version for users to flexibly call the pricing quantitative model library, and realize independent writing of structural scripts to test pricing operation.

Derivatives Models

FDQL (Financial Derivatives Quantitative Library) is the core module that supports various derivatives structures. The model library contains financial engineering models ranging from simple to sophisticated financial derivatives pricing, such as Black Scholes, Local Volatility, Bachelier, SABR, Hull-White, etc. . Most models offer both analytical and numerical solutions. The analytical solution is to achieve fast and accurate pricing for the product structure after the derivation of rigorous mathematical formulas, avoiding problems such as slow convergence, deviation and instability of subsequent sensitivity calculations caused by the short product period and interval characteristics of the montage simulation, and can provide logarithmic values. Reliability verification of the solution. The numerical solution is to use the Monte Carlo method with a calibrated stochastic model to simulate the underlying risk factors of derivatives, and to obtain the underlying asset price by calculating the value path through the law of large numbers.
HW1F HW2F Local Vol TARN CVA FRTB

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