WHPCF 2016: Ninth Workshop on High

Performance Computational Finance

The purpose of this workshop is to bring together practitioners, researchers, vendors, and scholars from the complementary fields of computational finance and high performance computing, in order to promote an exchange of ideas, discuss future collaborations and develop new research directions. Financial companies increasingly rely on high performance computers to analyze high volumes of financial data, automatically execute trades, and manage risk. 

As financial market data continues to grow in volume and complexity, computational capabilities of emerging hardware also increases. Extracting high performance from emerging architectures requires a combination of domain knowledge and specialized technical skills. The workshop will explore how researchers, scholars, vendors and practitioners are collaborating to address high performance computing research challenges.

We have peer-reviewed paper submissions that cover various aspects of computational finance. In addition to submissions that deal with performance and programmability challenges, theoretical analysis, algorithms, and practical experience in computational finance, we have focussed on submissions that demonstrate or result from the collaboration between financial practitioners, and academics, researchers, or vendors.

For 2016, we are particularly interested in submissions addressing the following emerging topics in high performance computational finance:

· High performance machine learning for financial trading

· Use of the FPGAs for high frequency trading

· XVA pricing

· Adjoint algorithmic differentiation (AAD)

· Many-core implementation of derivative pricing, calibration, risk management

· Use of high performance Python and R

Additional topics of interest to this workshop include, but are not restricted to:

· Use of accelerator platforms for stream processing

· Fast algorithms for algorithmic trading and high frequency risk management

· Scalable in-memory data processing platforms for large-scale computations

· Software infrastructure for high performance and high productivity

· Use of parallel design patterns for mapping of applications to parallel hardware

· Financial libraries and run-times

· Use of accelerator platforms for stream processing

· Use of heterogeneous hardware in computational finance

· Financial applications of high performance computing: risk algorithms, derivative pricing, algorithmic trading, arbitrage

· High-bandwidth/low-latency streaming of market data

· Cluster computing for computational finance

· Financial data center engineering

· Computational algorithms for finance

· Move from capacity to capability computing in financial applications