Financial Modeling Software

Apart from development of applications for financial industry DataArt also runs several ODCs of quants (Quantitative Analysts). Thanks to the access to top-of-the line math and computer science talent in St. Petersburg, Russia and affiliations with world-famous math schools - St. Petersburg State University and University of Fine Mechanics and Optics DataArt can find the best specialists working as quants for DataArt clients. DataArt also runs its in-house educational program focused on financial modeling which allows to attract and train more specialists.

General Financial Mathematics Knowledge of the Quants Group Specific Skills

• Probability; Statistics; Markov chains
• Econometrics; Portfolio theory; Financial time series, Regression analysis
• Statistical pattern recognition, Clustering, Neural networks
• Optimization – convex and global; Adaptive Random Search; Dynamic programming; Multidimensional Optimization
• Stochastic calculus (Ito, martingales)
• Options pricing / hedging; Models calibration
• Regression methods for American options
• Monte Carlo and Quasi Monte Carlo; Variance Reduction methods
• Simulation with Copulas
• Utility theory; Portfolio optimization with capital gains
• Risk measuring (VaR, CVaR)
• FI modeling (HJM, Libor) and Credit derivatives

Specific skills:

• C#/C++; Matlab/Maple; VBA.Excel; SQL
• Extensive experience in numerical programming (PDE; finite elements; wavelet methods)
• Quasi Monte-Carlo and Sparse Grids for high-dimensional integration, optimization and PDEs
• equity trading algorithms of mean-reversion type
• Automated trading systems development and implementation

Econo-Physics Classes:

DataArt and the faculty of Physics Department at St. Petersburg State University joined forces to sponsor a course in IT, Econo-Physics and Complex Systems Management. Graduates conduct their own research, the results of which have been noted to have scientific and practical value by academics and engineers alike. Some abstracts are presented below:

• P.B. Goldin – Analytical Results for Evaluation of Stock Options with Stochastic Volatility
• R.R. Schastlivtsev, A.M. Kuznetsov – Prediction of Crashes and Critical Points on the US Stock Market Using the Method of Modified Local Hölder Coefficients
• E.A Shalaginov – Prediction of Financial Data Volatility Using the Multifractal MSM Model
• P.A. Valinevich – Using Modified Multifractal MMAR Model for Predicting Financial Data Volatility

Success Stories

RELATED ARTICLES:

• IPM Engages DataArt for Development of Risk Management System
  
• FX Bridge Selects DataArt as a Project Technology Partner
  
• Hedging With Technology
  
• The Russians Are Coming
  
• DataArt Launches Dedicated Financial Technology Group
  
• DataArt Announces Formation of DataArt Finance
  
• DataArt exhibits at the Hedge Fund Business Operations Forum at the Princeton Club in New York.