NAG’s extensive mathematical and statistical software can help bring quicker solutions to many problems arising in the Finance Industry including:
- Portfolio Optimization
- Quantitative Analysis
- Risk Management
- Trend Forecasting
- Portfolio Enhancement
- Index Tracking
- Derivative Pricing
Portfolio Modelling and Index Tracking
The NAG optimization routines are ideal for use in portfolio modelling and index tracking. The large number of specialized optimization routines include a sparse nonlinear optimizer. They enable you to optimize portfolios containing a large number of different assets, using more sophisticated models than the standard Markowitz model. A sparse optimizer is much more appropriate for models typically used in finance, since it takes advantage of the sparsity of the problem and therefore uses less memory and results in faster performance.
NAG’s wide range of statistical routines may also be used to build a competitive portfolio: NAG has routines to build variance-covariance matrices from raw data. Cluster analysis routines enable stocks to be grouped according to prescribed criteria. From the resulting clusters, individual stocks may be selected to represent the group in a balanced portfolio. Time series routines indicate and predict trends.
NAG has a variety of routines that can assist in derivative pricing. For the solution of PDEs we have routines to solve parabolic and convection-diffusion problems. We even have some specific Black-Scholes solvers.
From the statistical areas we can point to the GARCH routines which enable modellers to assess historical volatility and predict future volatility more accurately. The extensive suite of pseudo and quasi-random number generators are invaluable for Monte Carlo simulations.
Our curve and surface fitting routines are widely used to smooth surface data; this is often helpful in developing financial models.
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