Finance & Banking Data Analytics with GAUSS

Main Applications of GAUSS in Finance

The GAUSS platform provides a powerful and efficient environment for analyzing financial data. GAUSS provides easy-to-use, pre-built financial analysis tools for every stage of your finance project from data wrangling and cleaning to forecasting and reporting.

GAUSS is used in a variety of theoretical and empirical finance applications including quantitative asset management, risk parity, option pricing and hedging, financial risk management, exchange options, and more.

Whether you are just in the beginning stages of data wrangling, cleaning, and visualization or the final stages of estimation and financial forecasting, GAUSS supports your financial data analysis needs.

Time Series, Regression Models and Other Main Functions of GAUSS for Finance

Data cleaning, processing, and management

• Easy data importation with support for SAS, STATA, Excel, CSV, HDF5, GAUSS matrices, GAUSS Datasets, and ASCII text files
• Data visualization
• Recoding and reclassification tools
• Data scaling methods including euclidean scaling, median scaling, maximum absolute value scaling, mid-range scaling, and standard deviation scaling
• Flexible handling of missing values including missing value imputation, pairwise deletion, and listwise deletion
• Dummy variable creation from categorical variables
• Data sorting and merging and both the file and matrix level

General Statistical Analysis

Pre-built GAUSS functions can be used to efficiently and intuitively implement fundamental econometric models including:

• Ordinary least squares
• Weighted least squares
• Generalized method of moments
• Generalized linear model
• Quantile regression
• High-frequency time series plots
• Binomial Options pricing (European and American call and put options)
• Compute Delta, Gamma, Theta, Vega, and Rho for European put and call options using the Black, Scholes, and Merton method
• Compute Delta, Gamma, Theta, Vega, and Rho for American put and call options using the Black, Scholes, and Merton method
• Price American put and call options using Black, Scholes at Merton method
• Price European put and call options using Black, Scholes at Merton method
• Compute implied volatilities for American call and put options using the Black, Scholes, and Merton method
• Compute implied volatilities for European call and put options using the Black, Scholes, and Merton method
• Financial market trading date management
• Maximum likelihood estimation
• Principal component analysis.
• Linear dependency analysis.
• Flexible least squares.
• Cholesky decomposition.
• Eigenvalue decomposition.
• SVD decomposition.

Time Series Analysis

With GAUSS time series analysis is made easy and efficient whether you're just getting started or developing new cutting edge methods. GAUSS time series capabilities include:

• Time series visualization
  • Supports standard frequencies, high frequency data, and irregular frequency data
  • Fully customizable graphics
  • Easy to export, publication-quality graphs
• Comprehensive unit root tests and cointegration tests
  • Augmented Dickey-Fuller unit root tests (ADF)
  • Phillips-Perron unit root tests (PP)
  • Dickey-Fuller Generalized Least Squares (DF-GLS)
  • Kwiatkowski-Phillips-Schmidt-Shin (KPSS)
  • LM tests for unit roots
  • Quantile unit root tests
  • Im, Lee, & Tieslau unit root tests with non-normal errors
  • Flexible fourier GLS, ADF, KPSS and LM unit root tests
• Unit root tests with structural breaks
  • Zivot-Andrews unit root test with a single structural break
  • Narayan and Popp unit root test with two structural breaks
  • Lee, Strazicich, and Mark LM unit root test with one and two structural breaks
• Autoregressive moving average models (ARMA)
  • Seasonal ARMA models (SARMA and SARIMA)
  • Integrated ARMA models (ARIMA)
  • ARMA models with exogenous variables (ARMAX)
• Vector autoregressive models (VAR)
  • Seasonal VARMA models (SVARMA and SVARIMA)
  • Integrated ARMA models (VARIMA)
  • ARMA models with exogenous variables (VARMAX)
• Full suite of generalized autoregressive conditional heteroscedasticity (GARCH)
  • Integrated GARCH models (IGARCH)
  • Asymmetrical GARCH models (GJRGARCH)
  • GARCH-IN-MEAN (GARCHM)
• Vector error correction models (VECM)
• Nonlinear time series models:
  • Structural break identification and modeling
  • Markov-switching models
  • Threshold autoregressive models (TAR)
• Kalman filtering
• Parameter instability tests
  • Chow forecasting
  • CUSUM test
  • Hansen-Nyblom test
  • Rolling regressions

GAUSS Applications Designed for Finance

Application	Description
Time series MT	Includes comprehensive tools for time series data analysis including MLE and state-space estimation Unit root and cointegration testing Model diagnostics and forecasting Nonlinear time series models
Linear regression MT	Provides procedures for estimating single equations or systems of equations including: Two-stage least squares. Three-stage least squares. Seemingly unrelated regression.
Fanpac MT	Provides econometric tools commonly implemented for estimation and analysis of financial data: Allows users to tailor each session to their specific modeling needs. Includes tools for modeling univariate and multivariate Generalized Autoregressive Conditionally Heteroskedastic (GARCH) models.
Maximum Likelihood MT	Provides a suite of flexible, efficient and trusted tools for the solution of the maximum likelihood problem with bounds on the parameters. Includes: Variety of descent and line search algorithms. Analytical and numerical derivatives. Dynamic algorithm switching. Multiple tools for statistical inference.
Constrained Maximum Likelihood MT	Provides a suite of flexible, efficient and trusted tools for the solution of the maximum likelihood problem with general constraints on the parameters. Features include: Linear and nonlinear equality and inequality constraints. Trust region method. A variety of descent and line search algorithms. Analytical and numerical derivatives. Dynamic algorithm switching. Multiple methods for statistical inference.
Optimization MT	Optimization MT provides tools for efficient optimization including: Select descent algorithms. Step-length methods. Algorithm switching.
Constrained Optimization MT	Solves the nonlinear programming problem, subject to general constraints on the parameters. Includes: Linear or nonlinear constraints. Equality or inequality constraints. Uses sequential quadratic programming method in combination with several descent methods. Trust region method.
Descriptive Statistics MT	Provides basic statistics for the variables in GAUSS datasets. These statistics describe and test univariate and multivariate features of the data and provide information for further analysis.
Algorithmic Derivatives	Provides tools for computing algorithmic derivatives. Works independently of other applications. Can be used with any application that needs derivatives. The use of algorithmic derivatives can improve accuracy and convergence speeds.

Industries that use GAUSS Data Analysis Tools

GAUSS is used across a number of industries for financial data analysis. GAUSS is found in

• Universities
• Government agencies
• Non-governmental organizations
• Nonprofit research organizations
• Corporations

Whether your goal is forecasting financial outcomes, hedge fund management, portfolio optimization, or teaching future financial analysts, GAUSS offers the tools you need to succeed.

Benefits of GAUSS for Finance

GAUSS provides a fast and flexible environment for financial data analysis. Whether you are performing ordinary least squares regressions or developing cutting-edge algorithms, GAUSS provides tangible advantages including:

• Over 1000 pre-built statistical and econometric functions.
• Light-weight and efficient analytics engine designed to make the most of your hardware and provide optimized computation speed.
• Intuitive matrix-based programming language for transparent and easy to understand programming.
• Fully interactive environment for speeding up your workflow from exploring data to analyzing results.
• Comprehensive documentation and examples.
• Comprehensive data support including CSV, Excel HDF5, SAS, Stata, text delimited files.
• Relational database support including MySQL, PostgreSQL, SQLite, Microsoft SQL Server, Oracle, IBM DB2, HBase, Hive and MongoDB.

Compatibility of GAUSS with Other Software

GAUSS is built to seamlessly integrate into any analytics environment:

• GAUSS is fully compatible with SAS, STATA, HDF5, CSV, and Excel datasets.
• Efficiently connect powerful analytics to any internal or customer-facing data source, application, or interface with the GAUSS Engine.
• Full technical support for assistance when migrating from and integrating with other software platforms.