XlQuant is an Add-in for Excel, it turns your favorite spreadsheet into a professional quantitative analysis software. All functions are implemented with high scientific standards and with the aim of maximum performance. Its objectives are to bring you the latest advances in financial modelling and make them accessible to as many people as possible.
XlQuant adds more than 100 functions to Excel that will allow you to focus on your work in a familiar environment.
The official documentation can be found Here
XlQuant’s architecture is based on three modules and are designed to cover a wide range of financial and statistical models. These three modules are:
The Portfolio module
- Computes Mean-Variance Optimal Portfolios for :
- a given return,
- a given level of risk
- a given degree of risk-aversion
- With several constraints :
- Full Investment
- No Short Shell
- No Borrowing
- Box
- Turnover
- Cardinality (to imposes a maximum number of selected asset)
- Computes Black-Litterman portfolios
- Computes the Value-At-Risk and Conditional VaR of a Portfolio with
- the historical method
- the parametric method (Normal/Student factors)
- Computes the Global Minimum Variance and Tangent Portfolios
- Computes Equally-weighted risk contributions (ERC) portfolios
- Computes several metrics of risk (marginal contribution to risk, concentration ratio…)
The Volatility module
- Estimating and Forecasting Univariate Garch Models
- Supported models : GARCH, GJR, FIGARCH, GAS, APARCH, EGARCH, IGARCH and RiskMetrics.
- Estimate and Forecast the conditional mean and conditional variance
- Supported distributions: Normal, Student and Skew-T
- Allow for modelling the conditional mean with an ARMA process.
- Estimate the Value-At-Risk
- Estimating and Forecasting Multivariate Garch Models:
- Supported models : CCC, DCC, RiskMetrics, Diagonal-Bekk and Scalar-Bekk.
- Estimate and Forecast the conditional mean, conditional variance, conditional correlation
- Supported distributions: Normal and Student
The Tools module
- The Tool module contains several helper functions to manipulate matrix.
- Ex: Singular Value Decomposition, Cholesky decomposition, transform a matrix into the closest positive definite matrix, check for positive definiteness of a matrix …