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Supported analytical methods include shrinkage estimators, robust portfolio optimization, walkforward portfolio optimization, benchmark tracking, BlackLitterman model, factor models, and many others. Features

Fully supports the multiperiod investment paradigm. 

Fully supports portfolios featuring assets with nonGaussian distribution of returns, or nonlinear interdependencies, including options and hedge funds. This is achieved through direct simulation of portfolio dynamics with no model assumptions. 
Portfolio Construction
Simultaneous creation of two environments for portfolio analysis:


Riskfree asset option. 

Factorselection option for a factorbased asset pricing model. 
Estimation of parameters
Equallyweighted sample estimates of expected returns and covariances 

Exponentially weighted sample estimates of expected returns and covariances (new in v.3.1) 

Stambaugh combinedsample estimates, used if asset histories differ in length. [pdf] 

Jorion expected returns estimate, which shrinks sample average returns to a common value. 

LedoitWolf covariance matrix estimate, which shrinks the sample covariance matrix to the constant correlations covariance matrix. [pdf] 

PastorStambaughWang joint estimate of expected returns and covariances, which shrinks sample estimates to their respective counterparts, implied by the selected factor model. [pdf] 

MacKinlayPastor joint estimate of expected returns and covariances, based on the assumption that prices are explained by an unobservable factor. [pdf] 

The BlackLitterman model that incorporates subjective invetsor views in parameter estimation and asset allocation process. [pdf] 

Dummy estimates of expected returns and covariances further used in construction of riskbased portfolio strategies (risk parity and maximum diversification) (new in v.3.2) 
Portfolio optimization
Four optimization criteria:


Robust portfolio optimization (worstcase scenario optimization): the resultant portfolios demonstrate optimal behavior under the worstcase scenario. [pdf] 

Walkforward optimization:


Optimization engine based on IPOPT (Internal Point OPTimizer) — one of the most powerful nonlinear optimizers available. 
Target shortfall probabilities analysis
Calculation of target shortfall probabilities according to selected ranges for the investment horizon and target rate. 
ValueatRisk analysis
Simultaneous calculation of two risk measures: ValueatRisk (VaR) and Conditional ValueatRisk (CVaR). 

Various techniques for calculation of VaR and CVaR, including: 

Construction of VaR and CVaR surfaces according to selected ranges for the investment horizon and significance level. 
Historical simulations
Simulations of portfolio strategies with continuous rebalancing. 

Simulations of portfolio strategies with continuous rebalancing and portfolio insurance — these strategies are optimal in a situation when a predetermined portion of the initial wealth and/or accumulated profits must be maintained. 

Portfoliostrategy simulations with "inaction region" rebalancing — these strategies are optimal in the presence of proportional transaction costs. 

Portfoliostrategy simulations with "inaction region" rebalancing and portfolio insurance. 
Data management
Choose either an Accessdatabase or Excel spreadsheet format to store your data. 

Several historical data sources:


Batch import from all data sources 

1click update from all data sources 
Miscelaneous
"Threefund" portfolio calculation — utilitybased portfolio, optimal in the presence of an estimation error in the model parameters. [pdf] 

Utilization of Block Bootstrapping algorithm in the calculation of VaR, CVaR, and shortfall probabilities. 

Determine Inaction region optimal size in the presence of proportional transaction costs, based on a multidimensional extension of the DavisNorman approach. [pdf] 

Wide range of optimization constraints, which also include:


Various performance measures including Information ratio, Sortino ratio and STARR ratio. 