Windham Portfolio Advisor
  • Windham Portfolio Advisor Support
  • Installation
    • Installing the Windham Portfolio Advisor
    • Installation Prerequisites
    • Installation FAQ
      • License Key Management
  • Time Series
  • Managing Custom Time Series
  • Custom Time Series Excel Add-in
  • Custom Time Series Utility
  • Updating the Windham Time Series Database
  • Mixing Data Periodicities within a Case File
  • Hedged and Unhedged Time Series
  • Overlays
  • Expected Risk
    • Annualizing Volatility and Return
    • Correlation
    • Covariance
    • Exponential Risk
    • Quiet and Turbulent Risk
    • Series Filter
    • Views (Risk and Correlation)
  • Expected Returns
    • Historical Returns
    • Equilibrium Returns
    • Implied Returns
    • Black-Litterman
    • Blend
    • Estimating Future Value: Arithmetic or Geometric
  • Optimization
    • Multi-goal Optimization
    • Transaction Costs and Turnover Controls
    • Risk Aversion
    • Full-Scale Optimization
  • Simulation
    • Simulation Methods
  • Exposure to Loss
    • Value at Risk
    • Probability of Loss
  • Risk Budgets
    • Risk Budgets
    • Value at Risk Sensitivities
  • Factor Analysis
    • Windham Factors
    • Factor Analysis
  • Cash Flow Analysis
    • Cash Flow Rules
    • Distribution of Wealth
    • Target Wealth Probability
  • Miscellaneous
    • Effective Tax Rates
    • Shadow Assets, Shadow Liabilities, and Illiquidity
    • Asset-liability Optimization
Powered by GitBook
On this page
  1. Expected Returns

Blend

Blending different expected return models

PreviousBlack-LittermanNextEstimating Future Value: Arithmetic or Geometric

Last updated 4 years ago

The WPA provides the option to blend any two expected return models. Users must select Blend Type One and Blend Type Two from the drop down menus in the parameters box.

When the Blend column is selected, a Confidence column appears to the left of this column.

This confidence level applies to the estimation type which is selected in Blend Type One. For example, if Blend Type One is historical and the Confidence level is 60%, the historical returns will be given a 60% weighting in the blend calculation.

Blending two estimation types can help users to develop more throughout estimations by balancing out the pros and cons of different estimation methods.

Blend Model Parameters
Confidence levels for the Blend Model