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
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  • Monte-Carlo Simulation
  • Bootstrap Simulation
  • Block Bootstrap Simulation
  1. Simulation

Simulation Methods

Empirical methods for risk management and wealth analysis

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Last updated 4 years ago

Monte-Carlo Simulation

The most commonly used simulation method, Monte-Carlo simulation, is a multi-variate normal model to simulate asset returns using the specified return and risk estimation models.

While the number of possible future scenarios (paths) can be controlled by the user, typically the default value of 5,000 is sufficient to generate reasonably stable results without exhausting computer memory (RAM).

The returns of each asset are simulated for each period within the investment time horizon. Using the simulated asset returns, portfolio weights, and an annual rebalancing interval, we derive the portfolio return and level of portfolio wealth. We use this simulated set of empirical data to evaluate risk statistics and wealth analysis.

Bootstrap Simulation

Bootstrapping is procedure by which new samples are generated from an original dataset by randomly selecting independent cross-sectional observations from the dataset. Bootstrap simulation offers the advantage of using actual empirical experience to simulate future scenarios capturing non-normal characteristics such as fat-tails. Whereas Monte-Carlo allows various specification of risk and return models, the Bootstrap method assumes that the historical risk model prevails.

Block Bootstrap Simulation

Block Bootstrapping preserves the serial dependence properties of the data by sampling contiguous blocks of cross-sectional returns with replacement (as opposed to independent cross-sectional returns) from the empirical dataset.

Simulation Configuration Options