Smart PortfoliosTM Dynamic Portfolio OptimizationTM (DPO) asset allocation system applies Extreme Value Theory, including GARCH (Generalized Autoregressive Conditional Heteroskedasticity) and other advanced data management solutions, to make better assessments and projections of the risk-adjusted returns of competing investment opportunities.
Understanding “risk” is DPO’s foundation. DPO helps enable better allocation decisions, resulting in higher risk-adjusted returns. In addition, the DPO system incorporates a diversification model to measure the dynamic changes in correlation during volatile markets, helping to reduce the odds of large losses. Smart PortfoliosTM’ improvements to its DPO process offer an upgrade to the asset allocation solution
Smart PortfoliosTM applies the DPO engine to optimize portfolios of mutual funds, money managers, indices or ETFs (Funds), to create portfolios for investors who have different risk tolerances but the same goal of maximizing risk-adjusted returns.
| Theory | Modern Portfolio | Extreme Value |
|---|---|---|
| Methodology: | Mean Variance Optimization | Dynamic Portfolio Optimization |
| Risk Measurement: | Standard Deviation | Expected Shortfall w/ Student-t |
| Return Forecasting: | Mean Variance | Monte-Carlo Modeling w/ GARCH |
| Diversification: | Linear Correlation | Copula-based Dependency |
| Data Distribution: | RWR Normal Distribution | Heavy-Tailed Stable-t Distribution |
| Model Features: | Static: MVO Model | Dynamic: Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH) |