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Constructing a robust hedge fund portfolio always involves the task of finding an optimal asset allocation satisfying the given investment objectives. Portfolio optimization may incorporate a broad range of risk metrics as well as various constrains (leverage, liquidity, category limits and so on). Going far beyond the conventional CAPM optimization framework, Quant delivers industry’s genetic optimization system for hedge fund portfolios.

Structure

Typical Tasks

  • Optimizing portfolio risk/return profile
  • Applying custom constraints and risk metrics for portfolio construction
  • Enhancing returns (minimizing risks) for a given portfolio
  • Eliminating excessive risk assets
  • Analyzing a real efficient frontier with VaR-based metrics

Features

  • Genetic optimizatrion routine for VaR-based risk metrics
  • Quadratic optimization for non-convex functions (MVaR and CVaR)
  • Various types of optimization: VaR, CVaR, MVaR, volatility, local correlation, max drawdown
  • Interactive efficient frontier charts
  • Background optimization option
  • Seamlessly integrates stochastic simulation providing a distribution of return function as an output

Why It Matters

An often asked question is: "Why does Quant Optimum include genetic algorithms traditionally used for the NASA research type projects? What is wrong with common Excel-based optimization?" The answer derives from non-normality of hedge fund distributions of returns.

Since the mean-variance theory and, consequently, the well-known quadratic optimization methods, are hardly applicable to hedge funds, we have to optimize the VaR-based objective functions better adapting distribution non-normalities. However, because the VaR presents a non-convex multiextreme function, the simplest optimization routines, widely used for classic portfolio optimization, become inappropriate (click here for the explanation). The genetic optimization algorithms, in turn, present one of the best known solutions for multiextreme optimization.

Another approach to avoid the complexity of multiextreme optimization is to use the alternative VaR metrics like MVaR or CVaR, which effectively leads to simple local optimization. However, these metrics are not free from drawbacks (see the Knowledgebase for the details). Addressing the explained problems, Quant Optimum delivers both the global VaR optimization framework and the quadratic solutions based on the alternative VaR metrics.

Screenshots

Objective Functions (examples)

  • Minimization of VaR with the mean return within the given range
  • Maximizzation of the mean return with VaR within the given range
  • Minimization of local correlation with the VaR and the mean return within the given range
  • Maximization of the mean return with the VaR and local correlation within the given range
  • Minimization of the CVaR with the mean return within the given range
  • Maximization of the correlation to a benchmark (trend following portfolio) with VaR within the given range