Value-at-Risk: Its Pros and Cons

Explaining advantages and pitfalls of different VaR-based statistics (VaR, CVaR, MVaR, and HVaR) for hedge fund risk assessment.

One of the main challenges in hedge fund risk assessment relates to inapplicability of the mean-variance models due to non-normal distributions of hedge fund returns. The estimate of risk by variance is only appropriate, when returns are normally distributed or investors exhibit quadratic preferences. Examination of returns of different asset classes shows that traditional instruments, like stocks or bonds, demonstrate distributions more or less approximated by normal, whereas derivatives evidence a high level of irregularities as skewness and kurtosis excess. Numerous studies evidence that most of hedge funds expose asymmetrical returns, thus making the mean-variance model hardly applicable in principle.

The Value-at-Risk statistics emerge as a natural choice for estimating tail risks of hedge funds regardless of their distributions of returns. However, the Value-at-Risk has its own drawbacks and can deliver unreliable results if used blindly. This tutorial discusses the usage of different VaR derivatives (CVaR, MVaR, HVaR etc.) for hedge fund risk assessment and the implementation of the VaR frameworks in Risk Shell.

Value-at-Risk And Its Derivatives For Hedge Fund Risk Assessment

• Understand the classic VaR model and its limitations.
• The main VaR problems and solutions. Non-convexity and subadditivity.
• Understand VaR derivatives: Conditional Value-at-Risk (CVaR), the Cornish–Fisher expansion or Modified Value-at Risk (MVaR) and Hybrid Value-at-Risk (HVaR).
• Analytical and stochastic simulation VaR calculations.

Value-at-Risk In Risk Shell: A Guide For Hedge Fund Investors

• Using VaR statistics for asset selection.
• Using VaR statistics for portfolio optimization and risk budgeting.
• Working with the VaR in Peer Group Analysis.
• The VaR and marginal risk contributions for fund of funds and hedge fund portfolios.
• The VaR and FlexiRank™: creating a prefect synthetic risk statistic.

Potential Audience

Institutional portfolio managers, hedge FoF and multi-asset portfolio managers, risk managers, CIOs, advanced family offices.