The evaluation of a portfolio’s overall risk exposure to normal or Black Swan market conditions cannot be adequately described by a single risk statistic. Instead, it requires the deployment of a selection of risk metrics and calculation methodologies, to ensure that the risk that one assumes to produce a performance, complies with given investment policy criteria.
Max Portfolio Loss Estimation
When managing a multi-asset portfolio, one wishes to quantify the potential risks that the portfolio is exposed to. A first line of assessment can utilize the prediction of the maximum potential portfolio loss over a selected time horizon, under a certain confidence level, known as Value at Risk (VaR).
VaR is a regulatory approved ex-ante risk metric, an estimate of the maximum amount that a portfolio could lose overnight, over a month or a year, depending on the probability confidence level and the time horizon that investors choose as the most representative for their portfolio objectives.
The most frequently used models for a VaR estimation are:
- Historical Simulation
- Monte-Carlo Simulation
- Parametric (Variance-Covariance) Method.
There is not an absolute answer to which is the ‘best’ estimation model, as each method has its pros and cons. However:
- If one wants to easily communicate the VaR estimation to a not so quantitatively prone audience, the Historical Simulation may be a preferred route
- For a most advanced methodology which does not explicitly rely on how the markets have behaved historically, the Monte-Carlo simulation could be the method of choice
- The Parametric method is easy to implement and requires no significant amount of data, but it may not be as widely utilized due to its assumption that market returns follow a normal probability distribution.
Risk Concentration
The aim of identifying large risk concentrations to specific portfolio segments is the ability to actively manage diversifiable risks. The Component VaR represents the amount of portfolio risk due to the asset allocation in that segment.
For example, let us assume that a balanced multi-asset-class portfolio is allocated to industrial sectors as: Financials 30%, Energy 30%, IT 40%, whilst the Component VaR estimation is: Financials CVaR 60%, Energy CVaR 25%, IT CVaR 15%. So, although Financials and Energy have identical portfolio weights, Financials amount for 60% of the total portfolio risk exposure, whereas Energy only for 25%, indicating a possible rebalance to mitigate the concentrated risk.
Furthermore, one can produce a risk decomposition for categorizations such as asset class, sector, risk country, reference currency, issuer credit rating and underlying security holdings, to identify asymmetries between the return contribution of each segment and their associated risk exposures.
Tail Risks
When unexpected extreme events occur (‘tail’ risks in the distribution of returns), a widely used estimation method for the portfolio risk is the Conditional VaR, also known as Expected Shortfall.
Conditional VaR tells us what the portfolio’s potential loss could be if the realized market returns occupied the tails of the return distribution curve. It therefore indicates what can happen to a portfolio if the markets move so excessively and unexpectedly that a 1% probability event (i.e. the ‘residual’ in the 99% confidence level) of the VaR estimation method does indeed occur.
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Objecutive Inc. is a software product and services company providing enterprise solutions to the financial services industry. Its flagship product, FundStudio, is a real time portfolio management system for investment managers, which excels in a multi-asset class environment with a wide instrument coverage, including equities, options, futures, FX, fixed income, asset-backed securities and credit derivatives.
Its award-winning FundStudio KlarityRisk is a risk management platform which encompasses a diverse palette of investment risk analytics and methodologies, stress-testing simulation scenario analysis, compliance-targeted portfolio risk limits management, and fixed income performance attribution analysis and reporting.
Disclaimer: Nothing contained in the aforementioned references constitutes an investment solicitation or a recommendation of any type.