Stress test measures the behavior of the portfolio under various extreme market scenarios. For example, if the price of oil drops, a portfolio with more allocations to oil dependent companies will be more affected than portfolios with less energy exposure. This dependency to oil prices can be measured many ways: the simplest is to perform a historical regression of returns between each security against oil and infer the portfolio sensitivity to oil, or historical beta. For the sake of argument, let’s assume that Beta is 0.2, i.e. a 1% move on oil prices (up or down) should result in a 0.2% move (up or down) in the portfolio. Practitioners tend to extrapolate that information to any shock magnitude: they infer that a -10% oil move should result on a -2% portfolio move and so on.
We don’t use this simplistic approach for many reasons:
- For one, it is predicated solely on historical information.
- Also, it assumes that on extreme scenarios the relationship between the portfolio and oil will be preserved: we know that the 0.2 relationship will not be constant and correlations change during more extreme scenarios.
- When portfolio contains maturing securities (options, bonds, futures, etc), regressing their historical prices with oil (in our example) will generate unreliable betas as their risk properties will change as they get closer to the expiration/maturity.
- And finally it assumes a linear relationship between all securities and oil, introducing serious errors when options are present in the portfolio.
Our approach is more akin to a statistician observing large quantities of data. It calculates the probabilities of extreme events happening and factors those probabilities into the calculations. For example: it knows that correlations and volatility increase on periods of large oil declines (as an example), resulting on large price oscillations for securities.
The Stress Test widget has 2 main components:
- A dynamic graph with grey bars (in the example they represent oil shocks).
- A dynamic graph with red/blue bars (which appears when you move the mouse over the grey bars).
Let’s describe each component separately:
1. Portfolio impact under shocks (grey bars):
The grey bars represent the expected PL of the portfolio for different shocks of an exogenous factor (oil in the examples contained in this document), within a range that is automatically calculated by Everysk. In certain templates, a specific grey bar might already be selected to highlight if an upside or downside shock is the most relevant for that template. By clicking on that bar, you will unlock the highlight.
As you move the mouse sideways over grey bars, the graph on the right will change accordingly. The individual contributions on the right graph are sorted from largest expected PL (on top) to lowest (bottom) for the specific shock that is active on the left.
If you want to zoom in a specific shock, click over that grey bar and move the mouse to the right graph to see the individual properties (CVaR-,Expected,CVaR+). Summing all the individual properties will result in the portfolio overall properties. In order to unlock the grey bar, select it again. The 3 properties are explained below:
- CVaR- : The average of the worst 5-percentile from the forward looking PL distribution of the portfolio.
- Expected: The average of the whole forward looking PL distribution of the portfolio.
- CVaR+ : The average of the best 5-percentile from the forward looking PL distribution of the portfolio.
Move your mouse over the rightmost grey bar above (oil up 8.5%): on that scenario the illustrative portfolio is expected to be up 2.12% (with 5% “tail” expectations of -4.03% and +8.73%). On the right side, we can see that Halliburton, Shell and Exxon benefit the most (in that order).
2. Individual Contributions to tail behavior (red/blue bars):
This graph is connected to the left graph. It represents the contributions from positions (or another aggregation such as sectors) to portfolio tail properties, per shock. The sorting is by the Expected attribute.
When the number of securities in the portfolio exceeds 10, only the top and bottom 5 names are shown.
The following simple example illustrates how to interpret these bars: a portfolio with 100 shares of Apple and Facebook, hedged by shorting 100 shares of SPY is depicted below on a market down scenario (orange highlight):
Both APPL and FB are contributing to the downside of the portfolio (red bars). Also, despite market falling, these positions could still contribute to the right tail of the portfolio, but with small magnitudes.
Conversely, the short SPY has a more defined behavior on markets falling. It will add positive contribution to the whole portfolio distribution (both bars are blue)
Users can determine in a visual way, which security is adding more left tail risk under which shock, as an example. Another important piece of information derived from this widget is the amount of asymmetric behavior in the portfolio. For a symmetric shock on any factor, we can compare the leftmost and rightmost grey bars. In general, the sizes will be similar but sometimes, specially in portfolios with options, they will be quite different indicating asymmetric responses to symmetric shocks.