1. Background:
Volatility, as measured by VIX, is at its lowest level since the beginning of the year. This could be the calm before the tempest and perhaps a sign of that is the newfound interest in defined outcome products.
Historically these products have been offered by banks when there is more uncertainty in the markets. In essence they give you maximum exposure to the market, up to some cap, while limiting losses to a percentage of the investment, say 10%. They decrease the inherent risk of investing in the market outright. In what follows we describe how Everysk’s API can produce a detailed risk profile of such products under various market scenarios.
An illustrative portfolio that mimics a defined outcome product is:
 Buy 100 shares of SPY
 Sell 1 SPY Call struck at 315, expiring in one year
 Buy 1 SPY Put struck at 256.5, expiring in one year
The option overlay is approximately selffinancing and the outlay of cash is not different than buying 100 shares of SPY. In one year, the maximum loss is 10% (from a SPY spot of 285) and maximum upside is roughly 10% as well. Before expiration the product can exhibit different payouts.
Practitioners have traditionally used a pricing formula to produce a localized “stress test” of the product. In the illustrative example above, both options would be repriced with a spot that is 10% lower using Black and Scholes or another model. That approach would generate a single profit and loss (PL) number with 100% certainty. Instead, Everysk generates full distributions of outcomes for each scenario by propagating the SPY shock to other risk factors. For example, when SP500 is falling, volatility is increasing, rates are dropping and so forth. We do this by simulating all risk factors (including macro shocks) and conditioning the simulations on the shock. The technical details are beyond the scope of this report and can be found in our white paper, chapter 4.
A system that merely shocks the inputs from a pricing formula cannot infer those subtle correlations. Also when an exogenous macro shock such as oil is considered, these systems are at a loss, as oil is not part of the pricing formula.
In what follows we describe some results using Everysk API:
2. Stress Tests:
First we stress test the portfolio when options still have a full year to expiration:
The horizontal axis shows SP500 shocks ranging from 10% to 10%. The vertical axis shows the expected gain/loss in the portfolio. The black line represents the expectation and red lines represent the 5percentile best/worst outcomes for each shock (unavailable from pricing formulas which provide a single PL per shock).
For a simple portfolio comprised of SPY and options on SPY, the black line with expected PLs in the plot above is very close to the Black and Scholes solution, as follows:

Contracts 
Price Before 
Price After(*) 
Black and Scholes PL (%)  Everysk PL (%) 
SPY 
100 
285.46 
256.90 
10.00  10.00 
Call 
1 
8.58 
2.28 
1.97  2.20 
Put 
1 
8.42 
17.47  3.50  3.17 
Total 

4.53  4.63 
(*) After 10% drop in SP500 / (**) Percent PL as of NAV of $28,546
The difference in PL from our calculation engine and Black and Scholes is that we automatically take into account the propagation of risk when SPY drops 10%. An example of propagation is shown below:
SP500 
VIX 
Treasuries 5yr 
BB Spread 
Oil 
10% 
164% 
14 bps 
+27 bps 
3.88% 
+10% 
35% 
+11 bps 
23 bps 
+3.84% 
Our calculation engine simulates both normal and lognormal risk factors. A BB spread index (not used in the options example) that is currently 2.24%, is expected to widen to 2.51% when SP500 drops 10% (=2.24% + 0.27%). Conversely a VIX of 10.85% is expected to jump to 28.64% (=10.85% * (1+1.64)) from the same 10% SP shock.
If we roll down the portfolio to 3 months to expiration, the stress test profiles become:
The 5percentile PLs are now bounded by the options intrinsic values: downside and upside are close to 10% and +10%, respectively, despite an expected PL that is still closer to 6% and +6% on those extreme shocks.
3. Summary:
We demonstrated in this short report how Everysk can reliably propagate macro shocks within a multiasset portfolio containing options.
We shocked SP500 and provided an intuition on how our model differs from a localized approach that stress tests options via their pricing formulas. We contend our methodology is more relevant to buy side investors interested in big picture effects.
Additionally our methodology generalizes to shocks on any exogenous index, even when the shock does not affect directly the options via their pricing formula.
As we write this report, the first ETFs to offer defined outcome payouts are being launched in the market. Our API should benefit sellers/buyers of such products.