Do you have the tools to gain valuable insights for navigating worst losses and abnormal returns?
Portfolio strategies often account for the “normal” nature of expected return even when there is empirical evidence of a major market crisis on a global scale (every 3 to 4 years for the last two decades). To determine whether or not it makes sense to spend money on reducing fat-tailed statistical return distribution, which represents the greater likelihood of a major event occurrence, a fund manager needs the appropriate tools to analyze the distribution in a specific time dimension (past, present and future).
Past: What does a portfolio’s worst returns in the left tail look like if risk factors (e.g. price, interest rates, credit spreads, volatility, etc.) were to behave in a similar fashion as they did in historical events of severe market shocks? How does a fund manager gain insight on realized idiosyncratic returns of a particular sector, industry or single name in isolation by examining the simulated portfolio returns for any given historical period?
Present: How do the realized (ex-post) returns and expected (ex-ante) portfolio risk parameters (exposure, sensitivities, VAR and especially expected shortfall, which is a measure of tail risk), change after an instantaneous portfolio rebalancing and capital allocation decision that reflects a portfolio manager’s modified view of expected returns?
Future: How does a fund manager simulate fatter tails for a given instrument type (where risk factors do not follow lognormal distribution) to derive an ex-ante measure of expected shortfall?
The Gravitas risk solution provides distinct and robust tools to cover past, present and future analysis, drilling down to position level and producing insights into left tail loss drivers for risk managers and right tail return drivers for portfolio managers.
Past: Simulate historical return for any custom time interval in the last ten year period for all asset classes and instrument types; Access to custom configured historical shock scenarios for all major global macro events of last three decades; Correlate worst losses to macro events and/or single name specific event.
Present: Calculate changes in realized returns and expected risk parameters from any portfolio rebalancing with an intraday risk calculation module. (This provides a realistic idea of “past” performance and expected risk of “future” performance, as described below.)
Future: Simulate fatter tails using two different methods (mixture of normal and normal rank correlation) to forecast both skewness and kurtosis, which are observed in empirical distributions of asset returns that cannot be captured by normal distribution modeling. (This provides a projected estimate of risk that is more consistent with less probable extreme market shifts.)