According to @Habib Ahmed, there are four approaches to Managing Risks:

Having those approaches in mind, we might not want to focus on literally fight Black Swan Risks as it might be too costly and has a relatively small chance of a successful attempt. In my perspective, when it is related to financial risk, black swan risk should be avoided by avoiding a high-risk investment and staying away from speculation and toxic financial products.

I have taken a slightly different approach in my past risk management roles for companies by including black swan risk as a separate risk in the risk register. In this way the risk register contains all known risks, typically farmed from wide consultation across the organisation as well as historic industry disaster analysis. Once those 'known' risks are identified, an additional 'black swan' risk is added to the risk database with low likelihood but disastrous consequences. Black swan risk, which is by definition indescribable, becomes an extreme outlier.

All known + black swan risks can then be aggregated into a single probability distribution using Monte Carlo (article attached below).

Although this is highly subjective, it has always served as a source of useful debate at executive and board levels. That debate has then helped in allocating human resources and budget from which to manage risk in the various ways described above by Muchammad Taufiq Affandi

Article Prioritizing risks for the future using Monte Carlo simulati...

Dear Mr. Muchammad Taufiq Affandi and Mr. Christopher C Kelly thanks a lot for valuable answers!

Mr. Kelly, I find your approach excellent and very applicable. I will consider improving my own ORM report in that direction.

I would like to add a remark or two.

If we decide to take use the label Black Swan to represent an exceptional event within all other occurring and homogeneous events, it would be theoretically predictable, but not empirically demonstrable, before its actual observation. This Black Swan’s key physical characteristics are: i) being rare; and ii) having different or remarkably altered mechanisms that produce it. It distinguishes itself from the rest of the events in the distribution by having exceptional evidentiary value. We seek just enough accuracy to be able to predict, with the degree of confidence consistent with the available knowledge and information, this event. It is the event consisting of a low probability and large magnitude that is of concern under precautionary policies. If the Black Swan is an outlier it may not belong to the empirical distribution for which data are at hand. The outlier may indicate a different mechanism than that assumed: it is a sentinel of another phenomenon perhaps yet to be understood. On the other hand, a white swan outlier is a White Swan that has a specific characteristic – such as weight -- that makes it rare within the probability space of the weight of all white swans. The empirical distribution function accounts for such White Swan (e.g., a very very large white swan).

Dear Mr. Paolo F Ricci

Thank you for your remarks! They are surely an excellent contribution to this discussion.

I think these are different kinds, so called, of unknown unknowns or meta-ignorance( Kerwin, A. 1993. by Smithson in Bammer at al.2008.).It is need to pay attention on unknown unknowns. In other words, what they are, and how they may be identified, and how can we transform either of these into their unknown knowns, etc.

In 1994, I conducted research on stock market forecasting using ask analysis and its software tools-the intelligent Eidos system. I considered the stock market as a system that develops by alternating deterministic and bifurcation States. It was possible to predict the onset of bifurcation States. It was at the bifurcation points that the spread of point forecasts made from different positions over time was greatest. They also proved to have the maximum prediction error. The accuracy of the forecast is directly related to the accuracy of point forecasts. It was at the bifurcation points that sharp jumps in high-amplitude rates occurred. See figure 7.2 and 7.3 on the page: http://lc.kubagro.ru/aidos/aidos02/7.4.htm.in these processes, mechanisms similar to those that are triggered by earthquakes or when we break the ruler (the transition of quantity to quality) can play a role. Bifurcation points are the points of change in the laws themselves in the subject area, the points of violation of ergodicity. And usually we make predictions assuming that the known operating patterns will remain. In this case, it turns out that this is an erroneous assumption. That's why the outliers are unexpected. They are subject to other laws, namely the laws of the meta-level.

В 1994 году я провел исследования по прогнозированию фондового рынка с применением АСК-анализа и его программного инструментария - интеллектуальной системы Эйдос. Я рассматривал фондовый рынок как систему, которая развивается путем чередования детерминистских и бифуркационных состояний. Оказалось возможным прогнозировать наступление бифуркационных состояний. Именно в точках бифуркации разброс точечных прогнозов, сделанных с разных позиций во времени, оказался наибольшим. В них же оказалась максимальной погрешность прогнозирования. Достоверность прогнозирования прямо связана с кучностью точечных прогнозов. Именно в точках бифуркации происходили резкие скачки курсов большой амплитуды. См. рисунок 7.2 и 7.3 на странице: http://lc.kubagro.ru/aidos/aidos02/7.4.htm. В этих процессах могут играть роль механизмы, сходные с теми, какие срабатывают при землетрясениях или когда мы ломаем линейку (переход количества в качество). Точки бифуркации - это и есть точки изменения самих закономерностей в предметной области, точки нарушения эргодичности. А обычно мы делаем прогнозы в предположении, что известные действующие закономерности сохранятся. В этом случае получается, что это ошибочное предположение. Поэтому выбросы и неожиданны. Они подчиняются другим закономерностям, а именно закономерностям мета-уровня.

Many experts say that Corona virus a black swan risk and I agree with them.

Black swans are events that happen once in a long time and in time series data it will be an outlier. Whatever estimates we have will not be accurate because of the outliers which will be averaged out. Perhaps a std deviation analysis will help to take into account outliers like black swans. In scenarios analysis, a deviation of 40-50% have been built into models to take into account crisis. Predicting such events is not within our tools of analysis yet because it happens out of the blue. Most prediction used a trend analysis.