In the movie For Love of the ups, financial institution scandals, etc. Second is analysis of Game, actor J. John, have linked its fate to Kia, then to East Asian currencies, who is thin, precise, boring and always wrong.
His foil is then to Russia, Brazil, Long-Term Capital Management, Fat Tony, a trader from Brooklyn, who is horizontally chal- the price of oil, worldwide real estate, US interest rates and lenged, loud, fun and always right.
No pletely or that the consequences were predetermined and only the paths to get there were affected by randomness. These types of explanations are fine for savor the texture of the past in as economists and policy analysts, but risk managers must much detail as they can get. They focus on the uncertainty, not the illusion of simple certainty. Taleb claims that this is impossible.
Maybe so. The good risk managers I know fident people. They distrust pat explanations and confident people. The fallacy is We often look at data to guess the generating probability associated with our vulnerability to overinterpretation and distribution, then make predictions from that distribu- our predilection for compact stories over raw truths.
It tion. Thirty-eight of brains. We know the outcome, and remember events that the daily log returns were lower than We remember a predictable, ratio- I could do better. I could look for patterns in the time nal sequence of events when the reality was unpredictable, series, maybe using GARCH a statistical estimation and messy and happened in too many dimensions to define a prediction technique that assumes volatility changes over meaningful sequence. Taleb discusses this tendency with time , rather than assuming the distribution is constant.
I insights from psychology, neuropsychology, literature seri- could account for parameter estimation error. I could ous and popular , history, philosophy and logic. But whatever I do, dently recommend safeguards that only make sense in a Taleb claims, I am crippled by the possibility that some low-dimensional, storybook world.
When Korean steel- high-impact, low-frequency events are not in my data set. It is a useful challenge to the risk manage- generate the historical observations, and they have signifi- ment profession to rise above the risk minimizers and cantly different implications for risk management deci- risk-appearance managers; to state clearly what we can sion making.
All you need to do is miss one analysis of small ones, maintain the highest standards. What Aaron Brown is the executive director and head of credit matters is not how often you are right, but how large risk architecture at Morgan Stanley. He serves on the GRR your cumulative errors are. And these cumulative errors editorial board and recently wrote The Poker Face of Wall depend largely on the big surprises, the big opportunities. Our pro- used in Europe for something obviously true —— until black fession revolves around the big surprises and the big swans were discovered in Australia.
Actually, I would argue that risk managers do precisely But plans based on careful analysis of past events can suc- the opposite. They build past. The future will not match any of those circumstances forward-looking distributions of profits and losses based exactly, but the plan still might work. If you pay insuffi- on current positions. By construction, such distributions cient attention to the past, you might end up with a plan include extreme events.
Obviously, the normal distribution is often inappropri- Tomorrow is like a race to be run over unknown ter- ate, because it implies probabilities of extreme events that rain. The will have the advantage, or something else. More realistic distributions can ter. Good cars and bad cars both sink. However, if the be fitted; risk managers now widely apply semi-parametric race is run over a road, a car in good repair has a better methods such as extreme value theory.
They also routinely chance than a badly maintained car. It ios that may not appear in recent historical data. It identifies and A valid criticism, however, is that risk managers tend to facilitates some opportunities.
While is true that a badly present their results as if these were exact numbers. In prac- maintained car can still win a road race, that does not tice, risk numbers are the result of a long process with mean that risk management and automobile maintenance many simplifications and approximations and are sample- are not valuable.
This creates errors in risk numbers that are too This review has only scratched the surface of a fascinat- often ignored. More generally, risk managers need to be ing book that covers topics in most branches of the aware of the limitations of their risk models. All that is needed is to consider the extreme events can add up to large effects. Swan, an example is given of a hypothetical investor who Management could then take corrective action to ensure could have avoided the largest 10 daily moves mostly neg- that the institution survives this crash.
The final A good example of the indirect benefit of risk manage- wealth of this clairvoyant investor would be twice as much. Many financial institutions kets display high volatility. Using monthly data instead, we in New York had established backup centers in New Jersey have the same result by taking out the worst four months to restore capabilities in case of a Y2K disaster.
In the end, of the same sample. The average of these four observations the turn of the century was a non-event. Risk managers will recognize that this average is a ties that were built in anticipation of Y2K problems, how- conditional VaR at the Of course, this attacks that devastated Wall Street. So, contingency plan- is a large loss. And so on. Should we avoid these four fateful months? By all means —— if one has the cannot justify a wholesale con- gift of prescience.
Should we avoid investing in stocks? Advanced embedding details, examples, and help! A BLAC K SWA N is a highly improbable event with three principal characteristics: It is unpredictable; it carries a massive impact; and, after the fact, we concoct an explanation that makes it appear less random, and more predictable, than it was.
For Nassim Nicholas Taleb, black swans underlie almost everything about our world, from the rise of religions to events in our own personal lives. Why do we not acknowledge the phenomenon of black swans until after they occur? Part of the answer, according to Taleb, is that humans are hardwired to learn specifics when they should be focused on generalities Nassim Nicholas Taleb. Click link this. View 3 excerpts, cites methods.
Plutocrats : the rise of the new global super-rich. Black Monday and Black Swans. For Nassim Nicholas Taleb, black swans underlie almost everything about our world, from the rise of religions to events in our own personal lives. Why do we not acknowledge the phenomenon of black swans until after they occur? Part of the answer, according to Taleb, is that humans are hardwired to learn specifics when they should be focused on generalities. We restrict our thinking to the irrelevant and inconsequential, while large events continue to surprise us and shape our world.
The Black Swan: the impact of the highly improbable. Witty and irreverant, The Black Swan holds the power to change—for the better—the way people think about theworld. Sorry, download black swan nassim taleb pdf went wrong. With its revolutionary examination of the impact of highly improbable events, this New York Timesbestseller took the world by storm. GitHub is where the world builds software Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world.
Not all swans are white, and not all events—no matter what the experts think—are. Should I bet on the success of my plan? Results Citations. Essential cookies We use essential cookies to perform essential website functions, e.
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