The Education of a Risk Taker

• Why the function "risk" is artificial
• The difference between risk management decision and risk "analysis"
  
• A short history of the ruin problem
• The binary vs vanilla problem
• Path dependence
  
• Why ruin and variations are different animals

Data Science without the BS

• The more complicated the less they know what they are talking about.
  
• Focus on the intuitions.

Fragility/Antifragility

• Fragility of exposures
• Fragility and path dependence
  
• Antifragility
• How to detect and measure
• Case studies
• The notion of negative (and positive) optionality
• Convexity to errors
• How people tend to blow up. And how they do it all the time.
  

Behavior and risk assessment errors

• Psychological mistakes with model error
• How people can focus on the wrong risks
• Framing
  
• Case studies
  

The inverse problem

• How to go from reality to model and how big the difference can get
  
• The notion of hidden risks
• Optimization over hidden risks

Fat tails

• What are Fat Tails? Where can we identify them?
• Why "science communication" misleads under fat tails.
  
• The Mother of All Problems: the Law of Large Numbers under Fat Tails
• Statistical norms and fattailedness. Standard deviation vs mean absolute deviation
• Correlation, scale and fat tails
• Another fat tailedness: elipticality
  
• Tempered distributions

Systemic vs nonsystemic

• Natural boundaries
  
• Risk layering
  

Squeezes and Fungibility

• Squeeze and complexity (I)
• Execution problems
  
• Path dependence and squeezability
• Fungibility problems with commodities... and others
• Pseudo-arbitrage
  

How to look at history: the "things are different" fallacy

• Case study: drawdowns over the past 200 years (Frey)
  
• Case study: violence over the past 2000 years (Taleb)

Path dependence, good for you?

• Some counterintuitive results
• Drawdown and fragility
• Distance from minimum
  

How to not be fooled by data

• What are limits of statistical methods?
•  How robustness is built.
• Higher dimensions and fat tails
• Linear regression and fat tails
  

Mediocristan vs Extremistan: a cheat sheet

• The mechanisms
  
• The boundaries.

Cyber-risks

Complexity

Precise risk methods

• How to look at the risks of an investment; how to make a risk report useful: Identifying risk sources. Alternative Extreme Betas, StressVaR, Heuristic(s)
• Portfolio selection and construction:
  
• diversification and portfolio constraints under extreme events
  
• Extreme risk budgeting
• What is wrong with traditional methods:  Markowitz, Black-Litterman, VaR, CVaR even with fat tails, Correlation (traditional), Exposures, basis risk… and how to work around them
• Risk from Models:
• Model fragility heuristic, asymmetry of model error, correlation breaks
• Uncertainty on risk model parameters (e.g. tail thickness)

Hidden optionality

• American options and hidden optionality.
• Interest rates and optionality
  

Formal approach to panics

• Squeezes, transaction costs.

Finance vs Insurance

• Insurance, law and heuristics

Extreme value theory (EVT)

• Benefits
• Tools and calibrations
• Domains where useful

Complexity Theory and Risk

• What is complexity? How does it affect risk analysis?
  
• Squeeze and complexity (II)
• Renormalization
• Cellular approaches
• The minority rule
  

Parting wisdom and some principles





 Please note that the Mini-Certificate is pitched to the level of an Executive MBA class, no more, no less.  Except that it is more fun, and deeper. And we talk about risk taking not just analysis.