Universal Principal of Risk Management: Pooling and Hedging of Risk

Pulling and Hedging of Risk

Probability theory
First found in the 17th Century
$$P=Prob. 0≤P≤1$$

Independence theory
each event is independent from the next, an example of this would be throwing a coin in the air, every time you throw each event is independent from the other to calculate this probability

$$Prob (A and B) = Prob (A).(B)$$

Binomial Distribution:
The binomial distribution is the discrete probability distribution of the number of successes in a sequence of n independent yes/no experiments, each of which yields success with probability p. Such a success/failure experiment is also called a Bernoulli experiment or Bernoulli trial; when n = 1, the binomial distribution is a Bernoulli distribution. The Binomial distribution is an n times repeated Bernoulli trial. The binomial distribution is the basis for the popular binomial test of statistical significance. eg. x events in n tries:

Population measures

Random Variable = E(x)=Mx=

 Random Variable Sample
The Geographic Average is should be used to calculate a performance of an investor.

Variance and Covariance

 

 

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