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Bootstrap is a derivation of Monte Carlo technique introduced by Efron in 1979. It uses the re-sampling with replacement method (unlike the re-sampling with no replacement method that we used in the Lotto Number Generator example). It is a convenient tool to extract estimates (such as standard deviation and confident interval) from a non-parametric data set (a data set with no underling distribution is assumed) or estimates that do not have a closed form (cannot be expressed in an equation)..
See Examples below;
Standard Deviation and Mean
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Lotto Number Generator
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Playing Card
Probability
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Normal Distribution Random Number Generator
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Monte Carlo Integration
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Black-Scholes Option Pricing Model - European Call and Put
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Binomial Option
Pricing Model
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Portfolio Optimization
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Multiple Regression
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Bootstrap - A
Non-Parametric Approach
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Multivariate Standard Normal Probability Distribution
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Monte Carlo Simulation
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Option Greeks Based on Black-Scholes Option Pricing Model.
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