Monte Carlo, Bootstrapping, and Value at Risk(var)



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tarix04.02.2018
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#24394

Monte Carlo, Bootstrapping, and Value at Risk(VAR)
Monte Carlo simulation is generally considered a procedure that generates possible outcomes by sampling from a theoretical distribution with predefined parameters. It is simply a way to determine outcomes based on 1,000's of theoretical return paths. Alternatively, bootstrapping, which can be thought of as a type of nonparametric Monte Carlo analysis, also runs thousands of simulations but takes the underlying data as given. Both of these methods can explore thousands of possible return paths, and derive confidence intervals encompassing these return paths. The major difference between the two is that Monte Carlo simulates data and bootstrapping takes the data as given and just resamples it over and over. What is advantageous about bootstrapping is that no assumption about the underlying distribution or its properties is assumed. However, bootstrapping does make the assumption that future paths will have the same basic historical return realizations that have been experienced in the past. Thus, if the historical data does not have an 80% monthly return, bootstrapping methods will not create one.
Value at Risk, See VAR.
In class exercise:

Run Monte Carlo and Bootstrapping. Replicate VAR analysis in VAR reading.
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