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Monte Carlo simulations to calculate expectations, variances, and probabilities of financial derivatives (European Call Options). Visualized convergence and Vectorized using Numpy.

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TimothyW553/Monte-Carlo-Options-Pricing

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Monte Carlo Simulation for Options Pricing

Background - What is a Monte Carlo Simulation?

They are a way of solving probabilistic problems through simulating many scenarios of the given problem. Helpful for finding expected value, variance, and other numerical summaries for complex problems. Learn more here: https://en.wikipedia.org/wiki/Monte_Carlo_method

For this project, we are simulating Financial Derivatives. Why? Options are complex and hard to simulate using analytical formulas. This is usually done with risk-neutral pricing (since it's easier to calculate)

Market Value vs Theoretical Value for MSFT

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Market Value vs Theoretical Value for AAPL

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Ways to improve accuracy

  1. Variance reduction methods:
    • Antithetic variates
    • Control variates
  2. Quasi-random variates compared to pseudo random numbers

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Monte Carlo simulations to calculate expectations, variances, and probabilities of financial derivatives (European Call Options). Visualized convergence and Vectorized using Numpy.

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