Challenge: Simulate Random Portfolios
Monte Carlo simulation is a powerful tool for exploring the possible outcomes of investment portfolios. In portfolio optimization, it is often used to randomly generate many different combinations of asset weights, calculate their expected returns and risks, and analyze which combinations might offer the best trade-off between risk and reward. By simulating a large number of random portfolios, you can visualize the range of achievable returns and risks, and identify efficient portfolios even before using more advanced optimization techniques.
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Write a function that simulates 100 random portfolios using hardcoded expected returns for three assets. For each portfolio, randomly assign weights to the assets so that the sum of weights equals 1. Calculate the expected return and standard deviation for each portfolio. Store the weights, expected return, and standard deviation for each portfolio in a dictionary, and collect all portfolios in a list. Return the complete list of portfolio dictionaries.
- Generate 100 portfolios with random weights for three assets, ensuring each set of weights sums to 1.
- Calculate the expected return for each portfolio using the provided asset returns.
- Calculate the standard deviation for each portfolio using provided asset standard deviations.
- Store each portfolio's weights, expected return, and standard deviation in a dictionary.
- Return a list containing all portfolio dictionaries.
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Can you explain how to set up a Monte Carlo simulation for a portfolio?
What are the main advantages of using Monte Carlo simulation in portfolio optimization?
Can you show an example of how the results from a Monte Carlo simulation are interpreted?
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Monte Carlo simulation is a powerful tool for exploring the possible outcomes of investment portfolios. In portfolio optimization, it is often used to randomly generate many different combinations of asset weights, calculate their expected returns and risks, and analyze which combinations might offer the best trade-off between risk and reward. By simulating a large number of random portfolios, you can visualize the range of achievable returns and risks, and identify efficient portfolios even before using more advanced optimization techniques.
Swipe to start coding
Write a function that simulates 100 random portfolios using hardcoded expected returns for three assets. For each portfolio, randomly assign weights to the assets so that the sum of weights equals 1. Calculate the expected return and standard deviation for each portfolio. Store the weights, expected return, and standard deviation for each portfolio in a dictionary, and collect all portfolios in a list. Return the complete list of portfolio dictionaries.
- Generate 100 portfolios with random weights for three assets, ensuring each set of weights sums to 1.
- Calculate the expected return for each portfolio using the provided asset returns.
- Calculate the standard deviation for each portfolio using provided asset standard deviations.
- Store each portfolio's weights, expected return, and standard deviation in a dictionary.
- Return a list containing all portfolio dictionaries.
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