To solve the problem, I will split it into three parts as shown in the image and write Python code accordingly.

Part 1: Load the dataset and create the stock_data DataFrame.

We will read in the CSV file, ignoring all columns except bid_price_close and time.

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import pandas as pd

# Load the CSV file (assuming it's in the correct directory).
file_path = '/mnt/data/your_file.csv'  # Adjust with the correct file path name.
stock_data = pd.read_csv(file_path)

# Keep only 'bid_price_close' and 'time' columns.
stock_data = stock_data[['time', 'bid_price_close']]

# View the first few rows to verify the DataFrame
stock_data.head()

Part 2: Calculate percent returns and scale them by 100.

We will compute the daily percent return using bid_price_close and store them as a pandas Series.

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# Calculate percent returns and scale by 100
one_day_returns = stock_data['bid_price_close'].pct_change() * 100

# Convert to pandas Series
one_day_returns = pd.Series(one_day_returns, name='one_day_returns')

# View the first few rows of the percent returns
one_day_returns.head()

Part 3: Assign posterior hyperparameters and create an Inverse Gamma distribution.

For this part, I will use the scipy.stats.invgamma distribution for your posterior.

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from scipy.stats import invgamma

# Assign hyperparameters for the Inverse Gamma distribution
posterior_a = 2.0  # Example value, replace with the correct one
posterior_b = 1.0  # Example value, replace with the correct one

# Create an Inverse Gamma distribution with the given posterior hyperparameters
posterior = invgamma(a=posterior_a, scale=posterior_b)

# Example: Generate some samples from the posterior distribution
samples = posterior.rvs(size=1000)

# Show first 5 samples
samples[:5]

Now that the code is structured into parts, you can adapt it by loading the actual dataset (I will check if there is a file uploaded).

Let me know if you would like further details or if you have any questions!

Here are 5 related questions:

1. How can you visualize the distribution of daily returns?
2. How can we validate if the Inverse Gamma distribution fits our data?
3. What alternative distributions might be suitable for stock returns modeling?
4. How would you perform maximum likelihood estimation on the posterior distribution?
5. How do stock return distributions impact portfolio risk assessment?

Tip: When using the pct_change method, be mindful of data with potential zero values to avoid division errors!