Professional options traders don’t take directional bets.
They spend their time finding mispricings.
Usually, they start with GARCH.
Here’s how you can too:
They spend their time finding mispricings.
Usually, they start with GARCH.
Here’s how you can too:
1/ Get data
Use yfinance to download data and compute the returns.
Use yfinance to download data and compute the returns.
2/ Fit a GARCH model
Use the excellent arch package to fit a GARCH model.
Use the excellent arch package to fit a GARCH model.
3/ Forecast volatility
Get the variance forecast, take the square root, and annualize.
Get the variance forecast, take the square root, and annualize.
4/ Find mispricings
Use your forecast in the pricing model.
Compare the value to the market.
If your forecast shows a lower price, buy.
Higher price, sell.
Use your forecast in the pricing model.
Compare the value to the market.
If your forecast shows a lower price, buy.
Higher price, sell.
I break down the details in a recent newsletter.
• Data
• GARCH
• Mipricings
And more…
Read it here, for free:
pyquantnews.com
• Data
• GARCH
• Mipricings
And more…
Read it here, for free:
pyquantnews.com
Getting Started With Python for Quant Finance.
Go from complete beginner to up and running with Python for quant finance in 30 days.
• Community
• Frameworks
• Live sessions
• Special guests
• Jupyter Notebooks
Starts 13 November - only 50 spots.
gettingstartedwithpythonforquantfinance.com
Go from complete beginner to up and running with Python for quant finance in 30 days.
• Community
• Frameworks
• Live sessions
• Special guests
• Jupyter Notebooks
Starts 13 November - only 50 spots.
gettingstartedwithpythonforquantfinance.com
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