GS Quant allows for access to more complex market models and associated measures. These are functions which allow more intuitive slicing of various market-model based datasets. Examples of this would include:
|Implied Volatility||Historical implied volatility curve for different strikes and tenors|
|Normalized Skew||Difference in volatility between out-of-the-money and in-the-money option (Put - call ) / ATM|
|Term Structure||Forward looking term structures of volatility or forward levels at a given point in time|
Examples require an initialized GsSession and data subscription. Please refer to Sessions for details
The following example shows how to chart historical skew level for SPX:
from datetime import date from gs_quant.data import DataContext from gs_quant.markets.securities import SecurityMaster, AssetIdentifier, ExchangeCode import matplotlib.pyplot as plt import gs_quant.timeseries as ts data_ctx = DataContext(start=date(2018, 1, 1), end=date(2018, 12, 31)) # Create a data context covering 2018 spx = SecurityMaster.get_asset('SPX', AssetIdentifier.TICKER, exchange_code=ExchangeCode.NYSE) # Lookup S&P 500 Index via the Security Master with data_ctx: # Use the data context we setup skew = ts.skew(spx, '1m', ts.SkewReference.DELTA, 25) # Get 25 delta skew skew.plot(title='SPX 25 Delta Skew') plt.show() # Plot output
Should produce something like this:
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