Fundamentals of Python for Financial Econometrics

Chapter 41: Asymmetric, Advanced, and Realized Volatility

Why can equally large negative and positive shocks have different volatility consequences, and what can intraday or OHLC data add?

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Loading notebook...Compare EGARCH, GJR-GARCH, APARCH, and GARCH-M conceptsleverage and news-impact curvesCalculate rolling, EWMA, realized, and range-based volatilityDiscuss microstructure noise and overnight information

Notebook lab guide

Related dataset

Ceteris Lab teaching sample

Estimated time

25 to 40 min

Packages

pandas, numpy

Expected output

Printed Python results that can be compared with the lesson explanation.

Learning goals

Load and inspect Ceteris Lab teaching sample.
Run the Python cells connected to Asymmetric, Advanced, and Realized Volatility.
Interpret the output using Compare EGARCH, GJR-GARCH, APARCH, and GARCH-M concepts and leverage and news-impact curves.

Common errors

File not found: check that wage_sample.csv is installed or use the course data folder.
Package import error: use the browser notebook first, then download for local Jupyter if your local packages differ.
Column name error: compare your variable names with the dataset variables listed for this notebook.

Dataset path helper

import pandas as pd

df = pd.read_csv("/data/wage_sample.csv")
df.head()

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