Value-at-Risk (VaR) Modeling
Calculate Value-at-Risk using three methods — Historical, Parametric, and Monte Carlo simulation — to quantify potential portfolio losses.
Value-at-Risk (VaR) Modeling
python • Step 2
Calculate Value-at-Risk using three methods — Historical, Parametric, and Monte Carlo simulation — to quantify potential portfolio losses.
Learning Objectives
- →Implement Historical VaR from return data
- →Calculate Parametric (Gaussian) VaR
- →Run Monte Carlo VaR simulation
- →Compare methods and understand their limitations
Dataset
View on KaggleDaily OHLCV for 29 of 30 DJIA components, 2006-01-01 → 2018-01-01.
*bleep* Load the dataset in the browser terminal:
from pyodide.http import open_url
import pandas as pd
df = pd.read_csv(open_url("/datasets/all_stocks_2006-01-01_to_2018-01-01.csv"))
print(df.shape)
print(df.columns.tolist()) Running locally? The file inside the Kaggle download is already named all_stocks_2006-01-01_to_2018-01-01.csv — drop it into the notebook's working directory and the code above runs unchanged (swap open_url(...) for "all_stocks_2006-01-01_to_2018-01-01.csv").
Dataset Columns
DateOpenHighLowCloseVolumeNameticker- Write your solution in the Try It Yourself editor below — matplotlib/seaborn figures and pandas tables render inline.
- Stuck? Expand a section and click ↑ Send to editor to stack its snippet into the editor, notebook-style.
- Hit ▶ Run (or ⌘↵) — once your output matches the expected result, the step is marked solved.
- Use ↺ Reset to restore the starter code at any time.
Try It Yourself
Historical VaR (95%): CVaR (Expected Shortfall):
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