Topic 6 – Multivariate Financial Time Series Bundle

Dataset

• Source: Financial Time Series Datasets – Kaggle
• Link: https://www.kaggle.com/datasets/praxitelisk/financial-time-series-datasets

Download Instructions

1. Open the dataset page above.
2. Click "Download".
3. Extract to data/financial/.
4. Choose some CSVs (indices, FX, commodities).

Data Loading

import pandas as pd
import os
print(os.listdir("data/financial"))
sp500 = pd.read_csv("data/financial/sp500.csv")  # adjust
oil   = pd.read_csv("data/financial/oil.csv")    # adjust
for df_ in (sp500, oil):
df_["Date"] = pd.to_datetime(df_["Date"])
df_.set_index("Date", inplace=True)
df_.sort_index(inplace=True)
merged = sp500[["Close"]].rename(columns={"Close": "SP500"}).join(
oil[["Close"]].rename(columns={"Close": "OIL"}), how="inner"
)

Implementation Steps

1. Data Exploration

• Load multiple financial time series (e.g., stock indices, commodities, FX)
• Select 3-5 series for multivariate analysis
• Align time indices (handle different frequencies)
• Inspect data quality and missing values

2. Exploratory Data Analysis (EDA)

• Plot each series individually
• Calculate pairwise correlations
• Visualize correlation matrix (heatmap)
• Analyze co-movements and relationships
• Perform time series decomposition for each series

3. Data Preprocessing

• Align series to common time index
• Handle missing values (forward fill, interpolation)
• Calculate returns for each asset
• Test each series for stationarity
• Apply transformations (differencing, log) as needed

4. Correlation Analysis

• Calculate correlation matrix of returns
• Analyze time-varying correlations (rolling correlations)
• Identify periods of high/low correlation
• Visualize correlation dynamics over time

5. Model Building

• Univariate Models (baseline):
• ARIMA for each series individually
• Multivariate Models:
• VAR (Vector Autoregression):
• Select optimal lag order (AIC/BIC)
• Estimate VAR model
• Granger causality tests
• VECM (Vector Error Correction Model):
• Test for cointegration (Johansen test)
• If cointegrated, estimate VECM
• Dynamic Correlation Models (advanced):
• DCC-GARCH for time-varying correlations

6. Model Evaluation

• Split data temporally
• Generate multivariate forecasts
• Calculate forecast accuracy for each series
• Compare univariate vs multivariate approaches
• Visualize joint forecasts

7. Portfolio Analysis (Optional)

• Construct portfolio weights
• Analyze portfolio returns and volatility
• Compare with individual asset performance

Expected Deliverables

1. EDA Report:

• Individual series plots
• Correlation analysis and heatmaps
• Time-varying correlation plots
• Stationarity test results

1. Model Results:

• VAR model parameters
• Cointegration test results (if applicable)
• Forecast accuracy comparison
• Multivariate forecast plots
• Granger causality results

1. Code:

• Complete Python notebook
• Functions for multivariate analysis
• Visualization utilities

Tips

• Select related assets (e.g., stock indices, commodities) for meaningful relationships
• Align data frequencies (daily, weekly) before merging
• Returns are typically more stationary than prices
• VAR models require stationary series
• Test for cointegration if analyzing prices directly
• Use appropriate lag selection criteria (AIC, BIC, HQIC)
• Multivariate models can capture spillover effects between assets
• Consider economic/financial relationships when interpreting results