Multivariate Financial Time Series Bundle

Level: Medium
Goal: Model several assets jointly (correlations, VAR, multivariate forecasting).

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

2. Model Results:

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

3. 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