Market Risk Modeling with Apache Spark

This is a personal data engineering project focused on building scalable workflows for Market Risk Modeling using Apache Spark.

It integrates macro-financial datasets from European sources—including stock indices, GDP components, and interest rate statistics—and processes them in Parquet format for efficient distributed analysis.

The goal is to explore Spark's capabilities for pipeline design, data cleaning, and risk factor extraction in a reproducible and modular environment.

This is a personal project to learn how to use Apache Spark for Data Engineering.

Note: A large file has been deleted from all branches and local/remote repositories: spark-4.0.1-bin-hadoop3/yarn/spark-4.0.1-yarn-shuffle.jar

This is because this project is not using the cluster system of YARN. If needed, Spark can be downloaded again in a separate folder to include this file.

How to Use:

1. Clone the repository:

   git clone <your_repository_url>

2. Navigate to the project directory:

   cd Spark_data_engineer_project

3. Run the main script:

   # Replace with your actual script name
   python main.py

4. Dependencies:
   • Make sure you have Python and the necessary libraries installed. You can install them using pip:

     pip install -r requirements.txt  # If you have a requirements.txt file

What's Next?

• Explore the existing code and modify it to suit your needs.
• Add new Spark functionalities and features.
• Consider deploying the project to a local Spark environment.

Project Scope:

This project is designed to explore Market Risk Modeling techniques using Apache Spark. It focuses on processing and analyzing macro-financial indicators relevant to the European market. All datasets are stored in Parquet format for efficient distributed computation.

Main datasets:

euro_stoxx50.parquet : Historical financial data from the EURO STOXX 50 index, used to analyze market volatility and asset behavior.

GDP_and_main_components.parquet : Cleaned data on Gross Domestic Product (GDP) and its main components (output, expenditure, and income) from European countries.

Interest_rate_statistics(2004_EU_Member_States_&_ACCBs).parquet : Long-term interest rate statistics published by the European Central Bank (ECB), covering EU Member States and Associated Central Banks since 2004.

Technical Objectives & Architecture

This project aims to build a modular and scalable data engineering pipeline for Market Risk Modeling using Apache Spark. The architecture is designed to support efficient ingestion, transformation, and analysis of macro-financial datasets relevant to the European market.

🔍 Objectives Ingest and unify heterogeneous financial datasets (market indices, macroeconomic indicators, interest rates).

Clean, normalize, and store data in columnar format (Parquet) for distributed processing.

Explore risk factor modeling techniques using Spark DataFrames and SQL.

Enable reproducible and scalable workflows for future model experimentation.

🧱 Architecture Overview

1. Data Sources

EURO STOXX 50 index: Historical market data for equity risk analysis.

GDP and main components: Macroeconomic fundamentals from European countries.

ECB interest rate statistics: Long-term interest rates by country and instrument type.

2. Processing Stages

Ingestion: Load raw CSV/Parquet files into Spark DataFrames.

Cleaning & Transformation: Handle missing values, harmonize schemas, filter relevant timeframes and countries.

Enrichment: Join datasets on time and geography to build a unified macro-financial view.

Export: Store processed datasets in Parquet format for downstream modeling.

3. Modeling Goals

Construct risk factor time series (e.g., interest rate spreads, GDP growth differentials).

Identify structural breaks or monetary policy shifts.

Prepare data for future integration with ML models (e.g., PCA, VAR, or stress testing frameworks).

4. Spark-Specific Components

SparkSession: Central entry point for all transformations.

DataFrame API: Used for all ETL operations.

Parquet I/O: Efficient storage and retrieval of large datasets.

Modular scripts: Organized by data source and processing stage for clarity and reusability.

Next Steps & Future Work

This project is a foundation for scalable Market Risk Modeling using Spark. The following steps are planned to expand its analytical depth and operational robustness:

🔄 Data Expansion Integrate additional macroeconomic indicators (e.g., inflation, unemployment, trade balance).

Include high-frequency market data for volatility modeling and intraday analysis.

🧠 Modeling Enhancements Implement factor decomposition techniques (e.g., PCA, rolling regressions).

Explore time series models for risk forecasting (e.g., VAR, GARCH).

Simulate stress scenarios based on historical policy shifts and macro shocks.

⚙️ Pipeline Optimization Modularize Spark jobs for ingestion, transformation, and export.

Add unit tests and validation checks for data integrity.

Benchmark performance across different Spark configurations.

📦 Deployment & Reproducibility Containerize the environment using Docker for consistent execution.

Prepare notebooks or scripts for cloud deployment (e.g., Databricks, EMR).

Document pipeline stages and modeling logic for reproducibility.

License

This project is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
You may view and share the code for educational or reference purposes, but you may not use, modify, or redistribute it for commercial purposes.

For full license terms, see CC BY-NC-ND 4.0