https://github.com/ferrangarciarovira/var-volatility-models
Comparative analysis of Value at Risk (VaR) measures using Black-Scholes pricing under different volatility models: jump diffusion and rough volatility.
https://github.com/ferrangarciarovira/var-volatility-models
black-scholes financial-modeling jump-diffusion python quantitative-finance rough-volatility value-at-risk volatility volatility-modeling
Last synced: 4 months ago
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Comparative analysis of Value at Risk (VaR) measures using Black-Scholes pricing under different volatility models: jump diffusion and rough volatility.
- Host: GitHub
- URL: https://github.com/ferrangarciarovira/var-volatility-models
- Owner: ferrangarciarovira
- License: other
- Created: 2025-04-12T14:49:21.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-05-28T17:39:01.000Z (4 months ago)
- Last Synced: 2025-05-28T18:36:11.396Z (4 months ago)
- Topics: black-scholes, financial-modeling, jump-diffusion, python, quantitative-finance, rough-volatility, value-at-risk, volatility, volatility-modeling
- Language: Jupyter Notebook
- Homepage:
- Size: 35 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Volatility and Value at Risk: A Comparative Analysis Using Jump, SABR, and Rough Volatility Models
This repository contains the code and documentation for my Bachelor's Thesis in Economics and Advanced Quantitative Methods at Universitat Pompeu Fabra. The project focuses on estimating the Value at Risk (VaR) of financial instruments using the Black-Scholes model, while comparing how different volatility modeling approaches affect the VaR outcome.
---
## Authors
- Ferran García Rovira
- Arnau Reig Caballeria
- Miquel Muñoz García-Ramos
---
## Project Objective
The goal is to analyze the predictive power, robustness, and practical implications of estimating volatility using:
- Black Scholes
- Jump Diffusion models
- SABR model
- Rough / Fractional volatility models
We evaluate how these affect VaR estimation based on real market data.
---
## Methodology
- Theoretical derivation of VaR using the Black-Scholes framework.
- Implementation of:
- **Jump Diffusion Models (Merton and Kou)**
- **SABR Model**
- **Rough Volatility (using fractional Brownian motion)**
- Empirical estimation of volatility using Python.
- Sensitivity analysis and robustness checks of VaR predictions and model performance.
---
## Structure
```text
/data/ # Market data used for simulations
/report/ # Final thesis document and LaTeX drafts
/results/ # Visualizations and model outputs
main.py # Script to execute full VaR analysis
README.md # Project overview and instructions
```
---
## How to Run This Project
### Prerequisites
You must have **Python 3.9+** installed.
### 1. Clone the repository
```bash
git clone https://github.com/ferrangarciarovira/VaR-Volatility-Models.git
cd VaR-Volatility-Models
```
### 2. Create a virtual environment and activate it
```bash
python -m venv env
env\Scripts\activate # Windows
# source env/bin/activate # Mac/Linux
```
### 3. Install dependencies
```bash
pip install -r requirements.txt
```
### 4. Run the notebook
```bash
pip install notebook
jupyter notebook
```
---