Econophysics Exercises

A collection of Jupyter notebooks exploring concepts in econophysics, financial data analysis, and portfolio optimization techniques. These notebooks are exercises from the course Econophysics 2024 at the UIB (University de les Illes Balears). The course is taught by Alex Giménez Romero, Dr. Pere Colet Rafecas and Dr. María Rosa López Gonzalo. The research group is part of the IFISC (Institute of Interdisciplinary Physics and Complex Systems).

Notebooks

• 1_Getting_started_w_Python_&_financial_data.ipynb: Introduction to Python for financial data analysis, including data retrieval and basic visualization techniques.
• 2_Modelling_price_dynamics.ipynb: Implementation of stochastic processes for modelling price dynamics, including Brownian motion and Student's t-distribution models.
• 3_ARCH_&_GARCH_processes.ipynb: Implementation of Autoregressive Conditional Heteroskedasticity models for volatility forecasting.
• 4_Correlations.ipynb: Analysis of various financial correlations, including correlation matrices, asset graphs, and hierarchical clustering techniques.
• 5_Portfolio_Optimization.ipynb: Modern Portfolio Theory implementation with efficient frontier calculation and portfolio performance evaluation.
• 6_Options.ipynb: Option pricing models and derivatives analysis.
• MPT_cryptocurrencies_and_traditional_assets.ipynb: Comparative analysis of Modern Portfolio Theory applied to cryptocurrency and traditional asset markets.

Features

• Fetching and processing financial data using yfinance
• Time series analysis of financial markets
• Volatility modelling using ARCH/GARCH processes
• Portfolio optimization using CVXPY
• Efficient frontier calculation and visualization
• Risk/return analysis with metrics like Sharpe Ratio and Maximum Drawdown
• Modern Portfolio Theory implementation
• Correlation analysis between different asset classes
• Asset clustering using hierarchical methods
• Cryptocurrency integration with traditional assets for portfolio diversification
• Option pricing models

Requirements

• Python 3.x
• Libraries:
  • numpy
  • pandas
  • matplotlib
  • seaborn
  • yfinance
  • cvxpy
  • arch
  • scipy

Usage

Open the notebooks in Jupyter Notebook, JupyterLab, or Google Colab to explore the implementations and analyses.

jupyter notebook

Related Concepts

• Modern Portfolio Theory
• Efficient Frontier
• CAPM (Capital Asset Pricing Model)
• Time series analysis
• Volatility clustering
• Financial econometrics
• Risk assessment methods

License

MIT