Volatility Timing under Low-Volatility Strategy

Objectives

This project is part of the Bloomberg API course in the Master's program in Economics & Financial Engineering at Paris Dauphine University - PSL. The aim is to replicate and analyze the methods and findings of the research article "Volatility Timing under Low-Volatility Strategy" by Poh Ling Neo and Chyng Wen Tee. This study introduces an innovative method of volatility timing under low-volatility strategy. The approach is particularly relevant for financial markets and offers new perspectives in quantitative management.

Setting up the project

Run the file install_for_windows.bat, it will install dependencies and create a virtual environment for the project.

Project Structure

Here's an overview of the project's structure:

Description

• data/: Contains datasets and intermediate data files.
• src/: Contains all the source code for the project.
  • backtester/: Code related to the backtesting of strategies.
  • base/: Core modules and utilities.
  • data/: Data loading and processing scripts.
  • performance/: Scripts for performance metrics and graphing.
  • strategies/: Implementation of various strategies.
  • utils/: Additional utility scripts and GUI components.
• static/: Contains static files such as the research paper and images.
• install_for_windows.bat: Batch file to set up the project on Windows.
• README.md: The readme file.
• requirements.txt: Contains the list of dependencies for the project.

Interface

The project includes a user-friendly interface designed using Tkinter. This interface allows users to interact with the different functionalities of the project, such as backtesting the strategies and computing metrics. Below is a description of the interface:

Finance Backtesting Tool

• Ticker: A text input field where users can enter the Bloomberg ticker symbol of the financial instrument they wish to analyze.
• Start Date: A date picker allowing users to select the start date for the analysis.
• End Date: A date picker allowing users to select the end date for the analysis.
• Rebalancing Frequency: A dropdown menu where users can choose the frequency of rebalancing (e.g., monthly).
• Risk-Free Rate Ticker: A text input field for users to enter the Bloomberg ticker symbol of the risk-free rate.
• Weights Type: A dropdown menu where users can select the type of weighting strategy (Equally Weighted, Max Diversification, Vol Scaling).
• Strategy: A dropdown menu allowing users to select the investment strategy (e.g., Volatility Timing).
• Do you have Bloomberg Access?: A checkbox for users to indicate whether they have access to Bloomberg data.
• Run Backtest: A button to start the backtest based on the selected parameters.

Results Display

After running the backtest, the results will be displayed in a table format within the interface. The metrics computed include:

• Total Return: The overall return of the strategy over the specified period.
• Annualized Return: The annualized return of the strategy.
• Annualized Volatility: The annualized volatility of the strategy.
• Monthly Volatility: The monthly volatility of the strategy.
• Daily Volatility: The daily volatility of the strategy.
• Sharpe Ratio: The Sharpe ratio of the strategy.
• Historical VaR (95%): The historical Value at Risk (VaR) at a 95% confidence level.

Each value in the table is displayed as a percentage for easy interpretation.

This intuitive interface simplifies the process of setting up and running financial backtests, making it accessible for everyone.

License

MIT

Authors

Authors: Naïm Lehbiben - Badr-Eddine El Hamzaoui