by Mehdi Zallaghi
This project focuses on applying quantitative finance techniques to address challenges in financial risk management. The goal is to use advanced statistical methods, optimization techniques, and machine learning algorithms to assess and mitigate risks in financial portfolios.
The primary areas of focus in this project are:
Risk Measurement:
Statistical methods, such as value-at-risk (VaR), conditional VaR (CVaR), and stress testing, are used to evaluate the risks associated with a financial portfolio.
Portfolio Optimization:
Developing optimization models to balance risk and return and applying techniques such as variance optimization and Reinforcement Learning models.
Deep Learning for Risk Prediction:
Employing deep learning algorithms for forecasting financial risks, returns, and enhancing asset allocation within the portfolio.
1. Risk Measurement
Risk is an essential part of finance, and accurate measurement is critical for successful risk management. In this project, I have implemented several risk measurement techniques, including:
Value-at-Risk (VaR): A statistical technique used to measure the potential loss in value of a portfolio under normal market conditions over a specified time period and at a given confidence interval.
Conditional Value-at-Risk (CVaR): An extension of VaR, CVaR measures the expected loss given that the loss is beyond the VaR threshold, capturing the tail risk of the portfolio.
Stress Testing: Simulates the effects of extreme market events on a portfolio to evaluate its resilience under adverse conditions.
2. Portfolio Optimization
The goal of portfolio optimization is to find the best asset allocation that maximizes returns for a given level of risk. The project includes the following techniques:
Mean-Variance Optimization (MVO): The classic approach for portfolio optimization that balances expected returns and portfolio risk (variance).
Reinforcement Learning (RL): The modern methods for portfolio management
3. Deep Learning for Risk Prediction
Machine learning is used to predict asset prices and volatility, enabling more dynamic and adaptive risk management strategies. Key techniques include:
Supervised Learning: Training deep learning models (e.g., LSTMs ) to predict asset returns based on historical data and other financial indicators.
Reinforcement Learning: Developing strategies for portfolio optimization by training agents to maximize portfolio performance over time using reinforcement learning techniques.
yfinance:for download market data from Yahoo!
Scikit-learn and PyTorch: For deep learning models and tools.