Columbia University’s M.S. in Financial Engineering prepares students for careers in quantitative finance, asset management, and financial analytics. These project ideas combine theory and computation to address problems in trading, pricing, and financial risk mitigation.
Algorithmic Trading Strategy Using Reinforcement Learning
Credit Risk Modeling Using Machine Learning on Default Data
Pricing Exotic Options Using Monte Carlo Simulation
Portfolio Optimization with Black-Litterman Model
Stress Testing Portfolios Using Historical Simulation
Volatility Forecasting Using GARCH and LSTM Hybrid Models
Value at Risk (VaR) Modeling with Time-Varying Correlation
Backtesting a Momentum-Based Crypto Investment Strategy
High-Frequency Trading Bot Using Statistical Arbitrage
Interest Rate Derivative Pricing Under Hull-White Model
Application of PCA in Yield Curve Factor Analysis
Machine Learning in Fraud Detection for Financial Transactions
Asset Allocation Model Based on Risk Parity Strategy
Implied Volatility Surface Modeling with SVI Parameterization
AI-Based Credit Scoring Using Alternative Data Sources
Derivative Pricing Using Finite Difference Methods
Copula-Based Portfolio Risk Analysis with Tail Dependence
Blockchain-Based Smart Contracts for Financial Derivatives
Climate Risk Impact Assessment on Investment Portfolios
Robo-Advisory Portfolio Construction with Dynamic Rebalancing
Collexa helps students model, price, and simulate financial instruments using Python, R, MATLAB, and QuantLib, with LaTeX-ready reporting support.
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