Columbia University’s Financial Engineering program equips students with analytical tools to solve problems in trading, asset management, and risk modeling. These projects focus on quantitative methods, algorithmic thinking, and simulation to deliver insights into financial decision-making.
Monte Carlo Simulation for Derivative Pricing
Portfolio Optimization Using Modern Portfolio Theory
Machine Learning Algorithms for Stock Price Prediction
Black-Scholes Model Application in Option Valuation
Credit Risk Assessment Using Logistic Regression
High-Frequency Trading Strategy Backtesting with Python
VaR (Value-at-Risk) Modeling Using Historical Simulation
Arbitrage Detection Using Statistical Analysis
Design of a Crypto Portfolio Management Dashboard
Quantitative Trading Bot Using Moving Average Strategies
Stochastic Differential Equations in Interest Rate Modeling
Sentiment Analysis of Financial News for Market Forecasting
Yield Curve Construction from Market Bond Data
Stress Testing a Financial Portfolio Under Crisis Conditions
Implementation of GARCH Models for Volatility Forecasting
Option Greeks Sensitivity Analysis Using Excel/VBA
Comparative Study of Fundamental vs. Technical Analysis
Reinforcement Learning for Automated Asset Allocation
Design of a Multi-Asset Risk Monitoring System
Pricing Exotic Options Using Binomial Tree Models
Collexa supports financial engineering students with Python-based financial modeling, backtesting frameworks, Excel automation, and LaTeX-based documentation.
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