End-to-end quantitative finance portfolio demonstrating skills in financial modeling, risk analysis, and data-driven investment research.
Analysis of U.S. financial institutions’ stock performance using historical price data.
Includes computation of simple and log returns, portfolio construction, and risk–return evaluation.
Implementation and comparison of OLS, Ridge, Lasso, and Elastic Net regression models.
Demonstrates the impact of regularization on bias–variance tradeoffs in predictive modeling.
Project 3 – Stock Price Prediction of Bank of America Using Machine Learning and Macroeconomic Indicators
Application of multiple regression-based ML models to predict BAC stock prices.
Integrates market and macroeconomic indicators such as VIX, Treasury yields, oil, and gold.
Modeling financial market volatility using ARCH, GARCH, and EWMA frameworks.
Compares volatility persistence and clustering effects for JPMorgan Chase daily returns.
Monte-Carlo simulation for pricing a European Call Option under the Black-Scholes model. Illustrates stochastic simulation, discounted payoff estimation, and random number generation via the Box–Muller algorithm.
Extends the Monte Carlo framework to two correlated assets to price a basket call option. It demonstrates correlation modeling through Cholesky decomposition and multi-asset option valuation techniques.
Implements an explicit finite-difference scheme to solve the Black–Scholes PDE for a foreign-exchange call option. The project highlights numerical methods, grid stability, and the treatment of domestic and foreign interest rates.
Simulates the Vasicek short-rate process using Euler–Maruyama discretization to price a zero-coupon bond. It showcases stochastic interest-rate modeling and the estimation of expected discount factors via Monte Carlo simulation.
Developed a Python-based market-making simulator to model bid/offer pricing, trade execution, and cumulative nominal risk visualization. Implemented dynamic position tracking and data validation to analyze trading performance and visualize exposure evolution over time.
Allows user to build their own portfolio with both European and American options (long and short). Then analyzes the entire portfolio's risk profile in real-time.
Python-based decision assistant for Texas Hold’em using Monte Carlo simulations.
Estimates win probabilities, computes pot odds, and recommends betting actions based on risk preferences.
Project 1 - Impacts of Transport and Heating Electrification on Great Britain’s Power Demand and Market Dynamics
Research focused on the UK Energy Market and how the electrification of the transport and the residential heating in the UK will impact its energy market in the future.