M.S in Computational Finance from Carnegie Mellon University Entry: Dates, Fees, Eligibility, Entry Requirements etc.

Term

Module Name

Description

Fall – Year 1

Probability and Statistics

Foundations in probability, estimation, hypothesis testing, and inference.

Financial Computing I (C++)

Programming in C++ for finance; numerical methods, object-oriented design.

Asset Pricing & Portfolio Management

CAPM, arbitrage pricing theory, portfolio optimization.

Stochastic Calculus

Brownian motion, Ito’s lemma, SDEs for financial modeling.

Professional Development Seminar

Resume building, networking, mock interviews, and industry panels.

Spring – Year 1

Statistical Arbitrage and Trading Strategies

Quantitative trading, mean reversion, statistical arbitrage, and backtesting techniques.

Financial Computing II (Python & ML)

Python, machine learning models, financial data handling, supervised learning.

Risk Management

Market, credit, liquidity risk; VaR, scenario analysis, regulatory frameworks.

Corporate Finance

Firm valuation, capital structure, dividend policy, financial statement analysis.

Data Science for Finance

Time series, regression, classification, PCA, and advanced analytics for financial modeling.

Summer Term

Internship

Full-time internship in finance, quant research, data science, or risk analytics.

Optional Online Electives

Optional modules such as AI in finance, deep learning, or advanced derivatives.

Fall – Year 2

Fixed Income Securities

Bond pricing, yield curve construction, term structure models, duration/convexity analysis.

Derivatives

Option pricing, Greeks, binomial/trinomial models, Black-Scholes-Merton.

Advanced Topics in Computational Finance

Emerging areas such as crypto, algorithmic trading, or advanced ML.

Elective (e.g., Credit Risk, Deep Learning)

Specialization in student’s area of interest.

Capstone Project / Practicum

Real-world finance project in collaboration with industry mentors or faculty advisors.