Master of Science in Financial Engineering

Issued by WorldQuant University

WorldQuant University's accredited Master of Science in Financial Engineering degree integrates mathematical, statistical, computer science, data science, and machine learning skills. Using market-driven practitioner examples, students focus on real-world financial challenges such as securities pricing, algorithmic trading, optimizing portfolios, and hedging risks, implementing and calibrating models in Python, and using their critical thinking skills to interpret and apply them.

Type Learning
Level Advanced
Time Years
Cost Free

Additional Details

Skills

Advanced Data Analysis
Algorithmic Trading
Computational Finance
Data Visualization
Equities
Financial Derivatives Pricing
Financial Econometrics
Financial Engineering
Financial Mathematics
Financial Risk Management
Hedging
Machine Learning
Monte Carlo Simulation
Option Pricing
Portfolio Optimization
Portfolio Theory
Python
Quantitative Finance
Regression Modeling
Statistical Modeling
Stochastic Calculus
Time Series Modeling
Volatility Modeling

Earning Criteria

Earners of this badge have met all graduation requirements and have been awarded a Master of Science in Financial Engineering degree from WorldQuant University. They have earned a minimum of 39 semester credit hours, maintained a cumulative average score of 80% or above, completed the Program within the required time frame, and demonstrated that they are able to:
Cultivate the relationship between ethics and finance to promote ethical financial decision making;
Apply principles of project management towards effective collaboration;
Explain the types of financial markets, institutions, participants, and instruments;
Examine the impact central banks, regulatory agencies, FinTech companies have on market performance, volatility, liquidity, and profitability;
Design regression, econometric, stochastic, and machine-learning models for pricing, hedging, and optimizing securities and portfolios;
Catalog the types of traders based on exchange-traded vs. OTC, buy/sell side, hedging vs. speculating, holding period, level of automation, and risk taken;
Incorporate the use of derivatives in trading strategies, portfolio applications, and risk management;
Recommend strategic portfolio or risk allocations based upon interpreting financial engineering models in light of liquidity impacts;
Forecast asset values and model parameters within portfolio and risk.