This badge was issued to Michael Olalekan Olafusi on 31 May 2021.
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
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
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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:
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Cultivate the relationship between ethics and finance to promote ethical financial decision making;
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Apply principles of project management towards effective collaboration;
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Explain the types of financial markets, institutions, participants, and instruments;
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Examine the impact central banks, regulatory agencies, FinTech companies have on market performance, volatility, liquidity, and profitability;
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Design regression, econometric, stochastic, and machine-learning models for pricing, hedging, and optimizing securities and portfolios;
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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;
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Incorporate the use of derivatives in trading strategies, portfolio applications, and risk management;
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Recommend strategic portfolio or risk allocations based upon interpreting financial engineering models in light of liquidity impacts;
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Forecast asset values and model parameters within portfolio and risk.