Statistical methods and models for forecasting future market movements and default probabilities of loans and forward contracts, together with economic theories and quantitative methods for investments, have empowered investors and other professionals to cope with complex investment decisions and to manage market, credit and operational risks.
This certificate program introduces mathematical and statistical models and methods in financial markets and shows how these methods can be combined with domain knowledge in finance for financial analysis and decision making.
You Will Learn
- Apply regression analysis to the Capital Asset Pricing Model and multifactor pricing models
- Use analytic tools, such as time series modeling and forecasting, to tackle rapidly changing financial markets
- Develop financial strategies based on algorithmic trading and statistical arbitrage
Who Should Apply
- Financial officers
- Venture funding professionals
- Entrepreneurs and start-up founders
- Engineers with financial responsibilities
Earning the Certificate
- Begin your certificate any academic quarter that an applicable course is offered, subject to prerequisites
- Take three courses for graduate credit and a grade
- Maintain a B average (GPA of 3.0) or better
Compared to the "Financial Risk Analysis and Management" Certificate, the "Quantitative Methods in Finance" focus is tailored more broadly toward Financial Portfolio Managers and Analysts focused on a comprehensive introduction to statistical financial modeling, time series forecasting, and quantitative trading strategies.
A background in probability, regression modeling, matrix algebra, real data analysis and investment science
Basic courses at the level of STATS 116 and STATS 200, and basic programming skills at the level of CS106
To pursue a graduate certificate you need to apply.
$10,080 - $11,880 (9 units)
to complete the certificate
For course tuition and fees, please click
Tuition & Fees
Time to Complete Certificate
1-2 years average
3 years maximum to complete
Stanford University Statistics Department
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