MScFE 560: Financial Markets
In this pilot course for the MScFE program, students are introduced to the world of professional finance: markets, products, participants, and regulation. The activities within financial markets will be discussed, including trading, financing, brokering, pricing, hedging, optimizing, and managing risk. Throughout the course, students identify a list of significant factors that affect the financial industry. Students will be able to interact with web apps that illustrate these concepts. Understanding the asset classes, activities, and influential aspects of the financial landscape will provide a solid foundation on which students will build mathematical and computational tools to develop models for financial engineering. No background in finance is required.
MScFE 600: Financial Data
This course introduces students to financial data: the source of energy for financial models. Students will learn how to apply Python to properly select, import, filter, structure, visualize, summarize, and analyze financial data for interest rates, equities, cryptocurrencies, ETFs, securitized products, and other asset classes. Students will also learn how to prepare data to be used in models for financial markets, from which decisions can be made, and how to accomplish fundamental analysis with accounting data, technical analysis with trading data, statistical analysis with transformed data, and sentiment analysis with textual data. Software engineering, visualization techniques, probability and statistics, linear algebra, and presentation skills will be developed throughout the course. The ultimate goal of this course is to build foundational skills that enable students to understand the type of data needed depending on their goals, how to source it, structure it, shape it, build with it, and discover what it tells. At their best, financial engineers turn data into empirically based, well-calibrated financial models whose output provides investors and risk managers with sound decisions in the uncertain world of finance.
MScFE 610: Financial Econometrics
This course provides a comprehensive introduction to financial econometrics. Students will learn how to model probability distributions of returns, including graphical, Bayesian, and non-parametrical methods. They will also learn how to model univariate time series, focusing on their moving average, autocorrelations, and volatilities, including GARCH models. Students will build additional tools to see how two financial series can relate to each other, using correlation, vector autoregressions, and cointegration. Further, they will build the statistical foundation and Python coding skills to run econometric models to apply in financial decision making. Finally, they will see how the ideas of bias, variance, and overfitting apply to machine learning.
MScFE 620: Derivative Pricing
Derivative Pricing is a hands-on course focused on pricing options. Students will build a conceptual background that deepens their understanding of why classical calculus is not sufficient for detecting rates of change in stochastic processes. Course content focuses on the concept of no-arbitrage and perfect replication using the world of stochastic calculus, including the Black-Scholes Model. Students will be able to construct pricing models such as binomial trees and finite difference methods to price an array of vanilla and exotic options. They will also measure sensitivities of the price to variables, such as the underlying price, volatility, time, interest rates, and carry costs. Finally, some extensions to classical models, such as the Heston Model and jump models will be addressed. Much of the course will include Python illustrations to build practical skills.
MScFE 622: Stochastic Modeling
In this course, students increase their knowledge of modeling stochastic processes. Students will investigate advanced volatility models that upgrade Black Scholes parameters to variables, increasing their stochastic modeling skills to address heteroskedasticity and variable costs as well as jump diffusions. Students will dive into Markov processes, including hidden Markov process and Markov decision process to financial applications, and will build a mathematical foundation for deep learnings, a tool they will use for machine learnings. Overall, students will be able to evaluate the assumptions, benefits, and difficulties associated with stochastic models.
MScFE 630: Computational Finance
Computational Finance is an advanced computing course that builds skills in optimization, calibration, and simulation. Student will use data to calibrate models using a variety of numerical methods, including parametric and non-parametric methods of statistical inference, linear and non-linear methods, and deterministic and stochastic programming methods. Where problems of skewness and heteroskedasticity occur, students will use techniques to handle non-normality. Students will learn how to run simulations, from classical Monte Carlo methods to Markov Chain Monte Carlo simulation, to agent-based simulations. Student will be able to calibrate the models they learned in the Derivative Pricing course through numerical, computational, and machine learning techniques. Python will be used to illustrate these models, from which students will adapt and apply to fit their own data sets. Once students have calibrated models or optimized portfolios, they will interpret the coefficients and apply the results to financial decision making.
MScFE 642: Machine Learning in Finance
This course addresses the fundamentals of machine learning. It continues the topics from the Financial Econometrics course whereby students will be able to apply algorithms to learn from data. Students will cover the mathematical and computational foundations of both the supervised and unsupervised machine learning problems, and they will use Python modules and a Tensorflow framework to predict, explain, or compare outcomes across different financial series. Students will apply machine learning techniques to determine if financial models are overfit, and use methods of regularization, cross-validation, and resampling techniques to mitigate it. In addition, students will develop a theoretical and practical background in deep learning models to improve the power of their financial model predictions.
MScFE 652: Portfolio Management
This course provides students with methodologies and skills to perform portfolio optimization. From the previous coursework, students will have a solid foundation on which to engage in the portfolio management process. In the first two modules, students will review classical methods of portfolio theory, including Markowitz portfolio optimization. Subsequent modules address more modern versions of the portfolio optimization process, including Black-Litterman, probabilistic scenario optimization, prospect theory, Kelly criterion, and risk parity. In addition, advanced econometrics and machine learning methods will be applied to the classical techniques, including the use of neural networks, genetic algorithms, information theory, and reinforcement learning. The course requires students to engage with the mathematical foundations, code implementation, and practical applications of portfolio management across many asset classes.
MScFE 660: Risk Management
This course provides students with both classical and modern methods of modeling and managing risk. The course begins by reviewing metrics and models for market, credit, and systemic risk, and applying these ideas to multiple asset classes, including derivatives. Machine learning methods will be integrated with both classical methods like VaR and GARCH and with robust methods like Extreme Value Theory. Then a comprehensive review of Bayesian methods will be given that builds towards a Bayesian network of modeling systemic risk. By taking the course, students will be able to synthesize a complex network and scenario analysis for both portfolio risk and systemic risk.
MScFE 690: Capstone Course
The Capstone Course is designed to put the students’ knowledge of financial engineering to the test. Students practically apply their understanding of the program content by accomplishing project milestones from developing a problem statement, identifying the required technology to find a solution to the problem, submitting multiple drafts for peer review and instructor feedback, and finalizing and presenting their fully developed project. The goal of the Capstone Course is to ensure that students have met the program outcomes and are able to apply their knowledge and skills to real-world scenarios.
Yes, a masters in financial engineering is worth it for many students. The financial engineering field is relatively new. Getting a MS in Financial Engineering could be a way to position yourself in a specialized niche within the financial services industry and set yourself apart from the crowd.What is the scope of Financial Engineering? ›
Financial engineers work with insurance companies, asset management firms, hedge funds, and banks. Within these companies, financial engineers work in proprietary trading, risk management, portfolio management, derivatives and options pricing, structured products, and corporate finance departments.What is MFE vs MSF? ›
In a nutshell, MSF will have a strong focus on corporate finance and financial management (three statement analysis, ratios, accounting to some degree), while MFE will mainly deal with the more complex mathematical theory that aims to understand how public markets operate (probability, statistics, pricing theory).Which engineering degree is best for finance? ›
Many hire financial engineers to help companies with risk management and portfolio management. Earning a financial engineering degree is the best way to break into quantitative finance or mathematical finance careers, and ultimately, become a financial engineer.Is Financial Engineering in demand? ›
In-Demand Skills For Many Sectors
These students begin careers with the traditional quantitative and mathematical skills, but with full knowledge of the changing needs of the global economy. These days, companies of all kinds are hiring Financial Engineers.
There is a high need for qualified financial engineers in the market. The demand for new financial engineers is particularly high in structured finance establishments. Moreover, the world of systematic, quantitative, algorithmic and automated trading offers various openings for financial engineers.Do financial engineers make a lot of money? ›
The salaries of Financial Engineers in the US range from $21,707 to $589,331 , with a median salary of $105,845 . The middle 57% of Financial Engineers makes between $105,845 and $266,649, with the top 86% making $589,331.Is Financial Engineering a good field? ›
Financial engineering provides many unique challenges and benefits to those who pursue this career path, including high-earning compensation for most jobs and integration with many potential industries.How much does a financial engineer make? ›
Avg. Base Salary (USD)
Financial Engineering pays an average salary of $5,460,103 and salaries range from a low of $4,758,411 to a high of $6,262,990.
If you're seeking a finance-specific career, pursuing a specialized degree may be a better fit. Your work experience may also lead you to choose one over the other, as an MBA is generally more suitable for current professionals while an MSF is an alternative for those who have little to no professional experience.
Both degrees can help graduates pursue career advancement opportunities. The main difference between MSFs vs. MBAs are their areas of focus. MBAs develop broad and versatile business knowledge and skills while MSFs deliver targeted education on organizational finance.Is MFE a STEM? ›
The MS in Financial Engineering program contains an interdisciplinary curriculum that includes STEM fields, which qualifies the program as a STEM designated degree.Are financial engineers called engineers? ›
Despite its name, financial engineering does not belong to any of the fields in traditional professional engineering even though many financial engineers have studied engineering beforehand and many universities offering a postgraduate degree in this field require applicants to have a background in engineering as well.Is Financial Engineering Same as finance? ›
Financial engineering is the application of mathematical methods to the solution of problems in finance. It is also known as financial mathematics, mathematical finance, and computational finance. Financial engineering draws on tools from applied mathematics, computer science, statistics, and economic theory.Why do so many engineers go into finance? ›
Most engineers point to one of the following reasons to explain their desire to work in finance: They want to make more money; they've hit a “ceiling” in their current role. They want better advancement opportunities. They want more interesting, client-facing work.Which is better finance or Financial Engineering? ›
Finance professionals, or FPs, have more options than FEs. They can work in commercial or investment banking, hedge funds, open their own financial planning firm, and more. This is because while they do not have a background in engineering, they are adept at understanding how to manage wealth.Does Financial Engineering require coding? ›
Financial engineering demands knowledge of programming theory. In today's financial world, it's not enough to understand traditional finance and math-based skills—Master in Financial Engineering grads need to be proficient in data modeling and be able to create algorithms using programming languages.What can I work with a master in financial engineer? ›
Financial Engineering graduates are ready for the international workplace in the finance and analytics industries. Financial engineers could be involved in derivatives pricing, financial regulation, corporate finance, portfolio management, risk management, trading or structured products .Do hedge funds hire financial engineers? ›
Financial engineers work in a variety of financial institutions, including hedge funds, banks, asset management firms, trading companies, and investment firms.How competitive is financial engineering? ›
The level of competition is high for entry-level quants as they need to display programming skills, knowledge of artificial intelligence methods and statistical theories. Good experience with languages like Python, Java, C++, and Scala is essential for financial engineering.
A Degree Designed for Your Career Destination
Premier investment and commercial banks, financial regulators, and stock market exchanges rely on Berkeley MFE grads as risk managers, traders, bankers, designers of specialized securities, and more.
Graduates of the program go on to executive-level positions at companies such as Prudential and Goldman Sachs, where they apply engineering techniques in creating opportunities that drive the economy forward.How long does it take to become a financial engineer? ›
Becoming a financial engineer will require finishing a four-year Bachelor of Science at minimum. Most aspiring quants major in finance, economics, statistics, mathematics, computer science, or engineering at accredited colleges. Today's employers prefer hiring financial engineers holding a graduate degree though.How do I start a career in financial engineering? ›
To pursue a career as a financial engineer, earn a bachelor's degree in a finance-related field, such as accounting, mathematics, or economics, followed by a master's degree in finance engineering or computational engineering.What is an example of Financial Engineering? ›
An example of financial engineering in practice is the work of quantitative analysts – usually referred to as “quants” – who develop things such as algorithmic or artificial intelligence trading programs that are used in the financial markets.Where can a financial engineer work? ›
- Investment Associate _ Group Venture Capital Energy / Utilities / Mobility. ...
- Project Manager. ...
- User Policy Strategist. ...
- Senior Project Manager. ...
- Head of Technology. ...
- Interdisciplinary Project Manager. ...
- Data Analytics Program Manager, VisionFund International.
|Engineering Job||Median Salary|
|1. Petroleum Engineer||$130,850|
|2. Computer Hardware Engineer||$128,170|
|3. Aerospace Engineer||$122,270|
Traditional MBAs are broader than the CFA program, covering topics such as management, marketing, and strategy. The CFA program, on the other hand, provides deeper coverage of investment management. Ultimately, the decision on which one to pursue depends on what one's career goals in finance are.Are MSF programs worth it? ›
Yes, a Masters degree in Finance is worth it for many students. The Bureau of Labor Statistics is projecting 5% job growth in business and financial occupations over the next 10 years.Do you need GMAT for MSF? ›
The MS Finance program requires either the GMAT or GRE. You may self-report the scores on your application for the admissions review process.
It is important to remember that those who have an MBA have already some experience in their field prior to enrolling in a postgraduate course. While an MBA is often looked at as being a more professional degree than an MSc, an MBA is usually quite broad apart from the electives undertaken.Is it hard to get into MSF? ›
Even if you decide this is the job for you, it won't be easy to get in. Thousands of eager, hopeful docs apply to MSF every year, but only a select few are chosen, mainly because this is an incredibly demanding job and very few have the skills, temperament, and overall ability to handle it.Is an MBA harder than a masters? ›
Both an MBA and master's in business are graduate-level programs, and meet the same rigorous academic standards. So, neither option is inherently easier than the other. The difficulty of each program also depends on the student's background.Is Masters in finance a STEM major? ›
The MFin program is STEM-designated and eligible students have the option to apply for up to 36 months of Optional Practical Training (OPT) upon graduation.Is Financial Engineering the same as actuarial science? ›
Actuarial Science is the discipline of assessing risk in industries such as insurance and finance using mathematical and statistical principles. Similarly, Financial Engineering is the science of solving problems in finance using mathematical methods.What is NYU MFE acceptance rate? ›
NYU Tandon School of Engineering has an acceptance rate of 35% making it competitive for applicants while applying for programs.Who is the father of financial engineering? ›
Merton: The First Financial Engineer.Is financial engineering a quant? ›
Financial engineers (also known as “quantitative analysts” or “quants”) are practitioners in the financial industry who are responsible for developing, testing, and improving on models, tools, and techniques that are prevalent in quantitative finance.Can engineers do MS in finance? ›
As a Masters degree in Financial Engineering, students get to understand the multidisciplinary aspect of the financial theory where the application of engineering methods, programming practice and tools of mathematics are used. The program prepares students in the professional field of finance from all over the world.Should an engineer do MBA in finance? ›
Thus, an MBA in finance would act as an asset to engineers in honing their existing skills and applying it to the financial aspects of business management – such as capital management, creation of analytical models for the purpose of risk management, portfolio or treasury management, financial planning, and more.
Examples of the types of work Engineers do at Goldman Sachs include Quantitative Strategists, Cyber Security, Software Engineering and Systems Engineering. Our quantitative strategists are at the cutting edge of our business, solving real-world problems through a variety of analytical methods.Is a Masters in financial planning worth it? ›
Benefits of an MS in Personal Financial Planning
Other benefits of earning the MS can include: An increase in earnings after completing the program. More exposure to real-world scenarios and case studies. Real-world critical thinking skills that you can immediately apply in your day-to-day occupation.
A master's degree in engineering is not always required to work in your specialization of choice, but it will certainly move your career forward by giving you a leg up in salary negotiations and by opening the door to greater job opportunities.Is Masters in Finance better than CFA? ›
While the Master's in Finance provides more general knowledge and is better suited for those who practice in more general fields in the business and financial sector, the CFA program is particularly tailored to those who need more specialized skills, such as investment analysis, portfolio strategy, and asset management ...Is an MBA better than a finance masters? ›
If your goal is to graduate with a broad awareness of management across disciplines and sectors, an MBA might be the best fit. However, if you want to gain management skills within the specific realm of finance, a masters in finance is most likely the route for you.Is it better to get an MBA or Masters in Finance? ›
If you are committed to a career in finance specifically, the master's in finance may be the right path for you. If you want to develop a broader skillset in business management and leadership, an MBA will give you all of the tools you need for jobs in a range of industries and institutions.Is it better to have an MBA or a Masters in Finance? ›
Whether an MF or MBA is better depends on your goals, financial situation, and experience. Individuals who already work in the business world and want to move up to leadership positions may be better suited for an MBA. People who want to focus on the financial industry may want to consider pursuing an MF.What is a good Masters engineering GPA? ›
A 3.5 and above is considered a great GPA for engineering students. It shows that you get mostly A's in all of your classes. Occasionally, companies will require a 3.5 GPA or above for internships.Is it better to get a masters or PHD in engineering? ›
Master's degrees prepare students for careers in industry that don't have a research focus, says Babatunde Ogunnaike, dean of the college of engineering at the University of Delaware. "If you want to work in research either in industry or in academia or for a government research lab, you need to get a Ph. D.," he says.Does engineering Masters GPA matter? ›
Understand GPA and test score requirements
Most top engineering schools require a minimum GPA of 3.0. Some schools mainly rely on the grades earned in the junior and senior years for their admissions decisions.
Financial engineering demands knowledge of programming theory. In today's financial world, it's not enough to understand traditional finance and math-based skills—Master in Financial Engineering grads need to be proficient in data modeling and be able to create algorithms using programming languages.Is financial engineering a good field? ›
Financial engineering provides many unique challenges and benefits to those who pursue this career path, including high-earning compensation for most jobs and integration with many potential industries.