An Introduction to Financial Engineering
Financial engineering became popular in the late 1970s as quantitative-minded traders began using mathematical techniques to have an edge in the markets.
Its popularity grew in response to the financial crisis of 2007-2008.
Financial engineering refers to the application of quantitative methods, especially those from the fields of mathematics, statistics, and computer science, to solve problems in finance.
It combines financial theory, mathematics, engineering methods, and programming skills to make decisions and design financial strategies.
Key Takeaways – An Introduction to Financial Engineering
Applications of Financial Engineering
Derivative Pricing and Risk Management
Financial engineers develop models to price derivatives (such as options, futures, and swaps) and assess their risk.
This involves understanding and modeling the behavior of underlying assets – as well as the many factors that affect the price of these financial instruments.
Portfolio Construction and Management
Using mathematical and statistical methods to optimize portfolios, financial engineering involves the construction of trading/investment portfolios to maximize returns while controlling risk.
Most notably it takes into account correlations and volatilities of different assets.
Financial engineering also considers leverage as a tool for risk balancing and risk engineering.
Algorithmic and High-Frequency Trading
Developing algorithms for automated trading strategies.
Financial engineers use a combination of historical data, stress testing, and statistical models to predict market movements and execute trades at high speeds.
Structured Products
Designing and valuing complex financial products that are tailored to specific investment strategies or risk management needs.
These can include products with various combinations of equities, bonds, currencies, and derivatives.
Risk Management Models
Creating models to identify, assess, and manage financial risks.
This includes market risk, credit risk, liquidity risk, and operational risk.
Financial engineers develop and use models like Value at Risk (VaR) and stress testing to quantify risks.
Quantitative Analysis
Applying advanced mathematical and statistical techniques to analyze financial markets and securities.
This involves the use of:
- probability
- stochastic processes
- differential equations
- econometric models, and more
Machine Learning and Data Analysis
Using machine learning algorithms and big data analytics to uncover trends, predict market movements, and make trading decisions.
This is a growing area within financial engineering.
It leverages computational power to process large volumes of data.
Asset-Liability Management
Managing financial risks arising from the mismatches in the assets and liabilities (debts and obligations) of an institution, which is most relevant in the banking and insurance sectors.
Corporate Finance Applications
Applying financial engineering in corporate settings to manage corporate risks, optimize capital structure, manage cash flows, and for strategic planning.
Regulatory and Compliance Solutions
Developing models and strategies to ensure compliance with financial regulations.
Financial engineers work on modeling for regulatory requirements like Basel III, Solvency II, and Dodd-Frank.
Credit Engineering and Structured Finance
Involves developing products and strategies related to credit, such as credit derivatives, collateralized debt obligations (CDOs), and mortgage-backed securities (MBS).
Fintech Innovations
Within financial technology (fintech), financial engineers are involved in projects like blockchain and cryptocurrency technologies, digital banking, and peer-to-peer lending platforms.
Financial Engineering Challenges
One of the biggest advantages of financial engineering is that it can help reduce risk.
By using mathematical models, financial engineers can identify potential risks before they become a problem.
They can also create financial products that are less likely to experience problems in times of market volatility.
However, a common problem in quantitative finance is the overreliance on mathematical models.
When models are based on data that was representative of the past and the future is different from the past, this is when these mathematical models will lose their value and can even be dangerous to rely on.
Financial Engineering as a Discipline
Financial engineering is a relatively new field and there is no one clear definition of it.
Some people view financial engineering as a branch of financial mathematics, while others see it as a separate discipline altogether.
One common trait that all financial engineers share is their quantitative skills – they are able to use mathematical models to analyze financial data.
Anyone who has a technical background could be considered a financial engineer, from a computer programmer or a statistician who work in finance.
Some consider a financial engineer as someone who is trained in the various tools of modern finance and has a background in financial theory.
Some would say it more tightly applies as a term for those who design financial trading strategies or original financial products.
Criticisms of Financial Engineering
Financial engineering has been popularly criticized for over-relying on flawed models and for creating financial products that are too complex and difficult to understand.
In some cases, financial engineers have created products that are so complex that they are impossible to realistically value or even trade.
Another criticism of financial engineering is that it can lead to excessive risk-taking.
By relying too much on mathematical models, financial engineers can sometimes create products that are not as safe as they seem.
Financial engineering has also been criticized for its role in the financial crisis of 2007-2008, with the use of derivatives.
Some people believe that financial engineering played a major role in the crisis and that the use of complex financial products was one of the main reasons for the crash.
For example, in the housing market the process might work as follows:
1. A bank lends money to someone buying a home.
2. The bank then sells the mortgage to a government agency (i.e., Fannie Mae in the US). This provides the bank with more funds to make new loans.
3. Fannie Mae will resell the mortgage as part of a group of other mortgages – called a mortgage-backed security, or MBS – on the secondary market. Its overall value is a function of the value of the mortgages that are packed within the security.
4. A hedge fund or investment bank divides the MBS into different portions to create different products to appeal to a variety of investors with different risk and return preferences.
For example, the fifth and sixth years of interest-only loans are riskier than the first and second years since they are farther out. There’s a greater chance the homeowner will default.
The trade-off is that it will have a higher interest payment.
The bank uses statistical techniques and computer programs to figure out how these products should be designed or engineered, which is naturally a “financial engineer’s” job.
It then combines it with similar risk levels of other MBS and will resell portions, called tranches, to hedge funds and other investors.
5. These products might hold their value well and provide returns for investors until home prices start dropping or buyers begin to default on their mortgages more than what’s expected.
If enough investors lose confidence in the products, they will sell them. If it gets bad enough these products might even be worth nothing.
If banks or other investors have enough exposure to them, this can put them in a liquidity crunch and threaten the financial viability of these institutions.
This is especially dangerous for banks that are “systemically important” and are highly leveraged.
(During the 2008 financial crisis, many large US financial institutions were levered 30:1.)
Financial Engineering – Education
Financial engineering is a growing field, and there are many opportunities for career growth. If you are interested in math and finance, this could be a great career choice for you.
Those who work in financial engineering related jobs generally have background in statistics, applied mathematics, economics, and computer science.
Some have “hard science” backgrounds, such as engineering (e.g., electrical, computer), or physics. Many have advanced degrees, such as PhDs.
Over the years, many graudate programs have added financial engineering programs to teach that type of curriculum directly. Financial engineering programs are not common at the undergraduate level.
Financial engineers are sometimes colloquially referred to as financial “rocket scientists”. They usually have background in applied math, statistics, engineering, or quantitative finance.
Those who come from academia tend to have a theoretical background and are more likely to have an advanced degree.
Conclusion
At its heart, financial engineering is the application of the principles of financial theory to financial problems and engineering products and new methods of dealing with them.
Financial engineers use mathematical models to analyze financial data and come up with new financial products.
Financial engineering can be used to reduce risk, but it can also be dangerous to rely on mathematical models when the future doesn’t look like the past, which in turn can make these products that aren’t well understood dangerous and, in the worst cases, systemically threatening.
Financial engineering is a growing field, and there are many opportunities for those with strong math, statistics, engineering, or hard science background.
Those who work in financial engineering related jobs generally have background in statistics, applied mathematics, economic theory, and computer science. More increasingly have advanced degrees, such as PhDs.
Over the years, many graduate programs have added financial engineering programs to their roster. While financial engineering programs are not common at the undergraduate level, these specialties are becoming more prevalent with more demand for graduates with backgrounds in applied math, financial theory, and other types of skills.