Financial Econometrics Explained: Methods, Models, and Careers

Key takeaways

Financial econometrics uses statistical methods to evaluate economic and financial data. Here are some important facts to know: 

• Econometricians, also known as quantitative analysts, make a median annual salary of $104,350 [1].

• Two of the core methods used in financial econometrics are time series analysis and volatility modeling. 

• You can use financial econometrics in such roles as risk analyst and financial data analyst.

Learn more about some of the core statistical and data analysis methods used in financial econometrics. If you’re ready to build your financial data analysis skills, consider enrolling in the University of Illinois at Urbana-Champaign Accounting Data Analytics Specialization. You’ll have the opportunity to gain skills in time series analysis, exploratory data analysis, and predictive modeling, and earn a Specialization in as little as three months. 

Types of financial econometrics methods

Financial econometrics uses many statistical methods when analyzing financial data, with some of its main methods using descriptive statistics. Some of the basic methods used in financial econometrics include:

• Measures of central tendency: This refers to the mean, median, and mode of the data. These methods give the most basic overview of the data, such as the average (mean), center-most data point (median), and most frequently occurring data point (mode). 

• Measures of dispersion: These measurements include the standard deviation, variance, and range of the data, which tell you how the data is spread out and distributed, or, in the case of finance, how variable it is. 

• Regression analysis: This method involves using independent and dependent variables to create a mathematical equation capable of predicting future outcomes. Common regressions used in financial econometrics include simple linear regression, multiple linear regression, polynomial regression, and logistic regression.

Two of the more complex methods of statistical analysis that heavily influence financial econometrics are time series analysis and volatility modeling.

Time series analysis

Time series analysis takes historical, time-dependent data and creates a statistical model of the past that makes predictions of the future. Depending on how detailed the historical data is, these models have parameters you can tweak to predict varying aspects of the desired financial metric. For financial econometrics, some examples of time series data include stock prices, interest rates, exchange rates, and key economic indicators. Most financial data sets exist as a time series. 

Volatility modeling

Volatility modeling is another core statistical technique in financial econometrics and measures the dispersion of the financial data you're analyzing. The amount of volatility or variation in the data represents the amount of risk associated with it. For example, if a stock has a great degree of variation between its daily prices, it’s volatile. Some models used in volatility modeling include:

• ARCH/GARCH: Autoregressive conditional heteroskedasticity (ARCH) and generalized autoregressive conditional heteroskedasticity (GARCH) model volatility over varying time periods. 

• Stochastic volatility models: These models build on ARCH/GARCH to use stochastic techniques to model the time variation with more malleability. 

• Jump diffusion models: These models try to account for the sudden spikes in financial data, such as stock prices. 

• Realized volatility models: These models utilize industry data to model the volatility of assets in an attempt to estimate future volatility. 

Is econometrics the same as financial forecasting?

Financial econometrics focuses on modeling the relationship between various financial variables using statistical methods and machine learning, while financial forecasting predicts future performance based on historical financial data. While they are not the same, the models and theories in financial econometrics help inform better ways to create financial forecasts. 

Uses of financial econometrics

Financial econometrics gives financial analysts statistical tools and methods to analyze and predict financial futures, metrics, and assets. The theories and applications of econometrics help make financial policy, influence business decisions, and model economic futures. Some deliberate use cases of financial econometrics in finance include:

• Analysis: The most basic form of econometrics helps financial analysts and economists determine the relationships between multiple financial variables, such as stocks, earnings, interest rates, and key economic indicators, such as gross domestic product (GDP) and unemployment rates. This analysis allows you to find real-world links to events like crashes and recessions. 

• Risk management: Managing risk in investments is a core strategy for banks, governments, and investors, and the statistical method of econometrics allows you to see the varying risks in specific investments. You can use techniques like value-at-risk (VaR) to determine the confidence level in certain investments over time, as well as stress testing to model different scenarios that could affect investments. 

• Asset pricing: The statistical methods used in econometrics allow investors to see how certain assets should be priced compared to how they are priced in the market. A common model is the capital asset pricing model (CAPM), which shows the relationship between risk and return. This econometric model allows investors to see an asset’s price versus the real-world market value of that asset. 

• Economic forecasting: Econometrics is an important factor in creating economic forecasts of the key indicators for growth, such as unemployment, GDP, inflation, and interest rates. Time series models are important methods used to create forecasts based on historical data. 

Who uses financial econometrics?

Econometrics is useful in both public and private sectors to describe economic data using statistical methods. In the public sector, this includes modeling key economic indicators, while the private sector uses it to assess the risk of investments. Explore some jobs that use econometrics, their median annual salary, and job outlook. 

Econometrician

Median annual salary: $104,350 [1]

Job outlook (projected growth from 2024 to 2034): 8 percent [1]

As an econometrician or quantitative economist, sometimes referred to as a “quant” at hedge fund firms, you utilize statistics and mathematics developed from econometrics to conduct financial analysis for an organization. You may produce statistical models that allow a business to predict futures or investors to make data-driven investment decisions. 

Risk analyst

Median annual salary: $101,910 [2]

Job outlook (projected growth from 2024 to 2034): 6 percent [2]

As a risk analyst, you may use econometrics to build models that help assess the financial risk of investments in securities as well as certain business decisions. You would help ensure the amount of risk a company takes aligns with the framework created by executives while monitoring a company’s compliance with financial regulations. You might specialize in credit risk, market risk, or the overall operational risk of certain events. 

Data analyst

Average annual salary: $112,590 [3]

Job outlook (projected growth from 2024 to 2034): 34 percent [3]

As a data analyst in finance, you may use econometrics to analyze data to help make financial decisions and predict future outcomes. While you may focus more on storing, managing, and extracting data rather than producing econometric models, you would use mathematics and statistics to help econometricians produce models. You would also help analyze, visualize, and manage financial data. 

Learn more: What Does a Data Analyst Do? Your Career Guide

Is financial econometrics data science?

Financial econometrics and data science are related yet different disciplines. Since financial econometrics is already an interdisciplinary field, data science adds another layer when it comes to financial analysis. Their main difference is that financial econometrics focuses on the use of statistical techniques, while data science focuses on the analysis of data sets. 

New techniques in data science, such as machine learning and big data processing, enhance financial data science, distinguishing it from traditional econometric methods while adding another dimension to economic analysis. 

Is financial econometrics useful​? Pros and cons to consider

Econometrics is useful for the statistical modeling of economic phenomena using data sets to make predictions and assumptions about economic behavior. It allows you to explore variables to determine how they affect different financial elements. For example, you could explore how changes in unemployment affect a nation's GDP by examining both variables using a regression analysis. 

However, econometrics does have limitations in its analysis of economic phenomena, one being that statistical correlation does not always relate to the social behavior of humans in an economic system. Econometricians can overcome these limitations by following economic principles and existing theories or by using their data to help develop new economic theories. 

Explore our free resources for a career in finance

Subscribe to our Career Chat newsletter on LinkedIn to stay on top of industry news and network with professionals. Then, check out the following free resources to learn more about finance:

• Hear from an expert: 8 Questions with an Expert: Google Financial Data Analyst

• Watch on YouTube: Introduction to Financial Markets by Yale University #1

• Take a quiz: Which Finance Course Should You Take? Find Out in 1 Minute

With Coursera Plus, you can learn and earn credentials at your own pace from over 350 leading companies and universities. With a monthly or annual subscription, you’ll gain access to over 10,000 programs—just check the course page to confirm your selection is included.

Build job-ready skills with Coursera Plus

Start 7-day free trial

• 
• 
• 
• 

Start 7-day free trial