Financial econometrics is an integral component of modern quantitative trading. Cutting edge systematic trading algorithms make extensive use of time-series analysis techniques for forecasting purposes. Thus, if you wish someday to become a skilled quantitative trader, it is necessary to have an extensive knowledge of econometrics.
A subset of my econometrics collection!
In this article I will list my favourite econometrics resources, beginning with an elementary statistical basis through to the current research literature. Please be aware that econometrics can be a tricky subject to grasp as it requires a substantial mathematical and statistical prerequisite base of knowledge.
Probability and Statistics Basics
The best way to start is to make sure that you are familiar with the essential basic probability and stastical concepts. I'm a big fan of the Schaum's Outline series of books, as they always have a huge number of questions to work through. More importantly, these questions actually have worked solutions! I often find it helps to solidify concepts if one is actively practising the theory being described. One of my professors at university would continually remind us that "mathematics is not a spectator sport". I would say the same is true of learning econometrics.
If you have a weak background in probability and statistics, which would be the case if you didn't take these courses in college, then I would highly recommend reading the following Schaum's Outline book on the topic or digging out a copy of Probability and Random Processes by Grimmett and Stirzaker, which is a classic probability text. If however you've taken some solid probability courses while at college/university, then you may want to skip straight ahead to the Schaum's Outline on stats and econometrics (below). Thus the first set of recommended resources are:
1) Schaum's Outline of Probability and Statistics, 4th Edition by Spiegal, Schiller and Srinivasan
Although I only personally have the 3rd edition of this book, I can confidently say that it is extremely useful in brushing up on your undergraduate probability and statistics.
The first part of the book begins with basic probability, random variables, probability distributions, expectation, correlation and ends with worked questions on special probability distributions. These distributions pop up everywhere within quant finance (normal, poisson, binomial, gamma, student's t etc), across derivatives pricing, risk management and quantitative trading.
The statistics section is significantly larger containing seven chapters across a wide range of beginner statistical concepts. Sampling and estimation theory are considered first, then hypothesis testing. Regression, ANOVA and finally Bayesian Methods are considered.
While the book is extremely comprehensive (it covers approximately two semesters of material), it does suffer from some typographical errors and is rather high level for a beginning course in statistics. It is a great supplement to a class or lecture series on the topic, however.
2) Schaum's Outline of Statistics and Econometrics, 2nd Edition by Salvatore and Reagle
Yet another good book from the Schaum's Outlines stable. Salvatore and Reagle initially cover similar ground to Spiegal above, but spend far less time on pure probability.
The worked examples are excellent and the writing style is particularly engaging. After the first five chapters on statistics, estimation and hypothesis testing, the book gradually veers towards statistical methods as applied to econometrics. This includes simple and multiple regression, an essential tool in the econometricians toolbox.
A brief chapter on time-series methods is provided, discussing Autoregressive Moving Average (ARMA), stationarity and cointegration. These tools form the basis of some of the modern quantitative trading algorithms.
Note however that this book is not deep enough in econometrics to be read in isolation. It is really designed as a "bridge" between those who have taken an introductory probability/stats course and want to see slightly more challenging material. The main benefit, as with all Schaum's books, is that there are hundreds of worked examples. The book also includes a computational chapter on which the methods can be tested.
At this stage you should have a good grasp of basic probabiltiy, statistical methods and exposure to the time-series concepts used in econometrics models. The next step is to get a grounding in economics modelling and how it can be applied to large data sets such as that arising from financial markets pricing data, which is the main domain of the quantitative trader.
I'm going to list two introductory econometrics texts here. I want to emphasise that I don't suggest reading them both in their entirety as they cover similar ground between them. However, they do vary significantly in their style and the topics they tend to concentrate on. Thus I will describe each in turn and you can make your own mind up as to which one sounds right for you.
3) Introductory Econometrics for Finance by Brooks
I really like Chris Brooks' text and I would highly recommend it for any prospective quant trader. The book begins with an extensive set of chapters on linear regression, peppered with many examples related to financial markets that are highly applicable to quantitative finance work. The chapters progress from simple linear regression to multiple regression and then discuss the importances of the assumptions of such models.
The next set of chapters concentrate on time-series analysis, including ARMA and Vector Autoregressive (VAR) models. Once again, there are plenty of examples related to quantitative finance, such as forecasting time series.
Chapter 7 discusses long-run relationships and spends time considering cointegration, a useful tool for mean-reversion algorithmic trading strategies. Chapter 8 provides the first glimpse into the world of modelling volatility including an extensive discussion on the famous Generalised Autoregressive Conditional Heteroscedastic (GARCH) model.
The book also has a chapter on Monte Carlo techniques, an area well known to long-term QuantStart readers. The main benefit of the book is that it is geared up towards students of finance, rather than those more interested in purer macroeconomic modelling.
4) A Guide to Econometrics, 6th Edition by Kennedy
This econometrics text by Kennedy takes a slightly different tack from that by Brooks. It concentrates far less on the financial aspect of econometrics than Brooks, instead spending a significant amount of time on discussing when assumptions to certain models can be violated. This is extremely useful as it often quite easy to apply a certain technique to a situation when the assumptions do not actually hold.
The book is not particularly heavy on mathematics (for that have a look at Greene, below) but it is far better at explanation. This is not a theorem-proof text! Its main strength is that it clearly elucidates complex econometric ideas and provides the rationale for why particular models are utilised. Other, more advanced books tend to gloss over these issues.
Although it is not as highly relevant as Brooks' book to quantitative trading, it will certainly help clarify any issues you may have with certain econometric ideas.
Alternative Econometrics Texts
Others have also recommended Basic Econometrics by Gujarati and Porter. Unfortunately, I would like to recommend this book as well but it is currently sitting at $180 on Amazon.com! Despite the extremely high price tag, many people I know have learnt introductory econometrics from this book.
Another text which crops up when discussing more theoretical aspects of econometrics is Econometric Analysis by Greene. If you would like a more mathematical or graduate level treatment of the subject, Greene is recommended.
Econometrics for Financial Engineering
After having studied a text like Brooks' above, you will be well on your way to being skilled within basic econometric theory and time-series modelling. The next step is to start delving deeper into the statistical basis for the econometric theory, so that you are completely familiar with when to be able to apply a certain technique to a particular financial situation.
We will now start to discuss more advanced material, including time-series modelling and the current state of the art in the econometrics research literature.
5) Statistics and Data Analysis for Financial Engineering by Ruppert
Ruppert's book is extremely comprehensive in its treatment of financial data analysis. The book covers significant financial ground from basic asset returns through to GARCH, CAPM, Factor Models and Risk Management. The book is in fact the main recommended text for the Coursera course described above.
Perhaps the main benefit of the book is that it provides numerous worked examples in the R language, which gives the book an extremely practical edge not seen in many other texts. However, it does not teach R from the ground up. For that you will need another book (such as A Beginner's Guide to R by Zuur).
While it contains the "usual suspects" in an econometrics text (such as univariate and multivariate modelling, as well as time-series/forecasting), it also has chapters on Fixed Income Securities, Copulas and Resampling. If you have the time and inclination I suggest reading through the book in its entirety and carrying out all of the worked examples in R. This will give you a thorough grounding in modern econometrics and statistics as applied to financial datasets.
At this stage you will have covered the necessary undergraduate material for financial econometrics. The following two books specialise in time-series analysis, which is the main area of application for a quantitative trader who works on financial pricing data. Both of these books are designed either for graduate students or practitioners.
6) Time Series Analysis by Hamilton
Firstly, I want to point out that this is quite an old book (almost 20 years, in fact!). Hence a lot of the current research literature has moved on. However, there is still plenty here which hasn't changed. Hamilton's book is geared up for the graduate level financial econometrician. It concentrates solely on time-series and so does not delve too deeply into simpler econometric theory.
The book begins with ARMA processes and forecasting, then consideres spectral analysis and asymptotic distribution theory. Later chapters include Bayesian methods, Kalman Filters and Cointegration. So why should you pick this book up if you have already mastered the previous content? The main benefit lies in the depth of the book and the fact that it provides the "bridge" to more advanced research literature.
In terms of audience, I would say that mathematicians will find the book relatively straightforward to progress through, whereas graduate economists might need to brush up on some of the mathematical prerequisites in order to make good progress.
7) Analysis of Financial Time Series by Tsay
Tsay's book complements the one by Hamilton rather well. Despite the fact it has extensive converage of time-series methods, it is written primarily for the practitioner. The book also manages to discuss aspects of high-frequency trading (HFT), market microstructure, risk management (VaR) and the continuous-time Black-Scholes framework for derivatives pricing.
The books spends a good deal of time considering non-linear time series and duration models, which is something not often considered in other works. The examples are carried out in the R language as well as S-PLUS, which makes it straightforward to implement some of the theory being discussed.
This book is extremely useful for practising algorithmic traders as it contains the usual group of time-series methods, such as ARIMA and GARCH, but also considers the models from the point of view of the investor trying to build successful models. This is in contrast to Hamilton's book, which is very much designed for the graduate student.
Current Research in Econometrics
Once you have mastered the prior statistical and time-series based texts you will be ready to try your hand at the modern research literature. Depending upon your affiliation, you may or may not be able to access some of the top financial and econometric journals. However, that shouldn't stop you as many academics publish their papers directly to their websites and provide pre-print papers on popular research networks such as the Social Sciences Research Network or the arXiv.
8) Pre-Print Servers - arXiv, SSRN
Pre-print servers, in my opinion, are one of the greatest uses of the internet to date. Having graduated from university it is tricky for me to obtain some of the latest papers out of journals. I won't digress into the politics of journal subscriptions, but I will say that SSRN and the arXiv computational finance section provide a great deal of interesting research material, much of it within financial econometrics.
The drawback of sites such as the arXiv, is that they lack the rigour of peer review (by the very nature of the site!), hence it can sometimes be harder to wade through the junk. This is why it is worth keeping on top of the literature via the journals, making use of the pre-print servers to access papers that you know are likely to be of high quality due to the individuals involved.
9) Journals - Journal of Econometrics, Journal of Finance
The traditional academic community will be publishing in high quality journals. They are often a great source of ideas for statistical tools, as well as quantitative trading strategies. The peer review aspect will often ensure you are at least getting a reasonably reputable paper to read before committing the effort to doing so!
If you have attended grad school for finance you will be familiar with some of the top names in finance and econometrics. Here are a few worth considering if you have the ability to access them:
- Journal of Econometrics
- Journal of Financial and Quantitative Analysis
- Journal of Finance
- Journal of Financial Economics
Honourable Mentions and Other Resources
Despite the length of this article, I have only begun to scratch the surface of the wealth of econometrics resources available. I honestly wish there were more hours in the day to read it all! One of the best ways to find upcoming quant research papers is to look at the following two blog feeds:
- Quantivitiy Twitter Feed - This contains a constantly updated set of great quant research papers, many in econometrics.
- The Whole Street - TWS provides a comprehensive "mashup" of various flavours of quant blogs, research papers and trading articles. Dig deep and you will likely find some fantastic articles.
That concludes our foray into financial econometrics. In later articles I will be looking at individual techniques, especially some of the more advanced models used for algorithmic trading purposes.
The main tool of econometrics is the linear multiple regression model, which provides a formal approach to estimating how a change in one economic variable, the explanatory variable, affects the variable being explained, the dependent variable—taking into account the impact of all the other determinants of the ...How long does it take to study econometrics? ›
BCom (Econometrics) is a three-year full-time programme in which students are introduced to economic theory, economic policy and the statistical theory underpinning empirical analysis which is more commonly referred to as econometrics.Is econometrics a difficult course? ›
Econometrics can be a difficult subject for many students.Is financial econometrics necessary? ›
Financial econometrics and statistics have become very important tools for empir- ical research in both finance and accounting. Econometric methods are important tools for asset-pricing, corporate finance, options, and futures, and conducting financial accounting research.How do I learn basic econometrics? ›
- Econometrics Academy. One-stop solution for Econometrics for a beginner. ...
- MIT Open courseware. ...
- Econometrics: Methods And Applications. ...
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- Quantitative and Econometric Analysis focused on Practical Applications.
There are two branches of econometrics: theoretical econometrics and applied econometrics.How can I be good in econometrics? ›
Try to get old econometrics exams from exam banks, libraries, or former students. These are particularly useful if the same economics professor has taught the course for many years. Talk to former students of the course. They'll know the examination style of the professor and may be able to provide useful tips.How can I be a good Econometrician? ›
Obtaining a master's degree in economics or mathematics with a specialization in econometrics, along with research and internship experience, can help prepare you for an econometrician job. The career also requires excellent problem-solving skills and an aptitude for complex quantitative analysis.What can I do with econometrics degree? ›
- Data analyst.
- Financial analyst.
- Risk analyst.
- Analytics consultant.
- Economic policy associate.
- Investment manager.
The MS in Econometrics and Quantitative Economics with a sub-plan of Financial Economics is a STEM-eligible degree designed specifically for students who wish to pursue careers in the banking and monetary systems.
As mentioned previously, AP Microeconomics course material was designed to mimic an introductory college-level course, so it will certainly be more difficult than a standard high school class. Students unfamiliar with economic topics — or how to work with data — may find it challenging.What can you do with an econometrics degree Reddit? ›
In econometrics and know people working for retail companies in the analytics division, finance companies, teaching, government jobs, etc. I personally got a job as an analyst for a start up company making mobile video games. It is a wide world for an econometrician because there is just so much data out there.How is financial econometrics different from econometrics? ›
It differs from other forms of econometrics because the emphasis is usually on analyzing the prices of financial assets traded at competitive, liquid markets.Is financial econometrics different from economic econometrics? ›
The tools commonly used in financial applications are fundamentally the same as those used in economic applications, although the emphasis and the sets of problems that are likely to be encountered when analyzing the two sets of data are somewhat different.Is econometrics used in investment banking? ›
Econometricians analyze data sets to model outcomes or make predictions using techniques such as linear regression. Econometricians may be employed at universities as academic economists or else work in financial firms such as investment banks or hedge funds, where they go by the term "quants."What are the important features of econometrics? ›
Econometrics is the use of statistical methods to develop theories or test existing hypotheses in economics or finance. Econometrics relies on techniques such as regression models and null hypothesis testing. Econometrics can also be used to try to forecast future economic or financial trends.
Econometrics is interesting because it provides the tools to enable us to extract useful information about important economic policy issues from the available data. Students who gain expertise in econometrics will also find that they enhance their job prospects.Where can I study econometrics in India? ›
- Indian Academy Degree CollegeKalyan Nagar, Bangalore. Admission '22Placement. ...
- University of MadrasChepauk, Chennai. Admission '22PlacementCutoff. ...
- West Bengal State UniversityBarasat, Kolkata. ...
- SRM Institute of Science and Technology, ChennaiKattankulathur, Chennai.
Ragnar Frisch, along with Jan Tinbergen, pioneered development of mathematical formulations of economics. He coined the term econometrics for studies in which he used statistical methods to describe economic systems.What is the role of financial econometrics? ›
Broadly speaking, financial econometrics is to study quantitative problems arising from finance. It uses sta- tistical techniques and economic theory to address a variety of problems from finance.
ECONOMETRIC ANALYSIS STEPS:STEP 1: ECONOMETRIC MODEL SPECIFICATIONSTEP 2: ESTIMATIONSTEP 3: DIAGNOSTIC TESTINGSTEP 4: PREDICTION /FORECASTINGSTEP 1: ECONOMETRIC MODEL SPECIFICATIONSpecification of an econometric model requires knowledge of economic theory or invokingcommonsense.Which is the most used method for estimation in econometrics? ›
Section 3 introduces the Maximum Likelihood Estimator, which is still one of the most commonly used estimation methods. The Generalized Method of Moments approach is introduced in section 4. The second part of the chapter focuses on econometric models and applications of these three estimation methods.Is economics a difficult subject? ›
It's a very difficult field of graduate study, however, and in my experience is probably the largest jump in difficulty of any major between undergraduate and graduate (although I clearly don't have experience with all majors).What is regression stat? ›
Key Takeaways. A regression is a statistical technique that relates a dependent variable to one or more independent (explanatory) variables. A regression model is able to show whether changes observed in the dependent variable are associated with changes in one or more of the explanatory variables.What qualities should an economist have? ›
- Mathematical aptitude. Numeracy is a key skill for an economist. ...
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This work is often well paid and comes with more freedom in terms of working hours and self-presentation than other office-based jobs. For more job opportunities and advice for economists, including top career paths for other economics specialisations, see our website at INOMICS.COM.What skills do you need to study economics? ›
- thinking logically and critically.
- the ability to simplify complex issues and extract the relevant pieces of information.
- data analysis.
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How much does an Econometrics make? As of Oct 18, 2022, the average annual pay for the Econometrics jobs category in the United States is $79,900 a year. Just in case you need a simple salary calculator, that works out to be approximately $38.41 an hour. This is the equivalent of $1,536/week or $6,658/month.Which is better economics or econometrics? ›
Econometrics is the mathematical aspect of Economics. Without economics, there is no econometrics. Thus they are equally as important.What is the highest paying job in economics? ›
- Statistician. ...
- Corporate lawyer. ...
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- Actuary. National average salary: $113,430 per year. ...
- Senior market analyst. National average salary: $115,166 per year. ...
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There are many diagrams in economics, but there is not a large amount of math. A proviso: The amount of math in the economics curriculum varies across colleges and universities. Some economics departments do not require their students to learn much math or statistics, but others do.Is a PhD in economics STEM? ›
We are pleased to announce that both the undergraduate major in Economics and the Economics PhD program are classified as STEM fields because of their quantitative nature. The CIP code for both programs is 45.0603 (Econometrics and Quantitative Economics).Does finance count as STEM? ›
Accounting and finance are not considered STEM majors. Neither involve enough math at the undergraduate level. They are business majors.Is it better to take micro or macro first? ›
Research has shown students who study macro first perform better academically in both macro and micro than students who study micro first.What kind of math do economists use? ›
Calculus is the most common type of math found in economics. Calculus includes the use of various formulas to measure limits, functions and derivatives. Many economists use differential calculus when measuring economic information.Do you need to be good at maths to do economics? ›
Generally – if you just want to do an undergraduate degree in economics, you don't have to be a maths genius to follow standard undergraduate level micro and macro courses.What can you do with a masters in economics Reddit? ›
- econ consulting (eg NERA, Brattle, boutique)
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However, they are both different. Econometrics deals with the use of statistical and mathematical tools to analyze trends and predict future outcomes. Mathematical economics involves the application of mathematical models in the analysis of economic concepts.Is econometrics same as statistics? ›
Econometrics originally came from statistics. In general statistics is more general than econometrics, since while econometrics focuses in Statistical Inference, Statistics also deals with other important fields such as Design of Experiments and Sampling techiniques.Is econometrics the same as economics? ›
Econometrics is a subset of economics, applying statistics and mathematical techniques to “justify” a theoretical economic model with empirical rigor. In other words, econometrics transforms the often arcane discipline of theoretical economics into policy and decision-making tools in the public and private sectors.
There are three types of data: time series, cross-section, and a combination of them is called pooled data. Time series data of a variable have a set of observations on values at different points of time. They are usually collected at fixed intervals, such as daily, weekly, monthly, annually, quarterly, etc.What is the methodology of econometrics? ›
The methodology of econometrics is the study of the range of differing approaches to undertaking econometric analysis. Commonly distinguished differing approaches that have been identified and studied include: the Cowles Commission approach. the vector autoregression approach.Which subject is better economics or statistics? ›
Economics will help you pursue a career in financial research, equity research, financial journalism whereas Statistics will give you option of pursuing a career in a range of Data Analytics related field which is in demand now a days.Is econometrics good for trading? ›
Financial econometrics is an integral component of modern quantitative trading. Cutting edge systematic trading algorithms make extensive use of time-series analysis techniques for forecasting purposes.Do quants use econometrics? ›
It seems quants increasingly use econometric models at work.How is econometrics used in trading? ›
Additional reading. Options Trading Using Econometric Models In the financial world, time series analysis is frequently used to predict stock prices, interest rates, and currency exchange rates.What are examples of econometric models? ›
- Linear regression.
- Generalized linear models.
- Vector Autoregression.
There are three types of data: time series, cross-section, and a combination of them is called pooled data. Time series data of a variable have a set of observations on values at different points of time. They are usually collected at fixed intervals, such as daily, weekly, monthly, annually, quarterly, etc.What is the difference between econometrics and statistics? ›
Statistics is about analysing data, econometrics is the application of statistical methods to economic data. Both disciplines involve the use of probability theory and computer simulations to establish properties of such methods.What are the topics in econometrics? ›
- Carbon Dioxide Emission.
- Environmental Kuznets Curve.
- Corporate Social Responsibility.
- Energy Consumption.
- Environmental Impact Assessment.
- Gross Domestic Product.
Ragnar Frisch, along with Jan Tinbergen, pioneered development of mathematical formulations of economics. He coined the term econometrics for studies in which he used statistical methods to describe economic systems.What are the stages of econometrics? ›
- Statement of theory or hypothesis.
- Specification of the mathematical model of the theory.
- Specification of the statistical, or econometric, model.
- Obtaining the data.
- Estimation of the parameters of the econometric model.
- Hypothesis testing.
Econometrics is interesting because it provides the tools to enable us to extract useful information about important economic policy issues from the available data. Students who gain expertise in econometrics will also find that they enhance their job prospects.Where can I get data for econometrics? ›
- The Econometric Society. ...
- Econometrics — Open Access Journal. ...
- EconPapers. ...
- Federal Reserve Economic Data (FRED) ...
- Federal Reserve System Economic Data. ...
- Global Financial Data. ...
- National Bureau of Economics Research (NBER) ...
- The Organisation for Economic Co-operation and Development (OECD)
Econometrics is a subset of economics, applying statistics and mathematical techniques to “justify” a theoretical economic model with empirical rigor. In other words, econometrics transforms the often arcane discipline of theoretical economics into policy and decision-making tools in the public and private sectors.What are the major sources of data? ›
There are two sources of data in Statistics. Statistical sources refer to data that are collected for some official purposes and include censuses and officially conducted surveys. Non-statistical sources refer to the data that are collected for other administrative purposes or for the private sector.Is it better to study economics or econometrics? ›
Economics is for policy makers while econometrics is for programmers and professionals who help companies forecast on future performance. Between the two courses, Economics at masters level is more superior to econometrics but less marketable.Which is better economics or econometrics? ›
Econometrics is the mathematical aspect of Economics. Without economics, there is no econometrics. Thus they are equally as important.Should I take econometrics or statistics? ›
In general statistics is more general than econometrics, since while econometrics focuses in Statistical Inference, Statistics also deals with other important fields such as Design of Experiments and Sampling techiniques.What are the important features of econometrics? ›
Econometrics is the use of statistical methods to develop theories or test existing hypotheses in economics or finance. Econometrics relies on techniques such as regression models and null hypothesis testing. Econometrics can also be used to try to forecast future economic or financial trends.