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BU.232.630 – Nonlinear Econometrics for Finance

Nonlinear econometrics for finance

2 credits

BU.232.630.W2

Mondays 6:00 - 9:00 pm

January 23^th - March 13^th 

Spring I 2023

DC Campus

Instructor

Federico M Bandi

Contact Information

Email: [email protected]

E-mail is the easiest way to contact me.

Office Hours

By appointment.

Teaching Assistants: see slides for Lecture 1.

Required Texts & Learning Materials

All materials will be posted online on OneDrive with a link on Canvas in the welcome announcement.

· The slides will be posted one or two days before class.

· Academic articles will also be posted, as needed.

Recommended Texts

A suggested, non-mandatory, technical reading is the book Econometrics by Bruce Hansen. A copy of the book is on OneDrive.

The first ten chapters are largely about linear models (for which I will also provide a set of lecture notes). Chapter 13 and Chapter 5 are about the generalized method of moments and maximum likelihood, techniques which will be central to our discussion.

Another suggested, non-mandatory, reading is the book Asset Pricing by John Cochrane. This is a less technical reading than the previous book but has a substantial finance content. An older version of this book is also posted on OneDrive.

Technology Requirements

We will use Python heavily. Please install the software before the beginning of classes. You should already have installed it from your Computational Finance course in the fall.

Course Description

Nonlinear Econometrics introduces econometric tools needed to analyze financial data and build state-of-the-art nonlinear financial models. This is an advanced class requiring strong foundations in multivariate calculus, matrix algebra, probability and statistics. The course covers methods of asymptotic (i.e., large-sample) inference in extremum (nonlinear) estimation. Among them, particular emphasis is placed on nonlinear least-squares (NLS), the generalized method of moments (GMM) and maximum likelihood (ML) estimation.

Prerequisite(s)

Computational Finance and Linear Econometrics are prerequisites. We will rely heavily on both courses. Complete familiarity with classical methods of inference in linear models – as introduced in Linear Econometrics - is critical to gain complete understanding of this course’s nonlinear methods. Programming in Python – as discussed in Computational Finance – will be used heavily throughout.   

Course overview

In a world of big data and increasingly sophisticated methods of statistical inference, NLS, GMM and ML methods represent fundamental building blocks for effective data analysis. Their logic and theory – the focus of this course – justify and demystify recent econometric advances in empirical finance (and beyond).    

Finance and Social Responsibility

The effectiveness and perceived integrity of finance have been tested in recent years. Along with preventable excesses and regrettable distortions, financial innovation has, however, always been an effective means for society to achieve its goals, from insurance to consumption to saving. The power of financial innovation as a generator of inclusive prosperity and widespread well-being can (and should be) reclaimed. In this context, optimization of shareholder’s value, for instance, may not be the only metric along which financial success is measured and should be placed, along with other traditional finance metrics, in the broader context of its contribution to society. To this extent, Carey encourages technical, non-ideological, exchanges of ideas leading to a better understanding of the broader role of finance as a force for shared prosperity. The reading (to be announced) provides an initial opportunity for technical discussions of these issues as they relate to the topics covered in Nonlinear Econometrics.

Learning Objectives

By the end of this course, students will be able to:

1. Evaluate linear econometric models in terms of their statistical fit

2. Evaluate nonlinear econometric models in terms of their statistical fit

3. Evaluate economic theories using linear and nonlinear methods

To view the complete list of the Carey Business School’s general learning goals and objectives, visit the Carey website.

Attendance
Participants are expected to attend all scheduled class sessions. Failure to attend class will result in an inability to achieve the objectives of the course. Full attendance - and active participation - are required for you to succeed in this course.

Classroom protocol

· All behaviors and communications in class sessions must be professional, civil and compliant with Carey student policies

· Participants are expected to turn off their phones while in class

Assignments

Homework (30%): There will be 3 homework assignments, each worth 10% of the final grade. The assignments have a very important pedagogical role. They are designed to check your understanding of the material covered in class by making you work through an array of theoretical and applied problems. You can work on these in groups (maximum 3 people) but you do not have to do so, if you so choose.

Quizzes (15%): There will be 3 in-class quizzes, each worth 5% of the final grade. The quizzes are in weeks 3, 5, and 7 - at the end of class. They will be short tests, with 2 or 3 questions to be solved in about 15 minutes, designed to check your understanding and knowledge of topics covered in previous weeks.

Final Exam (55%): The (cumulative) final exam will be between 2 and 3 hours long.

Regarding coding

You will, sometime, have issues (everybody does). If you do, you can ask the TA for your class (see slides for Lecture 1) but only after doing the following: (1) consulting available Python resources (virtually every question has been addressed online) and (2) asking one (or more) of your peers. In other words, every time you ask a question about coding you should first begin with your question and then add why (1) and (2) above where not helpful. In the absence of (1) and (2), the TA may not answer your query. This is an advanced course and you should begin training yourself to be creative and independent.

Grading

The grade of A is reserved for those who demonstrate extraordinary performance as determined by the instructor. The grade of A- is awarded only for excellent performance. The grades of B+ and B are awarded for good performance. The grades of B-, C+, C, and C- are awarded for adequate but substandard performance. The grades of D+, D, and D- are not awarded at the graduate level. The grade of F indicates the student’s failure to satisfactorily complete the course work. For Core/Foundation courses, the grade point average of the class should not exceed 3.35. For Elective courses, the grade point average should not exceed 3.45.

Tentative Course Calendar

Instructors reserve the right to alter course content and/or adjust the pace to accommodate class progress. Students are responsible for keeping up with all adjustments to the course calendar.

Optional Readings & Resources

Any optional and additional reading will be posted on OneDrive.