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IB2B20 – Financial Econometrics
创建日期：2024-07-09 16:26:02
所属分类：金融Finance
标签： IB2B20
Financial Econometrics

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IB2B20 – Financial Econometrics


Overview and class goals

This module aims at providing a solid training in econometric methods for the empirical analysis of financial markets. The core of the module is an undergraduate-level introduction to financial econometrics,  and  it  is meant to equip the student with the quantitative skills needed for carrying on research projects in empirical finance.  By the end of the module the students would have: (i) gained knowledge and understanding of the material needed for empirical quantitative analyses; (ii) developed the habit of thought, knowledge and understanding in order to carryout good quality applied econometric research; (iii) acquired the necessary skills to interpret and communicate empirical results to non-technical audiences; and (iv) developed critical insights to interpret the econometric results obtained by other researchers.  The emphasis throughout the module is on the application of standard techniques for the analysis of cross-sectional, time-series, and panel financial datasets.

Prerequisites

A willingness to work hard on possibly unfamiliar material.  Good knowledge of undergraduate- level statistics and mathematics for economics/business. A basic understanding of statistics is very helpful meaning that you will benefit more from the class if you have taken at least one undergraduate class in quantitative methods. It is my understanding that students have taken Business Statistics and Business Analytics. Basic knowledge of Matlab is advantageous.

Organization

One live one-hour lecture, one live one-hour seminar (starting from Week 2) and approxiamtely one-hour pre-recorded lecture/MATLAB session per week (available on the module’s MyWBS page).

The lectures will be on Tuesdays, 16.00-17.00 [Woods-Scawen Lecture Theatre, Arts], for weeks 1-10.

The seminars will be on Tuesdays  and Fridays,  weeks  2-10.   There  will  be  six  seminar groups.  Three on Tuesday at:  13.00-14.00  [Room R3.41,  1500-16-00  [Room  S0.09] and  18.00- 19.00 [Room S0.09], and three on Friday: 9.00-10.00[Room OC1.03], 10.00-11.00 [OC1.07] and 11.00-12.00 [Room OC0.05]. Giulio Rossetti will help me in delivering these seminars.

Lectures will focus on methodological and applied exercises.  Seminars will focus on practical exercises and Matlab coding.  Solutions will be provided to the students.  Giulio Rossetti will also provide Matlab support (see his office hour).

Note:  the undegraduate team allocate students to seminar groups, therefore please speak direclty to them if there are any enquiries on this.

Assessmemnt

❼ An in-person 2 hours exam  (80% of the final mark).  Date:  Summer  2024, precise date TBD (last year it was in the first two weeks of June).

❼ A group project (20% of the final mark, 1500 words).  Groups will comprise five students (approximately), chosen by the undergraduate team (speak to them for any enquiries on this) by week 5 (beginning 5th February).  Deadline to hand in the project:  14th March 2023.

Textbooks and other support materials

❼ Core  text:  Jeffrey M. Wooldridge, Introductory Econometrics:  A  Modern  Approach, 5th edition, South Western College.

❼ Core text:  Chris Brooks, Introductory Econometrics for Finance, 4th edition, Cambridge University Press.  (Note: 2nd and 3rd editions also applicable)

❼ Further material:  Occasionally, lecture notes will be provided as a further reading to gain a deeper knowledge of the topics.

Detailed syllabus

Week 1:  Economic data and econometric analysis.  The Simple Linear Regression  (SLR) and the Multiple Linear Regression  (MLR) models.   Derivation of the  Ordinary Least Squares (OLS) estimators and their algebraic properties. Unbiasedness and Efficiency of the OLS estimators and corresponding standard errors.  Estimation, Goodness-of-Fit, and finite- sample properties.

(Chs. 2-3 Wooldridge, Ch.3 Brooks (Ch. 2, 2nd edition) + Slides and Lecture Notes)

Week 2:  Linear Regression Models: Inference. Sampling distribution of OLS estimators, confidence intervals, hypothesis testing about linear combinations of parameters and testing multiple restrictions.

(Ch. 4 Wooldridge, Ch.4 Brooks (Ch. 3 , 2nd edition) + Slides and Lecture Notes)

Week 3:  Dummy Variables and Heteroskedasticity:  A single dummy independent variable and interactions involving dummy variables.  Definition of heteroskedasticity, consequences for OLS estimates and robust inference.  Testing for heteroskedasticity and the Generalized Least Squares (GLS) estimator.

(Ch. 7.2-7.4 and 8.1-8.4 Wooldridge + Slides and Lecture Notes)

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