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Introduction, methodology and the Random Walk Model.
Module Leader: Dr Stuart Fraser
代写留学生论文/留学生论文代写stuart.fraser@wbs.ac.uk
Room D1.18 (Social Studies)
Warwick Business School 2
Empirical Finance
Aims:
Provide students with an understanding of the theory andpractice of financial econometrics.
Delivery:
2 hour lecture per week covering theory with demonstrations of practical applications.
1 hour seminar per week in computer lab. using Eviews5.1.
Outcome:
You will be able to select and apply appropriate techniques for analysing financial data in a variety of different contexts.Warwick Business School 3
Empirical Finance
General theme: Efficient Market Hypothesis:
Testing its implications in models of asset prices and returns.
Course content (see course booklet for details):
Methods for analyzing returns (stationary processes).
Methods for analyzing prices (non-stationary processes).
Non-linear financial models: time varying risk.
Assessment:
Short empirical project (20%).
Exam in term 3 (80%).
Warwick Business School 4
Core module reading
Brooks (2002) Introductory econometrics for finance, CUP: Cambridge.
+Good grounding in basic econometrics (+ Eviewsexamples)
-Too basic
Cuthbertson(2002) Quantitative financial economics: stocks, bonds and foreign exchange, Wiley: Chichester.
+Strong on finance theory and empirical applications
-Assumes (advanced) prior knowledge of econometrics
Mills (1999) The econometric modelling of financial time series, CUP: Cambridge.
+Comprehensive coverage of time series techniques for finance with examples of empirical applications.
+/-Very technical
-No coverage of cross sectional techniques (egtests for CAPM)
Warwick Business School 5
Objectives for today…
Begin to understand that theory and data play an equally important role in econometric modelling.
Examine a model with a long tradition in modelling financial data: the random walk model (RWM).
Discuss the pros and cons of the RWM as a description of financial data
Today’s lecture is partly forward looking with the aim of highlighting and motivating topics looked at on the course.
Warwick Business School 6
Introduction
Traditional econometrics is concerned with estimating and testing relationships implied by economic/finance theories.
An econometric model is developed by adding a random error term to the theoretical model; but the processes generating the observed data used to test the theory are ignored.
This is unfortunate as economic/financial data are generated in the ‘real world’数学建模英文论文(non-experimental) as opposed to in the controlled confines of a laboratory.
Consequently the divergence between the theoretical and econometric models may be more than just a random error term.
This is a problem if your methodology can’t tell you what to do to bridge the gap between theory and reality (because it ignored the gap in the first place). For example…
Warwick Business School 7
Some FAQsasked by students grounded in ‘traditional’econometrics
Why do I need to include extra variables/lags in my model which aren’t in the theory I’m testing? Isn’t that data-mining?
Why am I estimating a complicated dynamic system of equations when all I want to test is a simple relationship like PPP ? (see below)
I’ve tested the variables I need to include in my model and they contain a mix of non-stationary and station本论文由英语论文网提供整理，提供论文代写，英语论文代写，代写论文，代写英语论文，代写留学生论文，代写英文论文，留学生论文代写相关核心关键词搜索。