Financial Econometrics Case Study

2019-2020

Course Objective

To learn to work with bad datasets of high-frequency trade data,
To learn to compute and understand realized volatility measures.
To learn to implement advanced time series models and to use these for
forecasting.
To learn to write a report on advanced methods and models.
To learn to give an oral presentation about the most important aspects.
To learn to work as a group.

Course Content

The student uses high-frequency trade data to compute realized measures
of volatility. These daily measures of realized volatility are used to
estimate time series models - realized GARCH (realized Generalized
Autoregressive Conditional Heteroskedasticy) and realized GAS (realized
Generalized Autoregressive Score) - which are used for forecasting
future volatility. The performance of these advanced models is compared
with simpler models such as regular GARCH and GAS models. Financial risk
measures such as Value-at-Risk are also considered.

Teaching Methods

Introductory lecture, discussions per groups, plenary sessions of final
presentations.

Method of Assessment

Written report and oral presentation - as a group of students.

General Information

Course Code E_EORM_FECS
Credits 6 EC
Period P3
Course Level 400
Language of Tuition English
Faculty School of Business and Economics
Course Coordinator dr. L.F. Hoogerheide
Examiner dr. L.F. Hoogerheide
Teaching Staff

Practical Information

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Last-minute registration is available for this course.

Teaching Methods Practical
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