COURSE ID: D-EF23 LANGUAGE:

FINANCIAL TIME SERIES ANALYTICS WITH STATA

This course offers an applied introduction to financial time series analytics in Stata. As such, day one is dedicated to the pervasive autoregressive moving average (ARMA) model, and day two, to a discussion of the modelling of time-varying volatility via the implementation of both ARCH and GARCH methodologies. A considerable amount of time is also dedicated to illustrating, through a series of practical examples, Stata’s capabilities for analyzing financial time series data.

In common with TStat’s workshop philosophy, participants will obtain extensive hands-on experience of the issues under consideration, working on example financial datasets under the careful guidance of the course tutor. Although the course is to be considered primary of an applied nature, technical treatment of the analysis in hand, will however, be provided into order to allow participants to properly address real world applications.

This introductory course offers financial analysts, financial economists and researchers the opportunity to acquire the necessary theoretical and applied background to enable them to: i) independently apply financial time series techniques to their own research arguments, and ii) to understand and evaluate financial econometrics analysis reported in academic and professional literature.

Participants are required to have a basic knowledge of either econometrics or statistics, and the statistical software Stata.

SESSION I

Introduction to time series data management in Stata

Simple descriptive tools for time series analysis

Moving averages

Decomposition

Exponential smoothing

Linear univariate time series models (ARMA models)

 

SESSION II

Modeling volatilities: ARCH and GARCH models

Some applications to risk management (VaR).

We are currently putting the finishing touches to our 2018 training portfolio. Schedule dates for the current year, will therefore be available in due course.

Should you be interested in receiving notification regarding course dates for 2018, please contact us at training@tstat.eu.

 


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This course offers an applied introduction to financial time series analytics in Stata. As such, day one is dedicated to the pervasive autoregressive moving average (ARMA) model, and day two, to a discussion of the modelling of time-varying volatility via the implementation of both ARCH and GARCH methodologies. A considerable amount of time is also dedicated to illustrating, through a series of practical examples, Stata’s capabilities for analyzing financial time series data.