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Financial Econometrics Using Stata - Part 1

Financial Econometrics Using Stata - Part 1

Course Overview

Financial econometrics applies mathematical and statistical tools to financial economics. In recent years, the growing complexity of the financial markets has led to the formulation of several econometric techniques, which could help practitioners to model and forecast the behaviour of market fundamentals.

This course provides a review of and a practical guide to several major econometric methodologies frequently used to model the stylised facts of the financial time series via ARMA models, univariate and multivariate GARCH models, risk management analysis and contagion.

Practical demonstrations of the alternative techniques will be illustrated using the software Stata and asset prices and forex time series.

The course is based on the new publication: Boffelli, S and Urga, G (2015). Financial Econometrics Using Stata. Stata Press Publication.

All course registrations will receive a complimentary copy.

Course Agenda

Day 1: Modelling the Conditional Mean of Financial Time Series

Sessions 1 & 2:

  • Introduction to financial time series features: distributions of asset returns, stationarity, autocorrelation, heteroscedasticity.
  • Univariate models of conditional mean (MA, AR, ARMA, ARIMA, ARMAX). Analysis of the properties and practical applications of identification and diagnostic checking of ARMA models.
  • Forecasting with ARMA models.

Sessions 3 & 4:

  • Vector Autoregressive models. Analysis of the properties and practical applications of identification and diagnostic checking of VAR models.
  • Impulse response function.

Day 2: Modelling the Volatility of Financial Time Series

Sessions 1 & 2:

  • Characteristics of asset returns volatility.
  • ARCH and GARCH models, Integrated GARCH model, GARCH in mean, GARCHX. Analysis of the properties and practical applications of identification and diagnostic checking of GARCH models.
  • Forecasting with GARCH models

Sessions 3 & 4:

  • Asymmetric GARCH models: SAARCH, EGARCH, GJR, TGARCH, APARCH. Estimating the news impact curve.
  • Alternative GARCH specifications: Power ARCH, Non-linear GARCH models.

Principal texts for pre-course reading

  • S. Boffelli and G. Urga (2016), Financial Econometrics Using Stata. Stata Press Publication.
  • R.S. Tsay (2010), Analysis of Financial Time Series. Wiley & Sons.

Principal texts for post-course reading

The course is based on the new Stata Press publication by S. Boffelli and G. Urga (2015), Financial Econometrics Using Stata.

Prerequisites

Working knowledge of statistics and econometrics and Stata is required. A working interest in financial econometrics is assumed.

Terms & Conditions

  • Student registrations: Attendees must provide proof of full time student status at the time of booking to qualify for student registration rate (valid student ID card or authorised letter of enrolment).
  • Additional discounts are available for multiple registrations.
  • Cost includes course materials, lunch and refreshments.
  • Delegates are provided with temporary licences for the software(s) used in the course and will be instructed to download and install the software prior to the start of the course. (Alternatively, we can also provide laptops for a small daily charge).
  • If you need assistance in locating hotel accommodation, please notify us at the time of booking.
  • Payment of course fees required prior to the course start date.
  • Registration closes 5-calendar days prior to the start of the course.

    • 100% fee returned for cancellations made over 28-calendar days prior to start of the course.
    • 50% fee returned for cancellations made 14-calendar days prior to the start of the course.
    • No fee returned for cancellations made less than 14-calendar days prior to the start of the course.

The number of delegates is restricted. Please register early to guarantee your place.

  •  CommercialAcademicStudent
    2-days (09/10/2017 - 10/10/2017)

All prices exclude VAT or local taxes where applicable.

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