Financial Econometrics

Production frontier models have over the years become an indispensable tool of analysis for both scholars and practitioners interested in the measurement of performances through efficiency scores, in academia, business and government. This course provides participants with both the knowledge and requisite applied toolset for applying frontier methods to cross-section and panel data in Stata.

The objective of our Multivariate Garch Models for Risk Management course is to provide participants with a comprehensive overview of the principal methodologies, both theoretical and applied, adopted for the analysis of risk in financial markets.

The modelling and forecasting of energy prices and volatility has become of utmost importance in the current turbulent times. The statistical features of energy data, which tends to follow periodic patterns and exhibit spikes, non-constant means and non-constant variances, renders the task of forecasting energy prices somewhat challenging.

The objective of TStat’s “Forecasting Energy Prices and Volatility with Stata” course is to provide participants with the specific analytical tools to undertake a rigorous and in-depth analysis of prices in international energy markets. The programme covers a wide range of econometric methods currently available to researchers and practitioners, such as: i) univariate and multivariate time series models to estimate and forecast prices and ii) univariate and multivariate GARCH models for the estimation and forecast of price volatility.

The growth in financial instruments during the last decade has resulted in a significant development of econometric methods (financial econometrics) applied to financial data. The objective of our Modelling Volatility and Contagion in Finance course is to provide participants with a comprehensive overview of the principal methodologies, both theoretical and applied, adopted for the analysis of risk in financial markets. To this end, the course focuses on the modelling and forecasting of financial time series of asset returns; the modelling of cross market correlations, volatility spillovers and contagion in financial asset markets. During the course, a number of alternative GARCH models and models of conditional correlations will be reviewed.

Time Series data is today available for a wide range of several phenomena in Business, Finance, Economics, Public Health, the Political and Social Sciences. The aim of TStat Training’s Times Series Modelling and Forecasting Course is therefore, to provide researchers and professionals with the standard tool kit required for the analysis of time series data in Stata. As such the program has been developed to offer an overview of the most commonly used methods for analysing, modelling and forecasting the dynamic behaviour of time series data, offering practical examples of empirical modelling using real-world data. The course begins with an introduction to Stata’s basic time series commands, before moving onto the analysis of time series features and to univariate time series models. Sessions 3 and 4 instead focus on the estimation of both multivariate time series models with stationary and nonstationary data and univariate models of volatility.

In common with TStat’s training philosophy, throughout the course theory and methods are illustrated in an intuitive way and are complemented by practical exercises undertaken in Stata, during which the course tutor discusses and highlights potential pitfalls and the advantages of individual techniques. Particular attention is also given to both the interpretation and presentation of empirical results. In this manner, the course leader is able to bridge the “often difficult” gap between theory and practice of time series modelling and forecasting.

Upon completion, it is expected that participants are able to autonomously implement the statistical methods discussed during the course to their own data, customizing when necessary, the Stata do-file routines specifically developed for the course.