Financial Time Series Workshop

ANALYSIS FOR STRATEGIC APPLICATION: Quantitative Analysis Track

2 DAY WORKSHOP
(Workshop is not being offered in this time)

Qualifies for CPD and 14 CPE Credits

INTRODUCTION

Financial time series modeling has wide use in quantifying various risk factors, predicting returns, prices, and risks. This workshop focuses on some of the common approaches to modeling financial time series. This intensive and highly interactive course includes the latest practical and theoretical developments in financial time series and offers practical case studies and interactive modeling exercises to reinforce both the various concepts and the relationship among these concepts.We strongly encourage delegates to ask questions to maximize benefit and, as such, times may vary during the day from the printed schedule. There will be adequate time allocated for refreshment breaks, lunch and for delegates to network and discuss the issues being addressed.

Who should attend?

This intensive and interactive training course is designed for practitioners with an understanding of statistical principles, who want to deepen their understanding of the particular problems encountered in financial time series analysis. The course benefits both commercial and investment bankers, treasury and investment professionals, and market and credit analysts.

What will you get out of this course?

• Gain a better understanding of the complexities in modeling financial time series

• Develop a structural approach in determining and controlling for the common characteristics of financial data

• Explore the use of ARIMA models for describing financial time series, including risk factors

• Understand the strengths and weaknesses of various GARCH model specifications

• Learn to model equilibrium relationships using Vector Autoregressive (VAR) and Vector Error Correction Models (VECM)

• Model long-run dependencies in time-series, including long-term memories

• Explore the use of stochastic volatility in modeling risk factors

The course uses OxMetrics, an object-oriented matrix language. The language is capable running C++ and GAUSS scripts. Programming in this language is easy and there are several freely available software libraries that extended the capabilities of this language and software. Both the language and the software are very intuitive and no previous exposure to or experience in Ox is required. To learn more about Ox, www.oxmetrics.com provides a wealth of information.

COURSE OVERVIEW AND OUTLINE

For this highly interactive course, all delegates are strongly recommended to attend the workshop with a laptop computer loaded with Microsoft Excel with Visual Basic and Excel Solver Add-ins. There will be several interactive group sessions to work on real-life cases.

The characteristics of financial time series

• Asset returns including the characteristics of equities, interest rates, foreign exchange rates, commodities

• Distributional properties of returns

• INTERACTIVE GROUP SESSION: Getting to know Ox by running descriptive statistics

Linear time series models

• Stationarity

• Correlation and autocorrelation

• Simple AR and MA models

• INTERACTIVE GROUP SESSION: Estimating AR and MA models to forecast returns

ARIMA models

• ARMA models

• Detecting non-stationarity

• Random walk, random walk with drift

• Unit-root tests

• ARIMA models

• INTERACTIVE GROUP SESSION: Forecasting returns

ARCH models

• Volatility

• Building an ARCH model

• Properties of ARCH models

• From ARCH to GARCH

• INTERACTIVE GROUP SESSION: Using ARCH and GARCH to forecast returns

Univariate GARCH models

• Comparing different univariate GARCH models, including I-GARCH, GARCH-M, E-GARCH, T-GARCH, HYGARCH, fIGARCH

• INTERACTIVE GROUP SESSION: Choosing the “right” model for different types of series

Stochastic volatility

• Stochastic volatility

• Estimating stochastic volatility models

• Using estimates of stochastic volatility

• INTERACTIVE GROUP SESSION: Comparing GARCH and stochastic volatility

Non-linear models

• Non-linearity tests, parametric and non-parametric tests

• Threshold models, including STAR, LSTAR MTAR

• Markov switching models

• INTERACTIVE GROUP SESSION: Modeling regime in returns and volatilities

Using econometric approaches for VaR calculations

• Single and multiple period case

• INTERACTIVE GROUP SESSION: Forecasting VaR and its components