Prerequisites

MATLAB for Financial Applications and knowledge of time-series modeling concepts.

This program has been approved by GARP and qualifies for 7 GARP CPD credit hours. If you are a Certified FRM or ERP, please record this activity in your credit tracker at http://www.garp.org/cpd

Time-Series Modeling in MATLAB

This one-day course provides a comprehensive introduction to time-series modeling using MATLAB^® and Econometrics Toolbox^™. The course is intended for economists, analysts and other financial professionals with prior experience of MATLAB who require to develop and maintain time-series models. The course is designed to follow the standard Box-Jenkins procedure for developing time-series models. High-level course themes include:

Preprocessing time-series data
Identifying long-term and seasonal trends in time-series data
Testing data stationarity using hypothesis tests
Creating and fitting ARIMA and GARCH time-series models to a data set
Comparing different model fits for the same data
Analyzing model dynamics using Monte Carlo simulations
Forecasting data using fitted models

See detailed course outline

Prerequisites

MATLAB for Financial Applications and knowledge of time-series modeling concepts.

This program has been approved by GARP and qualifies for 7 GARP CPD credit hours. If you are a Certified FRM or ERP, please record this activity in your credit tracker at http://www.garp.org/cpd

Day 1 of 1
Preparing Data for Model Fitting	Objective: Prepare time-series data for model fitting by identifying trends and applying data transformations. Removing exponential trends Identifying polynomial and seasonal trends Testing for data stationarity Stationarizing data Unit-root tests
Model Selection and Fitting	Objective: Use diagnostic tools to select a group of suitable candidate ARIMA and GARCH models for a given time series. Identify, create and fit candidate time-series models to data. Computing autocorrelation and partial autocorrelation Selecting models using formal tests Selecting candidate ARIMA and GARCH models for a given data set Creating and fitting time series models to a data set
Evaluating Model Appropriateness	Objective: Compute and evaluate model diagnostics to ensure model correctness, suitability and reliability. Inferring model residuals Testing residuals for Normality Analyzing model diagnostics and goodness-of-fit statistics Evaluating significance of individual model terms Comparing models
Forecasting and Simulation	Objective: Forecast models to predict future data. Simulate sample trajectories and statistics by applying Monte Carlo simulation techniques. Forecasting data using fitted models Using in-sample forecasts to evaluate model appropriateness Monte Carlo model simulation Backtesting models

Guaranteed to Run

When you register for one of these courses, you can rely on the fact that it won't be canceled or rescheduled for any reason.

MATLAB and Simulink Course Schedule

There are currently no scheduled classes for this course.