Coronavirus Disease 2019 (COVID-19) Update
Due to heightened concerns regarding the outbreak of COVID-19, we are adding more instructor-led online training courses as an alternative to classroom courses.
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
Time-Series Modeling in MATLAB
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.
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| 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.
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| Evaluating Model Appropriateness | Objective: Compute and evaluate model diagnostics to ensure model correctness, suitability and reliability.
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| Forecasting and Simulation | Objective: Forecast models to predict future data. Simulate sample trajectories and statistics by applying Monte Carlo simulation techniques.
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