Financial-Econometrics

Topics Covered

The course covers the following topics:

1. Introduction
2. Predictive Regression: Explores the concept of predictive regression in the context of financial forecasting.
3. Multi-Horizon Model: Discusses the use of a multi-horizon model for prediction.
4. Principal Components: Explains the application of principal components in reducing dimensionality and improving forecasting accuracy.
5. Out-of-sample Analysis: Describes the process of evaluating the model's performance on unseen data.

Stock Index and GDP Forecasting Using ARIMA Model

This project focuses on forecasting the stock index and GDP (Gross Domestic Product) using the ARIMA (AutoRegressive Integrated Moving Average) model. The project utilizes Python, MATLAB, time series analysis, and forecasting techniques to achieve accurate predictions.

Project Details

The main objectives of the project are:

• Develop an ARIMA model for forecasting the GDP and stock indexes of 10 countries.
• Optimize the model parameters to improve forecasting accuracy.
• Evaluate the performance of the model and assess its usefulness in financial forecasting.

Methodology

The project follows these steps:

1. Data Collection: Gather historical data on stock indexes and GDP for the selected countries.
2. Data Preprocessing: Clean and preprocess the data, handling missing values and outliers if any.
3. Model Development: Utilize the Econometrics Toolbox in Python and MATLAB to build an ARIMA model.
4. Model Parameter Optimization: Fine-tune the model parameters to improve accuracy.
5. Forecasting: Apply the optimized model to forecast future values of the stock indexes and GDP.
6. Evaluation: Assess the accuracy and reliability of the forecasts through performance metrics and visualizations.
7. Interpretation: Analyze the results and draw insights from the forecasted values.

Project Link

For more detailed information about the project, including code implementation and results, please visit the project link.