ARCH vs GARCH: Comparative Analysis of Volatility Models in EViews Assignments

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ARCH vs GARCH: Comparative Analysis of Volatility Models in EViews Assignments

Volatility modeling is a core component of the financial econometric analysis. It depicts the variance of returns and associated risks with financial assets. For research scholars who deal with the time-series data, EViews offers powerful tools to model and analyze volatility. Out of all the models included, it has been observed that ARCH and GARCH models are especially highlighted for producing efficient and effective results.

arch vs garch for eviews assignment help

In this article, we will discuss and compare ARCH and GARCH and highlight the necessity of hiring the professional EViews assignment help for the scholars who struggle with their EViews assignments.  

What is Volatility Modeling?

Volatility modeling deals with measuring the asset price fluctuation or estimate variability, which is useful in analyses of risk management, derivative pricing, and portfolio optimization. In conventional forms of time series models like ARIMA, where the variability is constant over a period of time, but it is not applicable to financial data. Unlike ARIMA, ARCH and GARCH permits change of variance over time capturing volatility clustering.

The ARCH Model

Overview

The ARCH model that was introduced by Robert Engle in 1982 was a huge breakthrough in volatility modeling. It assumes that the variance of the current error term is a function of squared errors of earlier periods.

Model Specification

The basic ARCH(p) model is specified as follows:

yt = μ + ∈t

∈t = σt . zt

σt2 = α0 + α1∈t−12 + α2∈t−22 + … + αp∈t−p2

Here, yt  represents the return series, ∈t  is the error term, σt is the time-varying standard deviation, and zt is a white noise error term.

Implementing ARCH in EViews

To implement an ARCH model in EViews:

  1. Load the Data: Import your time-series data into EViews.
  2. Specify the Model: Use the Quick -> Estimate Equation feature and specify your return series.
  3. Choose ARCH Model: In the estimation settings, select ARCH and specify the order (p).
  4. Estimate Parameters: Run the estimation to obtain the model parameters.

The GARCH Model

Overview

The GARCH model, developed by Tim Bollerslev in 1986, extends the ARCH model by incorporating lagged values of the conditional variance itself, making it more flexible and capable of capturing persistent volatility.

Model Specification

The basic GARCH (p, q) model is specified as follows:

yt = μ + ∈t

∈t =  σt . zt

σt2 = α0 + α1∈t−12 + … + αp∈t−p2 + β1σt−12 + … + βqσt−q2 ?

Here, βi terms represent the lagged conditional variances.

Implementing GARCH in EViews

To implement a GARCH model in EViews:

  1. Load the Data: Import your time-series data into EViews.
  2. Specify the Model: Use the Quick -> Estimate Equation feature and specify your return series.
  3. Choose GARCH Model: In the estimation settings, select GARCH and specify the orders (p, q).
  4. Estimate Parameters: Run the estimation to obtain the model parameters.

Comparative Analysis (ARCH vs. GARCH)

1. Model Flexibility

  • ARCH: Good for capturing short-term volatility clustering. Although it makes computation easier, it can be slightly troublesome when it reaches high orders.
  • GARCH: Provides higher flexibility, performs well in terms of capturing long-term dependency persistence, and that makes it useful for financial time-series data.

2. Parameter Estimation

  • ARCH: It is easier to estimate because there is a smaller number of parameters involved.
  • GARCH: It has more parameters, which makes estimation more challenging but offers better capturing capabilities of volatility.

3. Predictive Accuracy

  • ARCH: It gives reasonable forecasts for short-term.
  • GARCH: Normally has a higher level of forecasting accuracy of long-term since it efficiently models persistent volatility.

Strategies for Handling Volatility Models

1. Model Selection

The choice of a model also depends on the nature of the data and the application of the model. ARCH model might be sufficient for simpler and consistent data while a GARCH model may be appropriate for complex financial data with apparent volatility clustering.

2. Model Diagnostics

Once the model is estimated, perform the following diagnostics:

  • Residual Analysis: Examine residuals for autocorrelation as well as heteroskedasticity.
  • Information Criteria: Apply AIC or BIC to compare models and to choose the best fit.
  • Out-of-Sample Forecasting: Assess the model’s predictive performance on out-of-sample data.

3. Practical Application

A student may face difficulties while working on data preprocessing, model selection, and result analysis. To overcome these factors, help of resources such as the EViews documentation, online tutorials, and research papers may be obtained.

Seeking EViews Expert Assistance for Assignments

1. Benefits of EViews Assignment Help

When students face challenges with EViews assignments, expert EViews homework support can be highly advantageous:

  • Expert Guidance: Our experts provide homework assistance on model estimation, diagnostics, and interpretation of results.
  • Time Efficiency: It is much simpler for our experts to resolve technical problems and implement the models correctly. It helps you save time on your assignments.
  • Learning Opportunity: Students can enhance their knowledge of volatility modelling and acquire deeper understanding by collaborating with our eviews experts.

2. How to get EViews Assignment Help

Visit our website tutorhelpdesk.com or visit this link https://www.tutorhelpdesk.com/homeworkhelp/Statistics-/Eviews-Assignment-Help.html to post your eviews assignment. We will evaluate your assignment and revert with a affordable price quote and complete your assignment with strict adherence to the instructions and rubric before the deadline. You may also register on our website for free to upload and manage your assignment orders.

Conclusion

ARCH and GARCH models in EViews are powerful tools for volatility modeling which helps in analyzing the financial time-series data. While ARCH models are simpler and suitable for short-term volatility, GARCH models provide greater flexibility and accuracy for long-term forecasting. Understanding these models and their applications and taking the assistance of eviews experts helps students succeed in their EViews tasks as well as improve their understanding of econometrics.

If you are wondering, how to do my eviews homework, then we have the right solutions and right experts to work for you in not only completing your assignments but also guide you how to perform the steps in eviews for a comprehensive support. We also offer free doubt clearing session to address all your queries related to the solution.

Recommended Textbooks for Beginners

  • Econometrics For Dummies by Roberto Pedace
  • A comprehensive guide that simplifies complex econometric concepts, making them accessible for beginners.

FAQs

1. What is volatility modeling in financial econometrics?

The purpose of volatility modeling is to estimate and examine the variability of the returns of the financial assets. It plays a strategic role in the identification of the risk and return relationship, that is crucial for portfolio management, risk evaluation, and in the pricing of derivatives.

2. How do I choose between ARCH and GARCH models?

It depends on the type of your data, as well as on the purpose of data analysis and others factors. ARCH type models are easier but efficient for the short-term volatility while GARCH type models are more flexible to measure the long run volatility patterns.

What are common challenges in volatility modeling?

  • Data Quality: Wrong model estimation can be caused due to wrong or incomplete data
  • Model Specification: Establishing the right model order and type is very important.
  • Parameter Estimation: Parameter estimation is in fact quite challenging, especially in the case of complicated models.

3. How can students benefit from EViews assignment help?

  • Expert Guidance: Our skilled individuals can offer an accurate insight into model estimation, diagnostics, and interpretation.
  • Time Efficiency: Technical problems can be fixed by the experts while guaranteeing the correct model application.
  • Enhanced Learning: Students are able to get better and more profound knowledge of volatility modeling when working with the qualified experts.

4. Can I use EViews for other types of econometric modeling?

Yes, EViews is an all-round software that can be used for different kinds of econometric modeling which are time series, panel data and cross section data analysis.


Jeremy Posted on 08-Aug-2024 23:37:00