Autoregression and ARCH GARCH models

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In summary, the person is seeking advice on where to begin studying ARCH and GARCH models. They mention having a degree in Mathematics and Physics without coverage of statistics, but believe their understanding of basic stats and probability will suffice. They are provided with a link to a helpful resource.
  • #1
hayyan1
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Hello guys, I need to understand the ARCH and GARCH models and so I need some advice on where I should begin my study. In order to understand ARCH models, am I right to begin with studying autoregression? If so can you guys provide me with a link or book I should look into...

By the way, I have a masters degree in Mathematics and Physics, but I have not covered any statistics in my degree. This shouldn't be a problem, I don't think, because I know the basics of stats and probability.

Thanks
 
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Thanks Ill check that out
 

Related to Autoregression and ARCH GARCH models

1. What is autoregression?

Autoregression is a statistical model used to analyze time series data, where the current value of a variable is dependent on its past values. It is based on the assumption that there is a linear relationship between the current value and a certain number of previous values.

2. What is the difference between autoregression and ARCH GARCH models?

Autoregressive models only consider the past values of a variable, whereas ARCH GARCH models also take into account the volatility of the variable. ARCH GARCH models are used to model and forecast the volatility of a time series, while autoregressive models are used for forecasting the actual values of the series.

3. How are ARCH GARCH models useful in financial analysis?

ARCH GARCH models are commonly used in financial analysis to predict and manage volatility in stock prices, exchange rates, and other financial indicators. They take into account the volatility clustering and time-varying nature of financial data, making them more accurate in forecasting compared to traditional models.

4. What are the limitations of autoregression and ARCH GARCH models?

Autoregression assumes a linear relationship between past and present values, which may not always be the case in real-world data. ARCH GARCH models also have limitations in their ability to accurately predict extreme events or sudden shifts in volatility. Additionally, they are prone to overfitting and may require a large amount of data for accurate predictions.

5. How are autoregression and ARCH GARCH models estimated?

Autoregressive models are estimated using techniques such as ordinary least squares (OLS) or maximum likelihood estimation (MLE). ARCH GARCH models are estimated using the method of maximum likelihood, which involves iteratively estimating model parameters until the likelihood of the data is maximized. This can be done using specialized software packages or programming languages such as R or Python.

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