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Heteroskedasticity and Autocorrelation | Quantitative Trading Strategies and Models

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https://www.youtube.com/watch?v=SOI-9vNV2VM
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112
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Video from the course Quantitative Trading Strategies and Models https://quantra.quantinsti.com/course/quantitative-trading-strategies-models ***START FOR FREE*** Welcome to this video lecture! The objective of this video lecture, is to cover the required concepts for understanding the ARIMA and GARCH models. The concepts that we will learn in this video lecture are as follows, 1.Heteroskedasticity, 2.Serial Correlation or Autocorrelation. Let us begin with heteroskedasticity. If we correctly recall, one of the assumptions of linear regression is that the variance of its errors is constant across all the observations of the financial data. In other words, we can say that the errors are homoskedastic. This graph shows the values of the dependent and independent variables and a fitted regression line with homoskedastic errors. These errors or residuals are the vertical lines between the plotted or actual points and the fitted regression line or forecasted points. However, in heteroskedasticity errors are not constant. You may look at the difference between the two graphs. Unconditional heteroskedasticity occurs when the variance in errors is not correlated with independent variables or in other words error variance does not systematically increase or decrease with the changes in the values of independent variables. Though this violates the assumption, it is not statistically significant and causes no major problems while forecasting variables using regression analysis. On the o
Rating
5
Name
Quantra
Date
2022/05/11
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