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Autoregressive Conditional Heteroskedasticity (ARCH) Explained
Autoregressive conditional heteroskedasticity is a time-series statistical model used to analyze volatility in high frequency data.
Heteroscedasticity Definition: Simple Meaning and Types Explained
In statistics, heteroskedasticity happens when the standard deviations of a variable, monitored over a specific amount of time, are nonconstant.
GARCH Model: Definition and Uses in Statistics
Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) is a statistical model used to estimate the volatility of stock returns.
What Is the GARCH Process? How It's Used in Different Forms
The generalized autoregressive conditional heteroskedasticity (GARCH) process is an econometric term used to describe an approach to estimate volatility in financial markets.
Heteroskedastic refers to a condition in which the variance of the residual term, or error term, in a regression model varies widely.
Calculating Volatility: A Simplified Approach
Though most investors use standard deviation to determine volatility, there's an easier and more accurate way of doing it: the historical method.
Homoskedastic: What It Means in Regression Modeling, With Example
Homoskedastic refers to a condition in which the variance of the error term in a regression model is constant.
Robert F. Engle III Definition
Robert Engle III is an American economist who won the 2003 Nobel Prize in Economics for his analysis of time-series data with time-varying volatility.
Error Term: Definition, Example, and How to Calculate With Formula
An error term is a variable in a statistical model when the model doesn't represent the actual relationship between the independent and dependent variables.
Stochastic Volatility (SV)
Stochastic volatility assumes that the price volatility of assets varies and is not constant over time, which is erroneously assumed by the Black Scholes model.