What is 'Heteroskedasticity'
Heteroskedasticity, in statistics, is when the standard deviations of a variable, monitored over a specific amount of time, are nonconstant. Heteroskedasticity often arises in two forms: conditional and unconditional. Conditional heteroskedasticity identifies nonconstant volatility when future periods of high and low volatility cannot be identified. Unconditional heteroskedasticity is used when futures periods of high and low volatility can be identified.
BREAKING DOWN 'Heteroskedasticity'
In finance, conditional heteroskedasticity is often seen in the prices of stocks and bonds. The level of volatility of these equities cannot be predicted over any period of time. Unconditional heteroskedasticity can be used when discussing variables that have identifiable seasonal variability, such as electricity usage.As it relates to statistics, heteroskedasticity, also spelled heteroscedasticity, refers to the error variance, or dependence of scatter, within a minimum of one independent variable within a particular sample. These variations can be used to calculate the margin of error between data sets, such as expected results and actual results, as it provides a measure for the deviation of data points from the mean value.
For a dataset to be considered relevant, the majority of the data points must be within a particular number of standard deviations from the mean as described by Chebyshevâ€™s theorem, also known as Chebyshevâ€™s inequality. This provides guidelines regarding the probability of a random variable differing from the mean. Based on the number of standard deviations specified, a random variable has a particular probability of existing within those points. For example, it may be required that a range of two standard deviations contain at least 75% of the data points to be considered valid. A common cause of variances outside the minimum requirement are often attributed to issues of data quality.
Unconditional Heteroskedasticity
Unconditional heteroskedasticity is predictable, and most often relates to variables that are cyclical by nature. This can include higher retail sales reported during the traditional holiday shopping period, or the increase in air conditioner repair calls during warmer months.
Changes within the variance can be tied directly to the occurrence of particular events or predictive markers if the shifts are not traditionally seasonal. This can be related to an increase in smartphone sales with the release of a new model as the activity is cyclical based on the event but not necessarily determined by the season.
Conditional Heteroskedasticity
Conditional heteroskedasticity is not predictable by nature. There is no telltale sign that leads analysts to believe data will become more or less scattered at any point in time. Often, financial products are considered subject to conditional heteroskedasticity as not all changes can be attributed to specific events or seasonal changes.

Heteroskedastic
A measure in statistics that refers to the variance of errors ... 
Generalized AutoRegressive Conditional ...
A statistical model used by financial institutions to estimate ... 
Variability
The extent to which data points in a statistical distribution ... 
Standard Error
The standard deviation of the sampling distribution of a statistic. ... 
Residual Standard Deviation
A statistical term used to describe the standard deviation of ... 
Sensitivity Analysis
Sensitivity analysis is a technique used to determine how different ...

Investing
A Simplified Approach To Calculating Volatility
Volatility is sometimes greater than anticipated, but the way itâ€™s measured can compound the problems that occur when itâ€™s unexpected. 
Trading
A Simplified Approach To Calculating Volatility
Though most investors use standard deviation to determine volatility, there's an easier and more accurate way of doing it. 
Managing Wealth
The Uses And Limits Of Volatility
Check out how the assumptions of theoretical risk models compare to actual market performance. 
Markets
Explaining the Empirical Rule
The empirical rule provides a quick estimate of the spread of data in a normal statistical distribution. 
Markets
Understanding Regression
Regression is a statistical analysis that attempts to predict the effect of one or more variables on another variable. 
Markets
Stock and Flow Variables Explained: A Closer Look at Apple
The difference between stock and flow variables is an essential concept in finance and economics. We illustrate with financial statements from Apple Inc. 
Trading
Trading With Gaussian Models Of Statistics
The entire study of statistics originated from Gauss and allowed us to understand markets, prices and probabilities, among other applications. 
Trading
What's a Sensitivity Analysis?
Sensitivity analysis is used in financial modeling to determine how one variable (the target variable) may be affected by changes in another variable (the input variable). 
Managing Wealth
Redefining Investor Risk
Changing the way you think about time and risk can change the way you invest. 
Investing
Using Historical Volatility To Gauge Future Risk
Use these calculations to uncover the risk involved in your investments.

How far back in a stock's history should you go when gauging its volatility?
Discover why it can be difficult for investors to figure out how far back to go into a stock's history when gauging its volatility. Read Answer >> 
What is the difference between standard deviation and average deviation?
Understand the basics of standard deviation and average deviation, including how each is calculated and why standard deviation ... Read Answer >> 
What is the difference between variance and standard deviation?
Explore the differences between standard deviation and variance. Learn more about how statisticians use these two concepts. Read Answer >> 
How is standard deviation used to determine risk?
Understand the basics of calculation and interpretation of standard deviation and how it is used to measure risk in the investment ... Read Answer >> 
What is the difference between standard deviation and mean?
Understand the basics of calculating and interpreting mean and standard deviation and how these mathematical fundamentals ... Read Answer >> 
What does standard deviation measure in a portfolio?
Dig deeper into the investment uses of, and mathematical principles behind, standard deviation as a measurement of portfolio ... Read Answer >>