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. 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 scattering, 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 of 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 is 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
Heteroskedastic refers to a condition in which the variance of ... 
Homoskedastic
Homoskedastic refers to a condition in which the variance of ... 
Variability
Variability is the extent to which data points in a statistical ... 
Coefficient of Determination
The coefficient of determination is a measure used in statistical ... 
Variable Cost Ratio
The variable cost ratio compares costs, which fluctuate depending ... 
Multiple Linear Regression  MLR
Multiple linear regression (MLR) is a statistical technique that ...

Trading
Improve your investing with Excel
Find out how to use Excel, a useful tool for assisting with investment organizations and evaluations. 
Trading
The Linear Regression of Time and Price
This investment strategy can help investors be successful by identifying price trends while eliminating human bias. 
Investing
Stock Market Risk: Wagging The Tails
The bell curve is an excellent way to evaluate stock market risk over the long term. 
Investing
Tips for investors in volatile markets
Market volatility is inevitable, trying to time the market is extremely difficult. One solution is to invest long term. Find out the best investment strategy to handle the market volatility. 
Investing
Bet Smarter With the Monte Carlo Simulation
This technique can reduce uncertainty in estimating future outcomes. 
Financial Advisor
Life Insurance: Variable Vs. Variable Universal
Do you know why you might need one policy versus the other? Read on to find out the difference between Variable and Variable Universal life insurance. 
Investing
Two Approaches to Building a LowRisk Portfolio
Building a portfolio consisting of lowrisk assets is achieved primarily by using one of two principal lowvolatility strategies. 
Investing
How Investment Risk Is Quantified
FInancial advisors and wealth management firms use a variety of tools based in modern portfolio theory to quantify investment risk. 
Investing
Most Common Probability Distributions
In this article, we'll go over a few of the most popular probability distributions and show you how to calculate them. 
Trading
Exploring the Exponentially Weighted Moving Average
Learn how to calculate a metric that improves on simple variance.

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 >> 
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 and determine ... Read Answer >> 
How is risk aversion measured in modern portfolio theory (MPT)?
Find out how risk aversion is measured in modern portfolio theory (MPT), how it is reflected in the market and how MPT treats ... Read Answer >> 
What is the difference between standard deviation and Zscore?
Understand the basics of standard deviation and Zscore, and learn how each is calculated and used in the assessment of market ... Read Answer >> 
How can I create a linear regression in Excel?
Learn the steps involved in creating a linear regression chart in Microsoft Excel. A linear regression is a data plot that ... Read Answer >> 
Is variance good or bad for stock investors?
Learn how high variance stocks are good for some investors and how diversified portfolios can reduce variance without compromising ... Read Answer >>