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Definition of 'AutoregressiveConditional Heteroskedasticity- ARCH'An econometric term used for observed time

series. ARCH models are used to modelfinancial time series with time-varyingvolatility, such as stock prices. The ARCH

concept was developed by economist Robert

. !ngle, for which he won the "##$ %obel&emorial 'ri(e in !conomic )ciences.

Investopedia explains

'Autoregressive Conditional

Heteroskedasticity - ARCH'ARCH models assume that the variance of

the current error term is related to the si(e

of the previous periods* error terms, giving

rise to volatility clustering. This phenomenon

is widely observable in financial markets,

where periods of low volatility are followed

by periods of high volatility and vice versa.

or e+ample, volatility for the )' ## was

unusually low for an e+tended period during

the bull market from "##$ to "##, before

spiking to record levels during the market

correction of "##/. ARCH models have

become mainstays of arbitrage pricing and

portfolio theory.

Definition of 'Generalized

AutoRegressive Conditional

Heteroskedasticity (GARCH'

A statistical model used by financial

institutions to estimate the volatility of

stock returns. This information is used by

banks to help determine what stocks will

potentially provide higher returns, as well

as to forecast the returns of current

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investments to help in the budgeting

process.

Investopedia explains

'Generalized AutoRegressiveConditional Heteroskedasticity(GARCH'There are many variations of 0ARCH,

including %0ARCH to include correlation, and

10ARCH which restricts the volatilityparameter. !ach model can be used to

accomodate the specific 2ualities of the stock,industry or economic state.

In statisticsand signal processing, an autoregressive(AR) modelis a representation of a type

of random process; as such, it describes certain time-varying processes in nature, economics,etc. Theautoregressive model specifies that the outputvariable depends linearlyon its own previous values. It is a

special case of the more generalA!Amodel of time series.

Definition of 'Heteroskedasticity'1n statistics, when the standard deviations of a variable,

monitored over a specific amount of time, are non-constant.Heteroskedasticity often arises in two forms, conditional and

unconditional. Conditional heteroskedasticity identifies non-constant volatility when future periods of high and low

volatility cannot be identified. 3nconditionalheteroskedasticity is used when futures periods of high and

low volatility can be identified.

Investopediaexplains'Heteroskedasticity'1n finance, conditional

heteroskedasticity often isseen in the prices of stocks

and bonds. The level ofvolatility of these e2uities

cannot be predicted overany period of time.

3nconditionalheteroskedasticity can be

used when discussingvariables that have

identifiable seasonal

http://en.wikipedia.org/wiki/Statisticshttp://en.wikipedia.org/wiki/Statisticshttp://en.wikipedia.org/wiki/Signal_processinghttp://en.wikipedia.org/wiki/Random_processhttp://en.wikipedia.org/wiki/Natural_sciencehttp://en.wikipedia.org/wiki/Economicshttp://en.wikipedia.org/wiki/Economicshttp://en.wikipedia.org/wiki/Linear_predictionhttp://en.wikipedia.org/wiki/Linear_predictionhttp://en.wikipedia.org/wiki/Autoregressive%E2%80%93moving-average_modelhttp://en.wikipedia.org/wiki/Autoregressive%E2%80%93moving-average_modelhttp://en.wikipedia.org/wiki/Time_serieshttp://en.wikipedia.org/wiki/Time_serieshttp://en.wikipedia.org/wiki/Statisticshttp://en.wikipedia.org/wiki/Signal_processinghttp://en.wikipedia.org/wiki/Random_processhttp://en.wikipedia.org/wiki/Natural_sciencehttp://en.wikipedia.org/wiki/Economicshttp://en.wikipedia.org/wiki/Linear_predictionhttp://en.wikipedia.org/wiki/Autoregressive%E2%80%93moving-average_modelhttp://en.wikipedia.org/wiki/Time_series

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variability, such as

electricity usage.