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7/27/2019 Definition o1
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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
7/27/2019 Definition o1
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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
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variability, such as
electricity usage.