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A
univariate stochastic process X is said to be
homoskedastic if
standard deviations of terms
are
constant for all times t. Otherwise, it is said to be
heteroskedastic. This is illustrated
with realizations of two stochastic processes in Exhibit 1.
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Realizations of two processes are
indicated. The first exhibits homoskedasticity. The second exhibits
heteroskedasticity. |
Heteroskedasticity is an important concept in finance because asset
returns in the capital, commodity and energy
markets often exhibit heteroskedasticity.
Heteroskedasticity can take two forms. A process is
unconditionally heteroskedastic
if unconditional standard deviations are not
constant. It is conditionally
heteroskedastic if conditional standard deviations are
not constant (see the
notation conventions documentation).
For example, stock or
bond returns tend to be conditionally heteroskedastic.
The prices exhibit non-constant volatility,
but periods of low or high volatility are generally not known in advance.
New England electricity prices, on the other hand, exhibit unconditional heteroskedasticity. The prices tend to have higher volatilities during the
Summer than during other seasons. This is predictable, so the electricity
prices exhibit unconditional heteroskedasticity.
If a process is unconditionally heteroskedastic, then it is necessarily
conditionally heteroskedastic. The converse is not true. If a process is
not unconditionally heteroskedastic or not conditionally heteroskedastic, it
is said to be unconditionally
homoskedastic or
conditionally homoskedastic, respectively.
All these notions extend to higher dimensions, A
multivariate stochastic
process X is said to be homoskedastic if its
covariance matrix is constant for all times
t, etc.
In finance, a variety of models are used for conditionally heteroskedastic
processes. These include
autoregressive
conditional heteroskedastic (ARCH)
models;
generalized ARCH (GARCH)
models
regime-switching models; and
stochastic volatility models.
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