ARIMA and GARCH Models

ARIMA AND GARCH MODELS 1
ARIMA and GARCH Models
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ARIMA AND GARCH MODELS 2
ARIMA MODEL
An autoregressive integrated moving average mainly denoted by the initials ARIMA is a
model of regression analysis in statistics that utilizes time series data to predict the future trends
of an event or simplify data set for easy understanding. The model gauges the power of
dependent variables relative to other independent variables (Cadenas et al, 2016, p.109). ARIMA
model has found its extensive application in the prediction of the future of financial markets and
securities. This is achieved through the examination of the differences realized when series
values are compared instead of the actual values. ARIMA model is composed of three
components which must be understood in order to understand how the model works. The three
components are Autoregression (AR), Integrated (I) and Moving average (MA).
The Autoregression shows the changing variables that regress on their own lagged values
while the Integrated component represents the differences in the raw observations to transform
time series into stationery. A lively example can be seen in calculations where data values are
replaced by the difference between the current data values and previous values. The last
component is the Moving average, which simplifies the dependency between observational and
residual errors from moving average models which are applied in lagged observations. Each of
the three components of the ARIMA model works as standard notation parameter (Kumar and
Vanajakshi, 2015, p.21). A standard notion in this model has p, d, and q, where the integers
substitute for parameters to denote the ARIMA model used. Each of the three parameters: p, d,
and q have their own definition. For instance, p denotes the number of lag observations in the
model while d denotes the number of times each raw observation is differenced. The last
parameter, q, denotes the size of a moving average window