Page 57 - FINAL CFA II SLIDES JUNE 2019 DAY 3

LOS 9.l: Explain how to test and correct for seasonality in
a time-series model and calculate and interpret a READING 9: TIME SERIES ANALYSIS
forecasted value using an AR model with a seasonal lag.
Module 9.4: Seasonality
Forecasting with an AR Model with a Seasonal Lag

EXAMPLE: Forecasting with an autoregressive model: Based on the regression results from the previous example and the
occupancy levels over the past year (presented below), forecast the level of hotel occupancy for the first quarter of 2016.

LOS 9.m: Explain autoregressive conditional heteroskedasticity (ARCH)
and describe how ARCH models can be applied to predict the variance of a
time series.
This exists if in a single time series AR model, the variance of the residuals in
one period is dependent on the variance of the residuals in a previous period.
When this occurs SE of the regression coefficients and their hypothesis tests are
invalid.

ARCH Models are used to test for ARCH: the squared residuals are regressed
on the first lag of the squared residuals:

If the coefficient, a , is statistically different from zero, the time series is
1
ARCH(1) –contains ARCH errors.

Run regression procedures that correct for heteroskedasticity
(generalized least squares) to develop a predictive model.