Energy Price Forecasts and the Effect of Oil Price Shocks on the Stock Market in Selected Countries
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In this dissertation, I construct different oil prices forecasting models and
estimate them with 128 monthly indicators that tell a complex macroeconomic
story of the US economy from 19592020. This dissertation deals with presenting
and estimating different oil price forecasting models and studying the relationship
between stock market returns in some countries and the oil price changes. The
study divides the forecasting models into linear and nonlinear models. In linear
models, we consider some traditional time series models, such as random walk,
autoregressive (AR), moving average (MA), and autoregressive integrated moving
average (ARIMA) to forecast oil prices. The dissertation considers using principal
component analysis (PCA) and partial least squares (PLS) to extract factors to
be used in addition to the ARIMA process (ARIMAX).