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Title: Econometric forecasting of financial assets using non-linear smooth transition autoregressive models
Authors: Clayton, Maya
Supervisors: McMillan, David G.
Keywords: Econometric forecasting
STAR model
Error-correction model
Non-linear predictability
House price returns
Asymmetric non-linear dynamics
Non-linear stationarity
Issue Date: 23-Jun-2011
Abstract: Following the debate by empirical finance research on the presence of non-linear predictability in stock market returns, this study examines forecasting abilities of nonlinear STAR-type models. A non-linear model methodology is applied to daily returns of FTSE, S&P, DAX and Nikkei indices. The research is then extended to long-horizon forecastability of the four series including monthly returns and a buy-and-sell strategy for a three, six and twelve month holding period using non-linear error-correction framework. The recursive out-of-sample forecast is performed using the present value model equilibrium methodology, whereby stock returns are forecasted using macroeconomic variables, in particular the dividend yield and price-earnings ratio. The forecasting exercise revealed the presence of non-linear predictability for all data periods considered, and confirmed an improvement of predictability for long-horizon data. Finally, the present value model approach is applied to the housing market, whereby the house price returns are forecasted using a price-earnings ratio as a measure of fundamental levels of prices. Findings revealed that the UK housing market appears to be characterised with asymmetric non-linear dynamics, and a clear preference for the asymmetric ESTAR model in terms of forecasting accuracy.
Type: Thesis
Publisher: University of St Andrews
Appears in Collections:Management Theses

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