This thesis contributes to the ongoing discussion about the financial and statistical modelling of returns on financial stock markets. It develops the asset pricing model concept which has received continuous attention for almost 50 years in the area of finance, as a method by which to identify the stochastic behaviour of financial data when making investment decisions, such as portfolio choices, and determining market risk. The best known and most widely used asset pricing model detailed in the finance literature is the Two-Moment Capital Asset Pricing Model (CAPM) (consistent with the Linear Market Model), which was developed by Sharpe-Lintner- Mossin in the 1960s to explore systematic risk in a mean-variance framework and is the benchmark model for this thesis. However, this model has now been criticised as misleading and insufficient as a tool for characterising returns in financial stock markets. This is partly a consequence of the presence of non-normally distributed returns and non-linear relationships between asset and market returns. The inadequacies of the Two-Moment CAPM are qualified in this thesis, and the extensions are proposed that improve on both model fit and forecasting abilities. To validate and extend the benchmark Linear Market Model, the empirical work presented in this thesis centres around three related extensions. The first extension compares the Linear Market Model’s modelling and forecasting abilities with those of the time-varying Linear Market Model (consistent with the conditional Two-Moment CAPM) for 19 Turkish industry sector portfolios. Two statistical modelling techniques are compared: a class of GARCH-type models, which allow for non-constant variance in stock market returns, and state space models, which allow for the systematic covariance risk to change linearly over time in the time-varying Linear Market Model. The state space modelling is shown to outperform the GARCH-type modelling. The second extension concentrates on comparing the performance of the Linear Market Model, with models for higher order moments, including polynomial extensions and a Generalised Additive Model (GAM). In addition, time-varying versions of the Linear Market Model and polynomial extensions, in the form of state space models, are considered. All these models are applied to 18 global markets during three different time periods: the entire period from July 2002 to July 2012, from July 2002 to just before the October 2008 financial crisis, and from after the October 2008 financial crisis to July 2012. Although the more complex unconditional models are shown to improve slightly on the Linear Market Model, the state space models again improve substantially on all the unconditional models. The final extension focuses on comparing the performance of four possible multivariate state space forms of the time-varying Linear Market Models, using data on the same 18 global markets, utilising correlations between markets. This approach is shown to improve further on the performance of the univariate state space models. The thesis concludes by drawing together three related themes: the inappropriateness of the Linear Market Model, the extent to which multivariate modelling improves the univariate market model and the state of the world’s stock markets.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:631016 |
| Date | January 2014 |
| Creators | Neslihanoglu, Serdar |
| Publisher | University of Glasgow |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://theses.gla.ac.uk/5658/ |
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