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.

Firstly, we employ a three-state hidden semi-Markov model (HSMM) to explain the time-varying distribution of the Chinese stock market returns. Our results indicate that the time-varying distribution depends on the hidden states, represented by three market conditions, namely the bear, sidewalk, and bull markets. Secondly, we further employ the three-state HSMM to the daily returns of the Chinese stock market and seven developed markets. Through the comparison, three unique characteristics of the Chinese stock market are found, namely “Crazy Bull”, “Frequent and Quick Bear”, and “No Buffer Zone”. Thirdly, we propose a new diffusion process referred to as the ``camel process'' to model the cumulative return of a financial asset. Its steady state probability density function could be unimodal or bimodal, depending on the sign of the market condition parameter. The overreaction correction is realised through the non-linear drift term. Lastly, we take the tools in functional data analysis to understand the term structure of Chinese commodity futures and forecast their log returns at both short and long horizons. The FANOVA has been applied to examine the calendar effect of the term structure. An h-step functional autoregressive model is employed to forecast the log return of the term structure.

This thesis decomposes the UK market volatility into short- and long-run components using the EGARCH component model and examines the cross-sectional prices of the two components. The empirical results suggest that these two components are significantly priced in the cross-section and the negative risk premia are consistent with the existing literature. However, the ICAPM model in this paper using market excess return and two volatility components as state variables is inferior to the traditional three-factor model. Therefore, investor sentiment is augmented to the EGARCH component model to analyse the impacts of sentiment on market excess return and the components of market volatility. Bullish sentiment leads to higher market excess return while bearish sentiment leads to lower excess return. The sentiment-augmented EGARCH component model compares favourably to the original EGARCH component model which does not take investor sentiment into account. The sentiment-affected volatility components are significantly negatively priced in the cross-section. This paper explores the cross-sectional impacts of market sentiment on stock returns and reveals that the sensitivities of investor sentiment vary monotonically with certain firm characteristics in the cross-section. The analysis suggests that investor sentiments forecast the returns of portfolios that consist of buying stock with high values of a characteristic and selling stock with low values. A sentiment risk factor is constructed to capture the average return differences between stocks most exposed to sentiment and stocks least exposed to sentiment. The two-stage Fama-MacBeth procedure suggests that the sentiment risk factor is significantly priced in the cross-section.

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This thesis consists of three essays and empirically studies the behaviour of emerging and frontier stock markets against instability in the commodity and international financial markets. The first essay considers symmetric and asymmetric dynamic conditional correlation multivariate GARCH models to examine the correlations between the Gulf Cooperation Council (GCC) stock markets and the Brent and OPEC crude oil price indices and to gauge the oil shocks effect on the dynamics of the GCC stock markets. The analysis uses weekly data covering the period December 31st, 2003 to December 27th, 2012. The results show that: (i) two of the GCC stock markets are asymmetrically correlated with both the Brent and OPEC crude oil price indices and only two are symmetrically correlated with Brent oil; (ii) all the GCC stock markets exhibit positive and symmetric conditional correlations overtime and these correlations are more pronounced during periods of high oil price fluctuations. The second essay investigates the contagion effect and volatility spillovers from the U.S. financial, the Dubai and the European debt crises to the GCC stock markets, with particular focus on financial and non-financial sectors. It uses weekly data for the period December 31st, 2003 to January 28th, 2015 and applies GARCH models and indicators of crisis. The empirical results show that: i) contagion effects are present on some of the GCC stock markets and are more pronounced during the U.S. financial and Dubai debt crises, with a larger impact on financial sectors; ii) there is significant evidence of volatility spillovers from the financial sectors of the U.S., European and Dubai stock markets to some of the GCC sectors considered, even though spillovers are rather weak in magnitude. The last essay investigates the extent to which the GCC stock markets are correlated and integrated with those of the Asian countries. The analysis is carried out using the Johansen cointegration approach, the dynamic conditional correlation (DCC) GARCH model, and a standard correlation analysis based on a rolling window estimation scheme. The sample period of the analysis spans from December 31st, 2003 to September 30th, 2015. The empirical analysis offers three main results. First, there is a relatively moderate evidence of cointegration among some of the GCC and Asian stock markets particularly with of those of strong economic linkages among them. Second, evidence of time-varying correlation is found in some cases, while not large in magnitude, and shocks to volatility are highly persistence. Third, stock returns show a common trend exists, only during the global financial crisis.

This thesis has developed appropriate dynamic models to shed more light on how pervasive innovation (e.g., equity-for-guarantee swaps) in entrepreneurship/hedge funds could alleviate the severe financing constraints for entrepreneurs/hedge fund managers who plan to launch new businesses. Chapter 2 considers a risk-averse entrepreneur who invests in a project with idiosyncratic risk and takes debt financing via equity-for-guarantee swaps for diversification benefits. In contrast to the literature, we assume the entrepreneur is unable to get a loan from a bank directly because of the low credibility of the entrepreneur and lack of collateral and therefore, an innovative financial contract, equity-for-guarantee swaps, is signed among a bank, an insurer, and the entrepreneur. The new swap not only solves the serious problems of widespread financing constraints, but also significantly improves the welfare level of the entrepreneur. Chapter 3 develops a new financial derivative product called fees-for-guarantee swap to alleviate financing constraints of ESFs managers as well as mitigate the manager’s risk shifting behaviour. Numerical results indicate that the incentive compensation, managerial ownership and the possibility of fund liquidation significantly mitigate the manager’s risk shifting incentive. In Chapter 4, a dynamic valuation model of the hedge fund seeding business has been built to study the consumption and portfolio choice problem for a risk-averse manager who launches a hedge fund via a seeing vehicle. This vehicle, i.e. fees-for-seed swap, specifies that a strategic partner (seeder) provides a critical amount of capital in exchange for participation in the funds revenue. If structured properly this seeding vehicle could lead to Pareto improvement, as it alleviates the ESFs manager’s financial constraint, helps seeder get high potential return for good performance and ordinary investors are more willing to invest in funds backed up by seeding investment.

This thesis explores the asset pricing implication of higher moments of return distributions on the UK stock market. It is found that in a market populated by risk-averse, prudent and temperate investors, firms whose returns exhibit negative coskewness (CSK) or positive cokurtosis (CKT) yield higher premia relative to counterpart firms with positive coskewness and negative cokurtosis respectively. Furthermore, results show that CSK and CKT are genuinely priced in the UK stock markets and outperform the covariance risk, size, value, and momentum factors in explaining the expected cross-sectional variation in asset returns. It is further found that a theoretically motivated, higher co-moment asset pricing model has a significant explanatory ability over the cross-section of CSK and CKT portfolio returns. A CSK- and CKT-augmented CAPM performed better in explaining the cross-sectional variation in expected returns as compared to empirically-motivated asset pricing models, such as the three-factor model (Fama-French 1996) and the four-factor model (Carhart 1997). In particular, a unit factor loading of CSK risk yields a statistically significant monthly premium of 0.22% (2.64% p.a.) across CSK portfolios and a unit factor of CKT risk 0.15% (1.8% p.a.) across the CKT portfolios. Motivated by the significance of CSK and CKT, this thesis also explores whether higher co-moments of asset returns can explain the profitability of a number of investment strategies; size, value, asset growth, accruals, dividend yield, net stock issue and momentum. In particular, this study shows that in the UK stock market, firms with low asset growth and net-stock issue have higher subsequent stock returns compared to counterpart firms with high asset growth and net-stock issue. Furthermore, the performance of the above investment strategies continues even if their portfolio returns are adjusted for risk factors such as size, value, and momentum of the Fama-French three-factor (1993) and Carhart four-factor (1997) models. The introduction of CSK and CKT factor loadings into commonly used asset pricing models shows a slight decrease in the profitability of size, value, asset growth and net-stock issue but returns remain significantly positive. However, CSK and CKT factor loadings have no impact on the profitability of momentum, dividend yield and accruals strategies. Overall, risk-adjusted performance of the above investment strategies remains intact in the UK stock markets during 1990 to 2008. The use of higher moments is suggested when exploring risk-adjusted returns.

This thesis explores a selection of the ways in which an era of high mineral commodity prices - commonly dubbed the 'super-cycle' of the 2000s - is reshaping the map of global commodity markets. It pursues this agenda through three research aims: (1) to recast the relationship between geophysical resource supply, prices, and markets; (2) to examine some of the institutions that channel and benefit from resource wealth; and (3) to 'open the black box' of the commodity price formation process. The thesis pursues this agenda through four substantive papers, each with its own set of research objectives and findings, and primarily uses the example of phosphate as a vehicle for discussion. The first half of the thesis focuses on the production side of commodity markets. It begins by exploring the multidimensional nature of the concept of resource scarcity, both in its geophysical and socioeconomic aspects, by interrogating a prominent inherited conception of natural resource scarcity: 'peak' natural resources, specifically peak phosphorus discourses (chapter 3). The thesis then carries on the research agenda suggested by this initial study by conducting a field research-based case study of the little-known Moroccan state-owned phosphate mining and fertilizer company, OCP Group (chapter 4). It explores the particular type of principal-agent problem in generating and distributing national resource wealth that national extractive companies (NECs) such as OCP face. The second substantive half of the thesis is concerned with global commodity trading and price formation. It constructs an 'anatomy' of global phosphate markets in order to shed light on the phosphate price formation process (chapter 5). Based on this investigation, the thesis argues that despite the opacity of the processes by which phosphate is priced, an apparent lack of a 'benchmark' or reference price is not necessarily as problematic as market theorists might assume. Finally, the thesis takes a macro-level perspective of the relationship between finance and physical commodity trade by examining the role of financial trading in the governance of commodity markets (chapter 6). Overall the thesis distils the following findings. To begin with, a deeper and more nuanced understanding of the concept of resource scarcity puts short-term price movements as indicators of resource availability into perspective while revealing an unforeseen degree of complexity, as well as certain 'blind spots', in the geopolitical and institutional aspects of resource supply and trading. Second, the power of two particular, less-researched types of institutions that channel and benefit from resource wealth - names, national extractive companies and financial investors - is both less great and different in nature than is commonly assumed. Third, for institutional as well as geographic reasons that are specific to different types of commodities, the commodity price formation process is even further from the joint ideals of market transparency and liquidity than is commonly assumed. Finally, insofar as commodity production and trade can be conceived as part of the 'real economy', it cannot succumb to what is widely feared as the hegemony of 'financial logic'.

Part I: This chapter develops a lattice method for option evaluation aiming to investigate whether the option prices reflect the shifts in the distributions of the underlying asset returns and the risk-free interest rate. More precisely we try to investigate whether the option prices reflect the switches in the correlation between the underlying and risk-free bond returns that characterise different states of the economy. For this reason we develop and test two models. In the first model we allow all the parameters to follow a regime-switching process while in the second model, in order to isolate the regime-switching correlation effect on the option prices, we allow only the correlation to follow a regime-switching process. The models developed use pentanomial lattices to represent the evolution of the regime-switching underlying assets. Our findings suggest that the option prices reflect the regime-switches and that a model which considers these switches could produce more accurate results than a single-regime model. Part II: This part develops a class of closed-form models for options on commodities evaluation under the assumptions of mean-reversion in the commodity prices and factors’ values and regime-switching in the volatilities and correlations. At first we develop novel closed-form solutions of the 1-, 2- and 3-factors models and later in the paper these three models are transformed into regime switching models. The six models (three with and three without regime-switching) are then tested and compared on real market data. Our findings suggest that the by increasing the stochastic factors and assuming regime-switching in the models their flexibility and thus their accuracy increases.

The aim of this research is to investigate the long-run co-integrating relationships in the Asian markets. Our research focuses on 4 areas; pair trading, out-of-sample forecasting, testing the unbiased forward exchange rate hypothesis and testing the expectation hypothesis of the term structure of interest rates. The introduction is provided in chapter one. In chapter two, we develop a pairs trading strategy using individual stocks listed in the Stock Exchange of Thailand. Engle and Granger approach is used to identify the potential pairs that are cointegrated. The results show that pairs trading strategy is profitable in this market. Chapter three examines the forecasting performance of the error correction model on daily share price series from the Stock Exchange of Thailand. The disequilibrium term is classified into “correct” and “mix” sign based on Alexander (2008)’s criterion; the results indicate that the error correction component can help to improve the predictability in the long run. Chapter four tests the unbiased forward rate hypothesis of 11 Asian exchange rates using linear conventional regression, ECM and logistic smooth transition regression with the forward premium as the transition variable. Out-of-sample forecasting results also suggest that inferior forecasting performance could be obtained as a result of using linear models. In chapter five, we investigate the expectation hypothesis of the term structure of interest rate for four Asian countries. We employ linear models and nonlinear approaches that allow to capture asymmetric and symmetric adjustments. The result also indicates that the term structure can be better modeled by means of LSTR models. The forecasting exercise also confirms these findings.