The competitive implications of intra-industry diversification (chapter 2), the firm-investor network (chapter 3) and resource acquisition across the firm boundary (chapter 4) are of central concern to this thesis. The analysis draws on qualitative evidence, a quantitative, large-scale, longitudinal panel dataset (chapters 2 and 3), as well as conceptual reasoning (chapter 4). The empirical setting of the analysis is the hedge fund industry, which is characterised by small, entrepreneurial and knowledge-intensive firms. Whereas the three main chapters of this thesis are constructed and presented as stand-alone papers, three overarching insights emerge. First, intra-industry diversification impacts firm performance and firm survival in non-trivial ways. Whereas the positive effect of intraindustry diversification on survival seems to be driven by the risk reducing effect of beyond sub-sector diversification within an industry, the negative effect on performance seems to be driven by within sub-sector diversification, which may create limited value for investors. Second, the relationship of investment firms and their customers (i.e. investors) is much more multifaceted than the view of ‘investors as passive providers of capital’ would suggest. The analysis provides evidence for a performance and survival enhancing impact of the firminvestor network on the hedge fund firm. Third, moderation of complexity via a differential flow of resources is an important, yet underappreciated attribute of the firm boundary, which may contribute to the creation of resource asymmetries as a basis for competitive advantage. Although the evidence presented in this thesis is based on the empirical setting of the hedge fund industry, the findings on intra-industry diversification, the firm investor network and resource acquisition across the firm boundary may generalise to other firms in the (financial) services industry.

This thesis studies several practical issues in asset pricing, including MCMC estimation of time-changed Lévy processes, calibration techniques for stochastic volatility models, and a sampling scheme for the SABR model. First, a MCMC estimation approach is developed to estimate time-changed Lévy processes. Simulation-based experiments demonstrate good accuracy of the MCMC approach. An empirical study on its fitness of the return dynamics is provided, which shows that time-changed Lévy models can achieve excellent performance in capturing index returns. Second, a further study on MCMC estimation is applied to multivariate Lévy processes, in order to evaluate the efficiency and accuracy of the Bayesian technique for high-dimensional portfolio theory. Last, a new representation of the SABR model is proposed by adopting a coupling approach, based on which, the uncorrelated SABR is sampled from its density. Numerical experiments are implemented to compare the sampling scheme with the Euler discretization scheme and examine the accuracy of Hagan’s popular formula for the implied Black-Scholes volatility.

This thesis extends the empirical asset pricing literature by testing whether alternative specifications of Fama and French’s (1993) three-factor and Carhart’s (1997) four-factor models capture size, value and momentum anomalies. Specifically, the alternative models tested include the modified and index-based models of Cremers et al. (2013) and decomposed models of Fama and French (2012). This thesis investigates international stock returns and whether asset pricing models are integrated across four countries, namely the US, UK, Japan, and Canada. Finally, the information content of the empirically motivated size, value and momentum factors is tested using Petkova’s (2006) ICAPM model. The models are tested using both time-series and cross-sectional regression approaches. The results show that the factors constructed using different approaches have quite different average returns. In general, there is no size premium in average stock returns in any country. There is a value premium only for Japan and Canada that increases with size, while there is a momentum premium everywhere except Japan, which declines with size. Both timeseries and cross-sectional results show that the alternative models significantly improve the pricing performance, and especially the index-based model successfully explains the size and B/M portfolio returns for the four countries. None of the models can explain the size and momentum portfolio returns except for Japan. Although the international index-based model receives some empirical support in a combined international sample, the US and Japan, generally, the international models fail badly, which indicates a lack of integration. When relating size, value and momentum factors with innovations to the state variables in an ICAPM specification, the results are discouraging and contradict Petkova’s (2006) results for the US. The size, value and momentum factors remain important factors in explaining the crosssectional returns for all countries, even in the presence of the state variable innovations.

This thesis consists of three essays. The first two essays present empirical studies in which option market features related to information flows are examined. The third essay introduces a theoretical model to explain predictable dynamics in option pricing through the agents' learning process. In the first essay, I investigate the previously unexplored effects of asymmetric information on the adoption process of new equity options introduced into the market. I use a microstructure model to estimate measures of informational asymmetries. I discover that high informational asymmetries in the year prior to option listings produce larger levels of option adoption. Additionally, I find that option introductions induce reductions in asymmetric information. I also report that option bid-ask spreads start from low initial levels with a tendency to increase over time, which is unexpected since the introduced options are initially illiquid; however, this can be explained by the low level of initial activity by informed agents.The second essay examines whether the dynamics of the implied volatility surface of equity options contain exploitable predictability patterns. The option pricing predictability is expected due to the learning behaviour of agents in option markets. In particular, I explore the possibility that the dynamics of the implied volatility surface of individual equity options may be associated with subsequent movements in the volatility surface implicit in S&P 500 index options. I present evidence of strong relationships in the cross-section and the dynamics between implied volatility surfaces of equity options and S&P 500 index options. Moreover, I show that the predictability patterns of equity options are better characterized by the incorporation of information from the recent dynamics in the implied volatility surface of S&P 500 index options. Additionally, I analyse the economic value of the equity option predictability through trading strategies using straddle and delta-hedged portfolios, which produce abnormal risk-adjusted returns.Finally in the third essay, I introduce an equilibrium model to explain predictability patterns in option pricing through the learning process followed by investors. In this model the unknown fundamental dividend growth rate is subject to breaks, where the time periods between breaks follow a memoryless stochastic process. Immediately after a break there is insufficient information to price option contracts accurately. Therefore, a representative Bayesian agent has to learn step by step as new information arrives regarding the new fundamental value. I show that learning makes beliefs time-varying, which produces dynamic biases in option prices and implied volatilities. In addition, I find that learning generates different dynamic impacts on option contracts across moneyness and time-to-maturity; and hence it induces dynamics on the implied volatility surface. Furthermore, similarly to the predictability features observable in option market data, learning mechanisms make the option pricing dynamics predictable.

This thesis examines the ‘evaluation culture’ of derivatives ‘quants’ working in the over-the-counter markets for interest rate derivatives tied to Libor. Drawing on data from interviews with quants, financial mathematicians, and economists conducted primarily in the United Kingdom and the United States, combined with fieldwork at derivatives ‘quant’ conferences and an extensive set of technical sources, this thesis explores the historical development and contemporary patterning of modelling practices that are used within derivatives dealer banks to price and hedge Libor-based interest rate derivatives. Moreover, this thesis uses the historical development of interest-rate modelling techniques, beginning in the late 1970s, as a lens through which to understand the establishment, differentiation and separation of this ‘derivatives quant’ evaluation culture as a body of knowledge and practice distinct from financial economics. The analysis is carried out in nine chapters. The thesis begins with an introductory chapter, a chapter reviewing the relevant sociological and historical literature on economic and financial modelling, and a chapter covering the research methodology employed in the thesis. In Chapters 4-5, I provide background on the mathematical techniques used by derivatives quants and financial economists, the social and institutional structure of the Libor derivatives markets, and the instruments that are traded in these markets. In Chapter 6, I explore the organisational patterning of modelling practices in these markets and highlight the tacit and experiential nature of quant expertise. In Chapters 7-8, I investigate the ‘social shaping’ of models that are currently used to price so-called ‘exotic’ Libor derivatives. These models originated within the discipline of economics and were designed for a set of purposes different from models currently used by derivatives quants. By tracing out how these models were adapted to serve as derivatives pricing ‘engines’ within banks, I highlight how modelling practices are shaped by the organisational contexts in which they are used.

This thesis began with an introduction and literature review in Chapter 1. In Chapter 2, I propose a new intertemporal asset-pricing model based on heterogeneous beliefs to bring together the concurrent theories that could generate value and momentum effects. In this model, I assume that such behaviour occurs simply due to an agnostic view of forecasting returns considering the dominant strategy in the market. Given the endogenous price determination in the model, individuals were expected to adjust their own strategies to match the dominant strategy to obtain higher profits (from more accurate fore- casts). The idea was to bridge the literature on intertemporal asset allocation with the one on heterogeneous beliefs. In Chapters 3 and 4, I consider the empirical problem of implementing Markowitz (1952) mean-variance optimisation on a portfolio of stocks. In particular, I focus on the out-of-sample performance of the minimum-variance portfolio obtained from the use of asset group information and regularisation methods to obtain more stable estimates of the parameters in the model. Specifically, in Chapter 3, I introduce the use of regularisation methods to the portfolio selection problem and a literature review on the subject. In Chapter 4, I propose two alternative approaches for the use of the group structure information and to obtain more stable and regularised minimum-variance portfolios. I show that these procedures produce significantly better results in the portfolios compared with the unconstrained minimum-variance portfolios estimated from the whole data set in terms of portfolio variance and the Sharpe ratio.

The objective of this thesis is to develop and analyse two stochastic control problems arising in the context of investment capacity expansion. In both problems the underlying market fluctuations are modelled by a geometric Brownian motion. The decision maker’s aim is to determine admissible capacity expansion strategies that maximise appropriate expected present-value performance criteria. In the first model, capacity expansion has price/demand impact and involves proportional costs. The resulting optimisation problem takes the form of a singular stochastic control problem. In the second model, capacity expansion has no impact on price/demand but is associated with fixed as well as proportional costs, thus resulting in an impulse control problem. Both problems are completely solved and the optimal strategies are fully characterised. In particular, the value functions are constructed explicitly as suitable classical solutions to the associated Hamilton-Jacobi-Bellman equations

A decision maker, when facing a decision problem, often considers several models to represent the outcomes of the decision variable considered. More often than not, the decision maker does not trust fully any of those models and hence displays ambiguity or model uncertainty aversion. In this PhD thesis, focus is given to the specific case of asset allocation problem under ambiguity faced by financial investors. The aim is not to find an optimal solution for the investor, but rather come up with a general methodology that can be applied in particular to the asset allocation problem and allows the investor to find a tractable, easy to compute solution for this problem, taking into account ambiguity. This PhD thesis is structured as follows: First, some classical and widely used models to represent asset returns are presented. It is shown that the performance of the asset portfolios built using those single models is very volatile. No model performs better than the others consistently over the period considered, which gives empirical evidence that: no model can be fully trusted over the long run and that several models are needed to achieve the best asset allocation possible. Therefore, the classical portfolio theory must be adapted to take into account ambiguity or model uncertainty. Many authors have in an early stage attempted to include ambiguity aversion in the asset allocation problem. A review of the literature is studied to outline the main models proposed. However, those models often lack flexibility and tractability. The search for an optimal solution to the asset allocation problem when considering ambiguity aversion is often difficult to apply in practice on large dimension problems, as the ones faced by modern financial investors. This constitutes the motivation to put forward a novel methodology easily applicable, robust, flexible and tractable. The Ambiguity Robust Adjustment (ARA) methodology is theoretically presented and then tested on a large empirical data set. Several forms of the ARA are considered and tested. Empirical evidence demonstrates that the ARA methodology improves portfolio performances greatly. Through the specific illustration of the asset allocation problem in finance, this PhD thesis proposes a new general methodology that will hopefully help decision makers to solve numerous different problems under ambiguity.

The thesis explores applications of optimisation in investment management and risk measurement. In investment management the information issues are largely concerned with generating optimal forecasts. It is difficult to get inputs that have the properties they are supposed to have. Thus optimisation is prone to 'Garbage In, Garbage Out', that leads to substantial biases in portfolio selection, unless forecasts are adjusted suitably for estimation error. We consider three case studies where we investigate the impact of forecast error on portfolio performance and examine ways of adjusting for resulting bias. Treynor and Black (1973) first tried to make the best possible use of the information provided by security analysis based on Markovitz (1952) portfolio selection. They established a relationship between the correlation of forecasts, the number of independent securities available and the Sharpe ratio which can be obtained. Their analysis was based on the assumption that the correlation between the forecasts and outcomes is known precisely. In practice, given the low levels of correlation possible, an investor may believe himself to have a different degree of correlation from what he actually has. Using two different metrics we explore how the portfolio performance depends on both the anticipated and realised correlation when these differ. One measure, the Sharpe ratio, captures the efficiency loss, attributed to the change in reward for risk. The other measure, the Generalised Sharpe Ratio (GSR), introduced by Hodges (1997), quantifies the reduction in the welfare of a particular investor due to adopting an inappropriate risk profile. We show that these two metrics, the Sharpe ratio and GSR, complement each other and in combination provide a fair ranking of existing investment opportunities. Using Bayesian adjustment is a popular way of dealing with estimation error in portfolio selection. In a Bayesian implementation, we study how to use non-sample information to infer optimal scaling of unknown forecasts of asset returns in the presence of uncertainty about the quality of our information, and how the efficient use of information affects portfolio decision. Optimal portfolios, derived under full use of information, differ strikingly from those derived from the sample information only; the latter, unlike the former, are highly affected by estimation error and favour several (up to ten) times larger holdings. The impact of estimation error in a dynamic setting is particularly severe because of the complexity of the setting in which it is necessary to have time varying forecasts. We take Brennan, Schwartz and Lagnado's structure (1997) as a specific illustration of a generic problem and investigate the bias in long-term portfolio selection models that comes from optimisation with (unadjusted) parameters estimated from historical data. Using a Monte Carlo simulation analysis, we quantify the degree of bias in the optimisation approach of Brennan, Schwartz and Lagnado. We find that estimated parameters make an investor believe in investment opportunities five times larger than they actually are. Also a mild real time-variation in opportunities inflates wildly when measured with estimated parameters. In the latter part of the thesis we look at slightly less straightforward optimisation applications in risk measurement, which arise in reporting risk. We ask, what is the most efficient way of complying with the rules? In other words, we investigate how to report the smallest exposure within a rule. For this purpose we develop two optimal efficient algorithms that calculate the minimal amount of the position risk required, to cover a firm's open positions and obligations, as required by respective rules in the FSA (Financial Securities Association) Handbook. Both algorithms lead to interesting generalisations.

Anthropogenic climate change and environmental degradation impacts are no longer a worry for the distant future but a real concern for the present. Small Island Developing States (SIDS) and the poor, who often live by fragile ecosystems, are amongst the most vulnerable and exposed to the impacts of climate change. For these populations, climate related risks exacerbate other stressors and negatively impact livelihoods, security, and health. For low lying SIDS in particular, an additional fear is that climate change endangers their whole way of life, with their nationhood and culture being slowly engulfed by the approaching sea. Whilst the need to adapt is apparent, adaptation funding and motivating people to take up adaptive behaviours is a serious challenge. According to the ODI, financing climate change adaptation in the developing world can cost upwards of US$ 100-450 billion a year. Building adaptive capacity through cost effective solutions such as microloans for adaptive investments can be a promising strategy. By utilising the case study of Fiji, this Thesis attempts to unpack the cognitive drivers of climate change adaptive stated investment behaviour through a survey-based experiment (N=205). The prominent empirical method employed in this thesis was mediation analysis and specifically path analysis whereby the model specified is driven by theory. The choice of this method is justified through a comparison with multinomial logit. In the first instance, the antecedents of climate adaptive stated behaviour and the impact of information on subsequent stated behaviour were assessed through the framework of the Theory of Planned Behaviour. In addition perceptions to climate change in Fiji were explored through guided interviews (N=50). Overall positive attitudes, subjective norms and perceived behavioural control towards conservation and adaptation positively influenced intention to invest in adaptive strategies though intention only significantly influenced subsequent stated behaviour when information on climate change adaptation was provided. Next, the efficacy of incentives in engaging adaptive investments was assessed. The results indicated that the use of ‘green’ incentives (whereby loans are contingent on ecosystem impacts) was most conducive to the choice of adaptive investments over nonadaptive. In addition behavioural intention significantly mediated stated investment behaviour under the green incentive condition – which it is argued may show that such incentives crowd-in internal motives for engaging in environmentally protective behaviours. We also found that ethnicity was a strong positive moderator of behavioural antecedents and subsequent stated investment behaviour. Lastly the moderators of stated behaviour and its antecedents were examined by exploring resource dependence, perceived shocks, and perceived severity of environmental and other issues. Again, it was found that green incentives were successful in engaging people to take up adaptive investments more so then under a dynamic (whereby loans are contingent on repayement) and a no incentive condition. It was found that perceived shocks, and resource dependence could significantly impact cognitive antecedents of behaviour as specified by the Theory of Planned Behaviour and in particular perceptions of behavioural control. Shocks, resource dependence and perceived severity also moderated subsequent stated behaviour, with greater variability between between adaptive and non-adaptive investment choices under the no incentive and dynamic incentive conditions. The latter had a greater probablity of agents choosing non-adaptive over adaptive investments whilst in the former the opposite was true. Overall the results can be useful for adaptation policies, microloan best practice, and behavioural change interventions in SIDS in particular.