The thesis consists of three chapters that investigate informational asymmetry mechanisms surrounding financial institutions. In the first chapter, my co-authors and I develop a theoretical model to analyse the effect of competition on the conflict of interest arising from the issuer pay compensation model of the credit rating industry. We find that relative to monopoly, rating agencies are more likely to inflate ratings under competition, resulting in lower expected welfare. These results do not depend on the presence of ratings shopping, but instead focus on the trade-off between maintaining reputation (to increase profits in the future) and inflating ratings today (to increase current profits). In the second chapter, I document a direct link between stock mispricing, as proxied by mutual fund flow-driven price pressure, and corporate investment. One standard deviation increase in stock price pressure leads to an increase of 1.3 percent in investment. High price pressure firms with high investments have lower future stock returns and lower future operational performance than high price pressure firms with low investments. Investment sensitivity to price pressure is stronger for firms that are less financially constrained, firms with high churn rates (shorter horizon) and firms with high R&D intensity (with more opaque assets). Finally, investment sensitivity to price pressure remains positive and significant for firms that do not engage in seasoned equity offerings around the investment period, suggesting there is a channel between stock price pressure and corporate investment that is independent of external financing. The third chapter documents a pronounced market timing ability of institutional investors when it comes to selling individual stocks. Based on more than 8 million institutional trades over the period 1999 to 2009, my co-authors and I document that (i) large (block) sales of institutional investors correlate with future negative excess returns, while stock purchases do not predict positive excess returns at the stock level,(ii) the one-sided successful market timing of block liquidations is more pronounced if the block represents a larger share of the investor portfolio or/and the stock capitalization, (iii) international investors have a weaker one-sided timing ability for block liquidations. The evidence strongly supports the hypothesis that proximity of block holding investors to management provides important inside information advantages.

The thesis focuses on risk measures used to calculate solvency capital requirements. It consists of three independent papers. The first paper (Chapter 2) investigates time-consistency, the relation that should hold across risk measurements of the same financial position at different time points. Sufficient conditions are provided for coherent risk measures, in order to satisfy the requirements of acceptance-, rejection- and sequential consistency. It is shown that risk measures used in practice usually do not satisfy these requirements. Hence a method is provided to systematically construct sequentially consistent risk measures. It is also emphasized that current approaches to dynamic risk measurement do not consider that risk measures at different time points have different arguments. Here we briefly discuss this new setting highlighting that the notions of time consistency presented in the literature need to be reinterpreted. The second and third papers (Chapters 3 and 4) consider respectively the risk arising from parameter and model mis-specification due to estimation from a limited amount of available data. This risk may have a substantial impact on risk measures used to quantify solvency capital requirements. We introduce a new method to quantify this impact measured as the additional capital needed to allow for randomness in the data sample used for the estimation procedure. This level of capital we call residual estimation risk. In the second paper, for parameter uncertainty we prove the effectiveness of three approaches for reducing residual estimation risk in the case of location-scale families. These are based on (a) raising the capital requirement by adjusting the risk measure, (b) Bayesian predictive distributions under probability-matching priors and (c) residual risk estimation via parametric bootstrap. Risk measures satisfying standard properties are used, for example the popular TVaR. For more general distributions only (a) and (b) are investigated and a truncated version of TVaR is used. Numerical results obtained via Monte-Carlo simulation demonstrate that the proposed methods perform well. In the third paper (Chapter 4), we compare the effectiveness of four different approaches to estimate capital requirements in the presence of model uncertainty. For a given set of candidate models the model posterior weights can be obtained via a Bayesian approach. Then we consider approaches based on: (a) worst case scenario, (b) highest model posterior, (c) averaging the capital under each model according to the model posterior weights and (d) determining the predictive distribution of the financial loss and using it to calculate the capital. It is shown that all these methods are very sensitive to the set of candidate models specified. If this has been carefully selected (for instance via expert judgement) the approach based on the highest posterior performs slightly better than the others. Alternatively, if there is poor prior information on the model set the effectiveness of all these approaches decreases substantially. In particular, the worst case approach has a very low performance. It also emerges that mis-specifiying the model by using distributions that are more heavy-tailed than the one generating the data, may reduce the capital and thus it is not a conservative approach.

This thesis considers specific topics related to the dynamic modelling and management of risk, with a particular emphasis on the generation of asymmetric and fat tailed behavior observed in practise. Specifically, extensions to the dynamics of the popular GARCH model, to capture time variation in higher moments, are considered in the univariate and multivariate context, with a special focus on the Generalized Hyperbolic distribution. In Chapter 1, I consider the extension of univariate GARCH processes with higher moment dynamics based on the Autoregressive Conditional Density model of Hansen (1994), with conditional distribution the Generalized Hyperbolic. The value of such dynamics are analyzed in the context of risk management, and the question of ignoring them discussed. In Chapter 2, I review some popular multivariate GARCH models with a particular emphasis on the dynamic correlation model of Engle (2002), and alternative distributions such those from the Generalized Asymmetric Laplace of Kotz, Kozubowski, and Podgorski (2001). In Chapter 3, I propose a multivariate extension to the Autoregressive Conditional Density model via the independence framework of the Generalized Orthogonal GARCH models, providing the first feasible model for large dimensional multivariate modelling of time varying higher moments. A comprehensive out-of- sample risk and portfolio management application provides strong evidence of the improvement over non time varying higher moments. Finally, in Chapter 4, I consider the benefits of active investing when the benchmark index is not optimally weighted. I investigate advances in the definition and use of risk measures in portfolio allocation, and propose certain simple solutions to challenges arising in the optimization of these measures. Combining the models discussed in the previous chapters, within a fractional programming optimization framework and using a range of popular risk measures, a large scale out-of-sample portfolio application on the point in time constituents of the Dow Jones Industrial Average is presented and discussed, with clear implications for active investing and benchmark policy choice.

We develop and analyze a model of optimal portfolio choice with a finite time horizon T. The investor's objective is to maximize the expected utility of terminal wealth based on partial information generated by stock prices. Rebalancing the portfolio composed of a stock and a bank account incurs transaction costs. This thesis extends the literature by examining the joint impact of partial information and transaction costs on investors' decisions and expected utilities. After estimating the uncertain drift from historical prices, an investor updates the estimate over [0, T] based on partial information. This investor learns about the drift with the Kalman-Bucy filter, which provides a statistically optimal estimate. Three regions of the state space with two free boundaries characterize the optimal portfolio strategy. A numerical algorithm using dynamic programming and a Markov chain approximation solves the model. The existing algorithm with known parameters is time consuming and liable to cause underflow or overflow of the range of values represented. We propose four improvements to overcome the drawbacks. The algorithm with modifications can be applied to the model under partial information according to the separation principle. We define two measures to quantify the losses in utility caused by partial information and transaction costs. Four quantities are introduced to describe investors' trading behaviours. With simulations of stock prices and the drift, the comparative analysis of five market parameters reveals the properties of the model and tests the robustness of the algorithm. Compared with the investors who use erroneous estimates of the drift, the learning investor's portfolio holdings are close to the informed investor's portfolio holdings. The average cost per transaction to the learning investor is the lowest. This investor has these benefits because the filter reduces uncertainty. We discuss the implications for practitioners to highlight the practical contributions of this research.

The literature has little to say about M&A activities in emerging markets, especially when firms from these countries acquire targets in developed economies, yet this growing tendency has manifested itself clearly in the global markets for corporate control over the last two decades. Unsurprisingly, our understanding of what underpins their decisions to venture into more advanced economies or whether they are able to create or destroy value is still limited. Using recent data on the emerging markets, we find emerging-markets acquirers tend to acquire small firms with a relatively low stock of intangible assets in developed economies. This finding is in accordance with the strategic market entry hypothesis, which posits that acquirers aim to learn from more advanced markets through market entry and gradually consolidate their global competitive position in the long run. Nonetheless, no matter what their strategy really is, we find that it is unlikely to materialize in the long run, or at least in the course of three, four or five years. Expected synergies are likely to be overwhelmed by the strong nature of the value destruction of cross-border acquisitions and evident agency and hubris problems.

Most scholars have concluded that actively managed equity mutual funds as a whole underperform their passively managed counterparts, linked to some benchmarks. In other words, active equity fund managers on average do not have enough significant stock-picking abilities to add value for investors. However, earlier investigations may be flawed through failure to give adequate consideration to liquidity. Hence, this research pays much attention to liquidity effects on mutual fund performance and argues that it is a preference for holding highly liquid stocks which results in the perceived underperformance. First, we find no significant liquidity premium at fund level, no matter the holding period returns or risk-adjusted performance. This indicates that all or almost all active equity fund managers in effect pay considerable attention to liquidity. We also examine the effects of liquidity on fund performance among actively managed equity funds. In contrast with earlier research, we find that actively managed equity funds in the aggregate perform close to the passive strategy. That means, on average, active equity fund managers do at least have talent sufficient to generate returns to cover costs that their funds impose on investors. This we attribute to the liquidity requirement of mutual funds. Moreover, using bootstrap simulation, we discover that many more mutual funds can be classified as skilled funds rather than lucky funds, once a liquidity factor has been included. Thus, our research provides a new insight into mutual fund performance, and highlights liquidity as an important and non-negligible determinant in the evaluation of mutual fund performance.

We hypothesise that certain market conditions could lead to liquidity shocks that will consequently increase SEO underpricing (defined as the close-to-offer return). We propose three scenarios of market conditions, namely aggregate issues with large volume, large market declines and market volatility. Using a sample of about 5,000 seasoned equity offerings from 1987 to 2009, we found that market volatility is significantly and positively related to SEO underpricing after controlling for other factors. We employed an estimation method proposed by Chambers and Dimson (2009) to examine the behaviour of SEO underpricing over our sample period from 1987 to 2009. We found that after controlling for changing risk composition, price practice, market conditions and the influence of underwriter reputation and analyst coverage, there was still an upward shift in SEO underpricing over the sample period, and the pattern cannot be fully explained by the practice of setting offer prices at lower integers. We borrowed the investment banking power hypothesis from the literature and argued that the upward shift of SEO underpricing over the sample period could be explained by the increase of investment banking power. As the industry structure of underwriting transfers from a competitive market to an oligopoly market, banks use non-price dimensions to gain market power and consequently increase SEO underpricing.

A recent trends report estimates that the total value of US-domiciled assets under management using socially responsible investment (SRI) strategies is $6.57 trillion. This represents more than one out of every six dollars under professional management in the United States (Forum for Sustainable and Responsible Investment, 2014). In Europe, a recent report by the European Sustainable Investment Forum reports that the total value of European assets under management using SRI strategies is in excess of €6.9 trillion (Eurosif, 2014). Consequently, the importance of SRI to financial practitioners and academics is considerable. This thesis examines the performance, risk and exposures of US SRI indices, UK SRI equity funds (domestic and global) and US SRI funds (large cap, mid-small cap, balanced and bond) to investigate a number of issues relating to the performance of SRI portfolios. The work highlights the potential psychological returns which may be related to investing in SRI funds through shareholder activism and discusses the relationship between the potential risks and returns that are associated with this form of investing. The study finds that the requirement to screen can detrimentally affect the performance of SRI portfolios, but that these effects are more pronounced for UK funds which predominately employ negative screening techniques, than US SRI portfolios (indices and funds) which principally employ positive and restricted screening methodologies. The investigation also discovers that SRI portfolios with smaller investment choice, such as those that can only invest in the UK stock market are more affected by SRI screening than those with large investment universes such as global or US equity funds. This finding is consistent with the smaller investment universe of an SRI fund, making it more likely SRI screening will affect the fund’s performance and risk. Post screening, a fund manager may find it more difficult to purchase assets with the potential to provide a good return or to diversify risk effectively. SRI screening also affects the sector exposures, industry exposures, systematic risk and idiosyncratic risks of UK SRI funds, indicating that screening can result in SRI portfolios holding significantly different assets from conventional funds. In addition, the intensity with which a UK SRI fund screens is shown to significantly affect risk-adjusted performance. Importantly, this study also finds that US SRI funds are more likely to vote affirmatively with shareholder proposals which relate to social and environmental issues than their conventional counterparts and are more likely to vote against company management on these issues. This finding is consistent with SRI investors receiving a psychological return through the shareholder activism of SRI funds.

Analysing the instability in the multivariate correlation structure, the present thesis starts from assessing in-sample and out-of-sample performances of multivariate GARCH models with or without a structural break. The result emphasizes the importance of correlation change point detection for model fittings. We then propose semi–parametric CUSUM tests to detect a change point in the covariance structures of non–linear multivariate models with dynamically evolving volatilities and correlations. The asymptotic distributions of the proposed statistics are derived under mild conditions. Our simulations show that, even though the nearly unit root property distorts the size and power of tests, the standardization of the data with conditional standard deviations in multivariate volatility models can correct such distortions. Lastly, concerning classical trimmed issue in change point test, we extend the semi-parametric CUSUM to weighted CUSUM tests, which enhances the power across either ends of a sample. A Monte Carlo simulation study suggests that weighted CUSUM tests exhibit better performances than unweighted ones in finite samples. Regarding empirical applications, we show the absorption ratio is a leading indicator of the financial fragility, and we study global financial contagion effect, also we investigate unexpected events in the U.S. equity market.

Product Portfolio Management is a complex strategy development process with mechanisms for evaluating market dynamics and competition; however none provide the managerial insight into competitive response required to stay one step ahead in the market. This research uses Game Theory as a tool to tackle and give perspective to a case study in the UK Motor Insurance Industry. The case study focuses on Endsleigh and Zurich Insurance products presented on aggregator websites. To enable efficient modelling the players and portfolios were reduced through competitive strategic grouping and strategy analysis. Player utilities were generated through a customer survey and manipulation of known Product Portfolio utility optimisation functions. Finally, the model was analysed to find the Nash Equilibrium using a non-linear optimisation model written with parallel quadratic programming based on a quasi-Newton technique. The Game Theory model of this highly competitive market provided valuable feedback into the market dynamics and competitive responses to product strategy decisions. When presented to the case companies it was expressed that the model provided market insights that would allow the company to preempt the market, reducing rework in product development and strategic decisions, and create a competitive advantage. Furthermore, when presented to the government regulatory body, they expressed interest in the model for the purpose of developing better regulations to control market dynamics in support of maximising market potential while protecting customer interests. Overall, the Game Theory model proved useful as a tool for evaluating the case study given the insights it provided, above and beyond alternate Product Portfolio analysis tools, into market dynamic, competitive response and customer behaviour.