The main objective of this thesis is to implement stochastic correlation into the existing structural credit risk models. There are two stochastic models suggested for the covariance matrix of the assets' prices. In our first model, to induce the stochasticity into the structure of the correlation, we assume that the eigenvectors of the covariance matrix are constant but the eigenvalues are driven by independent Cox-Ingersoll-Ross processes. To price equity options on this framework we first transform the calculations from the pricing domain to the frequency domain. Then we derive a closed formula for the Fourier transform of the Green's function of the pricing PDE. Finally we use the method of images to find the price of the equity options. The same method is used to find closed formulas for marginal probabilities of defaults and CDS prices. In our second model, the covariance of the assets follows a Wishart process, which is an extension of the CIR model to dimensions greater than one. The popularity of the Heston model, which uses the CIR process to model the stochastic volatility, could be a promising point for using Wishart process to model stochastic correlation. We give closed form solutions for equity options, marginal probabilities of defaults, and some other major financial derivatives. For the calculation of our pricing formulas we make a bridge between two recent trends in pricing theory; from one side, pricing of barrier options by Lipton (2001) and Sepp (2006) and from other side the development of Wishart processes by Bru (1991), Gourieroux (2005) and Fonseca et al. (2006, 2007a, 2007b). After obtaining the mathematical results above, we then estimate the parameters of the two models we have developed by an evolutionary algorithm. We prove a theorem which guarantees the convergence of the evolutionary algorithm to the set of optimizing parameters. After estimating the parameters of the two stochastic correlation models, we conduct a comparative analysis of our stochastic correlation models. We give an approximation formula for the joint and marginal probabilities of default for General Motors and Ford. For the marginal probabilities of default, a closed formula is given and for the joint probabilities of default an approximation formula is suggested. To show the convergence properties of this approximation method, we perform the Monte Carlo simulation in two forms: a full and a partial Monte Carlo simulation. At the end, we compare the marginal and joint probabilities with full and partial Monte Carlo simulations.

Regulators and the general public frequently advocate for higher-quality disclosure policies to reduce information asymmetry. Research and anecdotal evidence documents sizable benefits to firms that maintain high quality disclosure. This thesis explores the costs and benefits of changing disclosure quality from the perspective of the financial analysts, a sophisticated user group. This thesis presents a comprehensive view of analysts’ evaluations of disclosure quality. I investigate capital market reaction when firms experience a sustained decrease in analyst disclosure ratings. The results demonstrate that firms with deteriorating disclosure experience negative consequences, consistent with increasing information asymmetry. However, the magnitude is not as large as expected given the benefits enjoyed when disclosure quality improves. Given that firms that allow their disclosure quality to decline give up benefits they previously enjoy, I investigate why they allow this decline to occur. The deterioration is negatively associated with the interaction between capital demand and expected earnings performance implying that when firms require capital, but are expecting poor future earnings, they are more likely to permit a deterioration to occur. Declines are also associated with the occurrence of various disruptive events that imply greater uncertainty about the firm. These firms have a strong demand for external capital which they satisfy by accessing private and public debt markets. Overall, firms that experience disclosure ratings declines are not a mirror image of firms that experience ratings increases. Finally, I investigate the association between the disclosure ratings and quantitative disclosure characteristics. The results indicate significant associations, consistent with the assumption that easily accessible and quantifiable disclosure measures are captured in analysts’ ratings of disclosure quality. This thesis adds to the literature by providing insight into how analysts evaluate disclosure quality and what managers are willing and able to deliver. The research documents attributes of disclosure quality that are regarded as important by financial analysts. While analysts are a key set of financial statement users, there are many other types of users. By understanding disclosure quality from a user's perspective, regulators and researchers are more able to anticipate the implications of a proposed change in disclosure rules.

The emergence of new public management has been implicated in the changes that have occurred in the public sectors of western democracies. One outcome of these changes is that the public sector is expected to operate in a more commercial manner and that it be accountable not only for the money that it spends but also for the effectiveness with which it spends those funds. In response to these expectations, changes have occurred in both the accounting technologies and reporting mechanisms for the public sector. The Treasuries and Departments of Finance for each jurisdiction in Australia set accounting and financial reporting policy for the public sector. However, since the establishment of the Public Sector Accounting Standards Board in 1983, the commonwealth and state governments have shown a willingness to adopt standards issued by the accounting profession. The adoption of three specific public sector accounting standards developed by the accounting profession in accordance with the conceptual framework, mean that a financial reporting model, based on the private sector 'decision-useful model', has been adopted in the Australian public sector. The 'decision-useful model' incorporates dependent users who are reliant on general purpose financial reports to make economic decisions. The decision to adopt this model for all public sector reporting entities, did not receive unanimous support. The complexities of the public sector formed the foundation for critics to question the applicability of this model to the public sector. In addition, critics argued that the model lacked empirical substantiation. The purpose of this research is to determine the applicability of the 'decision-useful model' to the public sector by empirically identifying users of public sector general purpose financial reports and their information requirements. Prior empirical research has been piecemeal in terms of both scope and research method. As a result, it has not been cumulative. This research will refine and extend the work of previous studies in two ways. First, in terms of scope, it will encompass all public sector entity types and will address all three elements of the 'decision-useful model' - the identity of users, what information they use and their purposes for requiring information. Second, this research will adopt a method which directly accesses users across public sector entity types. As a consequence, an assessment is able to be made of the applicability of the 'decision-useful model' in general and its application to specific public sector entity types. The findings of this research indicate that the 'decision-useful model' is misspecified in the public sector and that there are significant differences among public sector entity types in terms of users and their information requirements. First, the classification of users as normatively determined is not exhaustive and includes a large representation of non-dependant users. Second, all users preferred performance information and narrative information was preferred over general purpose financial reports. Further, users considered that general purpose financial information was more useful for accountability purposes than for decision making. These results should be useful to policymakers and accounting standard setters in the future prescription of the contents of financial reports for public sector entities.

The efficient market hypothesis states that an efficient market incorporates all available information to provide an accurate valuation of an asset. Presently investors and researchers attempt to forecast future returns (profit/loss if the asset is held for a certain period) and volatility (variance of the returns) of the asset based on past trading behaviour, and commonly ignore non-numerical information. It is almost impossible to forecast future returns for frequently traded assets such as stocks, bonds, and currencies, so many institutional investors prefer to forecast future volatility. Volatility is frequently used by traders and fund managers to measure the risk of continuing to own the asset. Most volatility forecasting models completely disregard the arrival of news and therefore theoretically violate the efficient market hypothesis. The aim of this research is to investigate how the inclusion of details of the arrival of asset specific news (news which is relevant to the asset) can improve the volatility forecasts of a model. The problem is that the efficient market hypothesis indicates that only new information will cause the market to react, and therefore it is necessary to determine whether the news contains any new information. Most news does not include any new information and therefore assuming all news will trigger abnormal market behaviour is unlikely to improve the performance of a model. Furthermore news which causes a shock, i.e., news which contains highly unexpected new information, will cause a greater change in volatility than news which contains expected information. Therefore to produce a model that factors in the arrival of news into volatility forecasts, it is beneficial to examine the content to predict the reaction to the news. This research combines the field of econometrics with machine learning and intelligent data analysis. All hypotheses tested within this thesis are tested on a large collection of stocks traded in the US, UK and Australia. To my knowledge, this is the largest dataset used for the types of experiments conducted in this thesis. In this thesis evidence is provided to suggest that asset specific news is correlated with abnormal returns, volatility, and volatility forecast errors. There is also evidence to suggest that abnormal volumes and trading activity correlate to asset specific news. This confirms the findings of previous studies though in most cases only a small dataset was used and often only one or two time series (i.e., return, volatility, volume etc.) were used. Furthermore many studies did not investigate the intraday effect of news (i.e., the reaction on the day the news was released). The studies which investigated the intraday effect tended to focus on macroeconomic news, which is scheduled and eagerly anticipated by investors. Therefore the behaviour is easier to detect that for asset specific news. It is demonstrated that the content of news can be used to forecast abnormal returns and forecast periods when the given volatility forecasting model exhibits abnormally large errors (the difference between the realised volatility and the volatility which the given model forecast) with a high degree of accuracy. This was achieved by analysing the content of past news which correlated with abnormal market behaviour. For this research a new method for ranking terms is introduced and demonstrated to be very effective. Previous studies have revealed that the content of news can be used to forecast abnormal returns but, to my knowledge, no study has investigated the volatility forecast error. Furthermore, most previous studies have used a small dataset, and to forecast at relatively low frequencies (most are daily, though one is hourly). To the best of my knowledge no previous study has use such a large dataset to predict the high frequency (as little as 5 minutes) market reaction to news. Nor has any previous study achieved classification accuracies as high as those achieved in this thesis. Finally, a news aware volatility forecasting model is produced and the evidence demonstrates that the performance is better than an alternative model which does not account for news under certain circumstances. Furthermore it is demonstrated that using the content of news to choose documents which are more likely to cause the market to react yields better forecasts. Very few researchers have included the arrival of news in a volatility forecasting model, and all of these have used small datasets. Furthermore, to my knowledge, none of these researchers have used the content of the news to choose news which is more likely to cause the market to react.

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Thesis (Ph. D.)--University of Adelaide, Dept. of Commerce, 1987. / Includes bibliographical references (leaves 522-538).

Thesis (Ph.D.)--University of California, Los Angeles, 2003. / Includes bibliographical references (leaves 229-232).