On the Determinants of the Implied Default Barrier

We use the maximum likelihood (ML) estimation approach to estimate the default barriers from market values of equities for a sample of 762 public industrial Canadian firms. The ML approach allows us to estimate the asset instantaneous drift, volatility and barrier level simultaneously, when the firm's equity is priced as a Down-and-Out European call (DOC) option. We find that the estimated barrier is positive and significant in our sample. Moreover, we compare the default prediction accuracy of the DOC framework with the KMV-Merton approach. Using probit estimation, we find that the default probability from the two structural models provides similar in-sample fits, but the barrier option framework achieves better out-of-sample forecasts. Regression analysis shows that leverage is not the only determinant of the default barrier. The implied default threshold is also positively related to financing costs, and negatively to liquidity, asset volatility and firm size. We also find that liquidation costs, renegotiation frictions and equity holders' bargaining power increase the implied default barrier level.Barrier option, default barrier, bankruptcy prediction, maximum likelihood estimation, strategic default

Corporate Governance and Bank Failure in Nigeria: Issues, Challenges and Opportunities

The paper is set out to investigate issues, challenges and opportunities associated with corporate governance and Bank failure in Nigeria and to see if a significant relationship exists between corporate governance and Banks failure. Relevant data were collected from the staff of eleven randomly selected commercial banks based in Lagos, using a well structured questionnaire. The statistical technique for data analysis and test of hypothetical proposition is Pearson product coefficient of correlation(r.) The result of the findings revealed that the new code of corporate governance for Banks is adequate to curtail Bank distress and that improper risk management, corruption of Bank officials and over expansion of Banks are the key issues why Banks fail. The study concluded that Corporate Governance is necessary to the proper functioning of banks and that Corporate Governance can only prevent bank distress only if it is well implemented. Finally the study recommends: that corporate governance should be used as a tool to help stem the tide of distress, as it entails conformity with prudential guidelines of the government; the Central Bank and NDIC should enforce the need for all banks to have approved policies in all their operation areas and strong inspection division to enforce these policies; that government owes the country a patriotic duty to establish and sustain macroeconomic stability in order for the banking system to perform at its optimum capacity , economic and political stability can help prevent bank distress and more importantly, is the need for qualified staff in the banking system as this will enable the utilization of expertise, skill and care in the performance of duties by staff, this will lead to better performance

Early Warning Indicators of Economic Crises: Evidence from a Panel of 40 Developed Countries

Using a panel of 40 EU and OECD countries for the period 1970-2010 we construct an early warning system. The system consists of a discrete and a continuous model. In the discrete model, we collect an extensive database of various types of economic crises called CDEC 40-40 and examine potential leading indicators. In the continuous model, we construct an index of real crisis incidence as the response variable. We determine the optimal lead employing panel vector autoregression for each potential indicator, and then select useful indicators employing Bayesian model averaging. We re-estimate the resulting specification by system GMM and, to allow for country heterogeneity, additionally evaluate the random coefficients estimator and divide countries into clusters. Our results suggest that global variables are among the most useful early warning indicators. In addition, housing prices emerge consistently as an important source of risk. Finally, we simulate the past effectiveness of several policy instruments and conclude that some central bank tools (for example, reserves) could be useful in mitigating crisis incidence.Bayesian model averaging, dynamic panel, early warning indicators, macroprudential policies, panel VAR.

Employing a Machine Learning Approach to Detect Combined Internet of Things Attacks Against Two Objective Functions Using a Novel Dataset

One of the important features of Routing Protocol for Low-Power and Lossy Networks (RPL) is Objective Function (OF). OF influences an IoT network in terms of routing strategies and network topology. On the other hand, detecting a combination of attacks against OFs is a cutting-edge technology that will become a necessity as next generation low-power wireless networks continue to be exploited as they grow rapidly. However, current literature lacks study on vulnerability analysis of OFs particularly in terms of combined attacks. Furthermore, machine learning is a promising solution for the global networks of IoT devices in terms of analysing their ever-growing generated data and predicting cyber-attacks against such devices. Therefore, in this paper, we study the vulnerability analysis of two popular OFs of RPL to detect combined attacks against them using machine-learning algorithms through different simulated scenarios. For this, we created a novel IoT dataset based on power and network metrics, which is deployed as part of an RPL IDS/IPS solution to enhance information security. Addressing the captured results, our machine learning approach is successful in detecting combined attacks against two popular OFs of RPL based on the power and network metrics in which MLP and RF algorithms are the most successful classifier deployment for single and ensemble models

Data Analytics for Automated Near Real Time Detection of Blockages in Smart Wastewater Systems

Blockage events account for a substantial portion of the reported failures in the wastewater network, causing flooding, loss of service, environmental pollution and significant clean-up costs. Increasing telemetry in Combined Sewer Overflows (CSOs) provides the opportunity for near real-time data-driven modelling of the sewer network. The research work presented in this thesis describes the development and testing of a novel system, designed for the automatic detection of blockages and other unusual events in near real-time. The methodology utilises an Evolutionary Artificial Neural Network (EANN) model for short term CSO level predictions and Statistical Process Control (SPC) techniques to analyse unusual CSO level behaviour. The system is designed to mimic the work of a trained, experience human technician in determining if a blockage event has occurred. The detection system has been applied to real blockage events from a UK wastewater network. The results obtained illustrate that the methodology can identify different types of blockage events in a reliable and timely manner, and with a low number of false alarms. In addition, a model has been developed for the prediction of water levels in a CSO chamber and the generation of alerts for upcoming spill events. The model consists of a bi-model committee evolutionary artificial neural network (CEANN), composed of two EANN models optimised for wet and dry weather, respectively. The models are combined using a non-linear weighted averaging approach to overcome bias arising from imbalanced data. Both methodologies are designed to be generic and self-learning, thus they can be applied to any CSO location, without requiring input from a human operator. It is envisioned that the technology will allow utilities to respond proactively to developing blockages events, thus reducing potential harm to the sewer network and the surrounding environment

Social media mental health analysis framework through applied computational approaches

Studies have shown that mental illness burdens not only public health and productivity but also established market economies throughout the world. However, mental disorders are difficult to diagnose and monitor through traditional methods, which heavily rely on interviews, questionnaires and surveys, resulting in high under-diagnosis and under-treatment rates. The increasing use of online social media, such as Facebook and Twitter, is now a common part of people’s everyday life. The continuous and real-time user-generated content often reflects feelings, opinions, social status and behaviours of individuals, creating an unprecedented wealth of person-specific information. With advances in data science, social media has already been increasingly employed in population health monitoring and more recently mental health applications to understand mental disorders as well as to develop online screening and intervention tools. However, existing research efforts are still in their infancy, primarily aimed at highlighting the potential of employing social media in mental health research. The majority of work is developed on ad hoc datasets and lacks a systematic research pipeline. [Continues.]</div

Advanced space system concepts and their orbital support needs (1980 - 2000). Volume 2: Final report

The results are presented of a study which identifies over 100 new and highly capable space systems for the 1980-2000 time period: civilian systems which could bring benefits to large numbers of average citizens in everyday life, much enhance the kinds and levels of public services, increase the economic motivation for industrial investment in space, expand scientific horizons; and, in the military area, systems which could materially alter current concepts of tactical and strategic engagements. The requirements for space transportation, orbital support, and technology for these systems are derived, and those requirements likely to be shared between NASA and the DoD in the time period identified. The high leverage technologies for the time period are identified as very large microwave antennas and optics, high energy power subsystems, high precision and high power lasers, microelectronic circuit complexes and data processors, mosaic solid state sensing devices, and long-life cryogenic refrigerators