Risk Management in Financial Information Systems using Bayesian Networks

During the last 20 years many technological advances have inundated the entire spectrum of our everyday lives. None of these advances has had such an impact like the IT revolution which can only compare with the Industrial Revolution of the 18th Century. The advent and acceptance of Information Technology as the norm rather the exception has seen this sector move from a tedious and cumbersome manually managed and run sector, to an almost paperless industry that is almost entirely dependent on Information Systems. With the growth of the dependency on IT, the impact of risk concerns on the development and exploitation of information systems has also increased exponentially. Within the financial services industry, risk management involves assessing and quantifying business risks, then taking measures to control or reduce them. These methods are generally built around a well structured process. However, the product coming from the different risk management steps is still largely informal, and often not analytical enough. This lack of formality hinders the automation of the management of risk-related information. Furthermore, these risk management system focuses on specific phases of the software life cycle, without recognizing that risks in one stage can have an impact on other stages. This necessitates the proposed study in order to propose a generic approach that may be deployed to mitigate risks from the early stages of financial information systems development for daily financial institution operations until the post-implementation phases. This paper proposes a new approach for performing a risk analysis study of financial information systems. It is aimed at developing a generic approach for Risk Analysis and Management applicable from the early phases of information system development unlike in the existing models which are applied after the development process. It can be utilized for identifying and valuating the assets, threats, and vulnerabilities of the information system, followed by a graphical modeling of their interrelationships using Bayesian Networks. The proposed approach will exploit the results of the risk analysis for developing a Bayesian Network model, which presents concisely all the interactions of the undesirable events for the system. Based on “what–if” studies of system operation, the Bayesian Network model identifies and prioritizes the most critical events. Keywords: Riks, risk management, Bayesian Network mode

Dependence estimation and controlled CVaR portfolio optimization of a highly kurtotic Australian mining sample of stocks

The drivers of mining stock prices are known to be several. Sharp spikes on the stocks return distribution have been linked to the presence of unusually high volatility signifying the presence of high levels of kurtosis. The accurate measurement of the stocks’ underlying co-movements for more accurate CVaR portfolio optimization requires, therefore, the utilization of sophisticated and specific-specialized techniques which could adequately capture and model these characteristics. Here this issue is addressed by applying statistical-graphical models for dependence estimation. Twenty mining stocks, out of the 801 listed in the ASX as of December 2012, have been selected for the analysis under the criteria of satisfying the eight years trading period sought, having very weak or no autocorrelation of residuals and displaying the highest kurtosis. Models’ estimations of dependence are compared and inserted into a differential evolution algorithm for non-convex global optimization in order to conduct risk controlled CVaR portfolio optimization (Ardia, Boudt, Carl, Mullen & Peterson, 2011) and be able to identify the one yielding the highest portfolio return. The findings are of relevance in portfolio allocation and portfolio risk management. Energy and mining stock markets are subjected to numerous price drivers holding complex relationships. The dynamics of production and consumption based on seasonality features, transportation and storage, weather conditions, commodity price fluctuations, currency changes, market confidence and expectations, trading speculations and the domestic and international states of the economy impact mining stock prices in particular and unobvious ways reflected in high volatility with sudden spikes in the stock’s return distribution (Pilipovic, 1998). The generation of accurate measurements of the dependence matrix of mining stock’s return series is therefore both, a non-trivial task due to the hard to decipher characteristics present in return series suffering from high levels of kurtosis (Carvalho, Lopes & Aguilar, 2010) and, a crucial element in portfolio optimization and portfolio risk management. The use of graphical techniques in this study is justified on the basis of their utility and suitableness. Graphical models such as pair c-vine copulas, the graphical lasso and adaptive graphical lasso provide, for instance, the visualization and flexibility to represent a problem in a more simplified and dissected form (Lauritzen, 1996). Graphs also appear to be naturally adequate to express the interaction of variables and thus facilitate the analysis of their dependency. The models of dependence estimation and CVaR portfolio optimization, on the other hand, are desirable due to mathematical and statistical framework they provide which may lead to satisfactory results and, their apparent ability to overcome the flaws (i.e. standardized model application to all joint distributions, restrictive and deterministic linear and monotonic modelling functions as in the Pearson and Spearman) traditional measures display when dealing with highly kurtotic data, joint distributions with stronger dependence in the tails and controlled risk non-convex portfolio optimization problems. Findings indicate that the highest portfolio returns are generated by inserting the covariance output matrix from the student-t copula into the differential optimization algorithm and, the student-t copula fitting with separate modelling of the marginal distributions appears to be the most desirable modelling choice. The portfolio return by the adaptive graphical lasso is lower than that of the student-t and is followed by the Gaussian pair c-vine copula. The regular graphical lasso produced the lowest portfolio return and the covariance matrix values were higher for models producing the highest portfolio returns implying that the models generating the lowest portfolio returns underestimated the dependence of the assets. The implications of the findings suggest that specific modelling of each marginal distribution, as compared to modelling based on a Gaussian framework, may lead to an edge in the estimations due to the distribution differences encountered on each marginal. Furthermore, the ability of the model to capture dependence in the tails, as it is the case of the student-t copula, does provide a modelling advantage too. This paper appears to be the first one in, comparing the portfolio performance of the models of dependence estimation in the context of controlled CVaR, applying the models treated to a highly kurtotic mining sample of stocks from the Australian market and modelling separately the distribution of the marginals when fitting the student-t copula

A Study of Risk Management Practices in the Nigerian Construction Industry.

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Master of Philosophy.The multiplier effect of the construction industry to both developed and developing countries cannot be overemphasised. The 2012 construction sector review purports that the UK construction industry has an annual turnover of more than £100 billion and accounts for 10 per cent of the country’s GDP. In contrast Nigeria, which is urbanising at one of the fastest rates in the world, contributes only 3.2 per cent in terms of Gross Domestic Product. In other words, the contributions of the construction industry warrants persistent review of its gaps; risk and uncertainty are particularly rife in most Nigerian construction projects, and the cost implications are severe enough to influence its low GDP contribution and beyond. The aim of this research effort is to understand the competitive advantage (value chain) of enshrining risk management practices up and down the construction supply chain. A literature review was first conducted to identify and categorise different risk management practices on and off a construction site. In turn, the population for the study was determined using stratified random method of sampling. The units of analysis in this case study are contractual interfaces and organisational structure, of which there can be hundreds in a typical case. After an initial scoping study – the administering of 150 questionnaires – of risk management practices amongst general contractors. Fourteen in-depth interviews were conducted across a typical value chain. Drawing on principles of grounded theory, interview transcripts were analysed through a combination of content analysis and graphical representation of contractual and organisational structures. Clients and contractors were found to be risk averse even though they claimed to have formal written procedures for risk management. Their awareness of the importance of risk management in construction business is more of lip services. A graphical representation of the Nigerian contractual structure, supply chain and value chain was achieved. Consequently, a conceptual model is developed for enshrining risk management practices in developing countries. The micro and macro implication of the prescribed model is subject to its testing and validation

Optimal risk minimization of Australian energy and mining portfolios under multiple measures of risk

Australia’s 2000’s decade saw the sharpest rise in mining investments arising from developing Asian emerging economies’ high demand for commodities like coal, iron ore, nickel, oil and gas which drove up prices to a historic level (Connolly & Orsmond, 2011). As of December 2012, 39 % and 9 % of the Australian Securities Exchange’s stocks were of the mining (coal and uranium stocks are included in this category) and energy (e.g. oil, gas and renewable energy stocks) sectors respectively, and investors recently have been considering separate portfolio positions in energy and mining stocks (Jennings, 2010). Facts of these nature set the stage for the task of selecting an optimal portfolio of stock securities where the fundamental questions faced by every investor, individual or institutional, are: a) what is the optimal point in time to go long in the investment position?, b) what are the optimal amounts to invest in every asset of a portfolio? and, c) when is the optimal time to short the portfolio investment position? The focus of the present study is on b) within a one period ahead forecast scenario. Understanding the price and volatility movements of stock securities taking as a basis of study their own dynamics and co-dynamics is a complex task that may be better addressed through a multilateral modelling approach. This paper, in this regard, departs from a single model application by fitting multiple risk measures to the optimization of four portfolios each consisting of 20 ASX’s stocks from the gold, iron ore-nickel, uranium-coal and oil-gas sectors. The five risk measures compared are: the variance, mean absolute deviation (MAD), minimizing regret (Minimax), conditional value at risk (CVaR), and conditional drawdown at risk (CDaR), where the last two are threshold based measures. The risk measure parameters are input into meanvariance quadratic (QP) and differential evolution (DE) portfolio problem specifications. Accurate estimations of the underlying interaction of stocks return series is a crucial element in portfolio allocation and portfolio risk management and frequentist traditional measures of dependence are rather inadequate. Here, with the objective of achieving more accuracy in the estimation of the dependence matrix, a Gaussian pair c-vine copula (PC), the regular graphical lasso (RL) and adaptive graphical lasso (AL) are fitted. Possible advantages from using these recently proposed and sophisticated techniques under model specifications where the covariance matrix is the measure of risk are indicated. The main objectives of the present study are to calculate the optimal weights to be invested in every stock of the portfolios making use of linear and nonlinear model specifications and the risk measures suggested, analyse the weight allocation differences and seek portfolio optimization advantages from using pair vine copulas and the graphical lasso in the estimation of dependence. The present multimodal approach is, therefore, expected to be more robust and as a consequence, provide more complete information that could serve for improved decision making on portfolio selection, allocation and rebalancing. Research questions are answered based on the analysis of gold portfolio outcome values, only. Findings indicate that CDaR is an important risk measure to be considered, along with other measures of risk when optimizing portfolios of stocks and no single measure of risk is suggested alone. The Gaussian pair cvine copula through the use of one different parameter in the modelling of every pair of variables’ joint distribution appears to be more sensitive in capturing data’s distribution characteristics. The adaptive graphical lasso also appears to be more perceptive when grasping the signal of the underlying interaction of the stocks. Therefore, valuable information could be drawn and inferred from applying multiple risk measures and sophisticated statistical techniques for their estimation. The weight allocation from threshold risk measures such as CVar and DaR and Minimax clearly differs from the rest. The models identified stocks with high return relative to risk and vice versa. The originality of the present study lies on the sectors of application and its multi-model nature

A Graphical Adversarial Risk Analysis Model for Oil and Gas Drilling Cybersecurity

Oil and gas drilling is based, increasingly, on operational technology, whose cybersecurity is complicated by several challenges. We propose a graphical model for cybersecurity risk assessment based on Adversarial Risk Analysis to face those challenges. We also provide an example of the model in the context of an offshore drilling rig. The proposed model provides a more formal and comprehensive analysis of risks, still using the standard business language based on decisions, risks, and value.Comment: In Proceedings GraMSec 2014, arXiv:1404.163

Advanced Techniques for Assets Maintenance Management

16th IFAC Symposium on Information Control Problems in Manufacturing INCOM 2018 Bergamo, Italy, 11–13 June 2018. Edited by Marco Macchi, László Monostori, Roberto PintoThe aim of this paper is to remark the importance of new and advanced techniques supporting decision making in different business processes for maintenance and assets management, as well as the basic need of adopting a certain management framework with a clear processes map and the corresponding IT supporting systems. Framework processes and systems will be the key fundamental enablers for success and for continuous improvement. The suggested framework will help to define and improve business policies and work procedures for the assets operation and maintenance along their life cycle. The following sections present some achievements on this focus, proposing finally possible future lines for a research agenda within this field of assets management

Developing a Bayesian Network risk model to enhance Lean Six Sigma

In today\u27s global market, manufacturing organizations are striving to improve their pro- duction performance in order to remain competitive advantages. For the past few decades, many efforts have been conducted by both researchers and practitioners to develop managerial and technical approaches to improve manufacturing processes. Among them, Lean and Six Sigma have become the two most recognized methodologies and together they comprise the primary components of process improvement strategies. However, with the manufacturing system and its external environment becoming more and more complex, a great range of risk factors can affect the results of the Lean Six Sigma initiatives. Consequently, the organization is constantly exposed to risks of not being able to generate a quality product to meet the customer\u27s requirements. The existence of risk is often neglected because there is no easy way to perform the risk analysis for Lean Six Sigma activities due to their complexity. The purpose of this study is to develop a risk-informed model that provides a systematic evaluation for potential risks to enhance the implementation of Lean Six Sigma initiatives. The methodology derives from the Bayesian Network methodology and is incorporated with other risk management techniques. Combining graphical approach to represent cause-and-effect relationships between events of interests and probabilistic inference to estimate their likelihoods, Bayesian Network provides an effective method to evaluate the reliability of Lean Six Sigma. The developed model can be used for assessing the potential risks associated with Lean Six Sigma initiatives and prioritizing efforts to minimize their impacts. The model can serve as a primary component of the decision-making toolbox for maximizing the effectiveness of Lean Six Sigma initiatives and subsequently increasing the competitiveness of a manufacturing firm