Toward Business Integrity Modeling and Analysis Framework for Risk Measurement and Analysis

Financialization has contributed to economic growth but has caused scandals, misselling, rogue trading, tax evasion, and market speculation. To a certain extent, it has also created problems in social and economic instability. It is an important aspect of Enterprise Security, Privacy, and Risk (ESPR), particularly in risk research and analysis. In order to minimize the damaging impacts caused by the lack of regulatory compliance, governance, ethical responsibilities, and trust, we propose a Business Integrity Modeling and Analysis (BIMA) framework to unify business integrity with performance using big data predictive analytics and business intelligence. Comprehensive services include modeling risk and asset prices, and consequently, aligning them with business strategies, making our services, according to market trend analysis, both transparent and fair. The BIMA framework uses Monte Carlo simulation, the Black–Scholes–Merton model, and the Heston model for performing financial, operational, and liquidity risk analysis and present outputs in the form of analytics and visualization. Our results and analysis demonstrate supplier bankruptcy modeling, risk pricing, high-frequency pricing simulations, London Interbank Offered Rate (LIBOR) rate simulation, and speculation detection results to provide a variety of critical risk analysis. Our approaches to tackle problems caused by financial services and the operational risk clearly demonstrate that the BIMA framework, as the outputs of our data analytics research, can effectively combine integrity and risk analysis together with overall business performance and can contribute to operational risk research

A survey of simulation techniques in commerce and defence

Despite the developments in Modelling and Simulation (M&S) tools and techniques over the past years, there has been a gap in the M&S research and practice in healthcare on developing a toolkit to assist the modellers and simulation practitioners with selecting an appropriate set of techniques. This study is a preliminary step towards this goal. This paper presents some results from a systematic literature survey on applications of M&S in the commerce and defence domains that could inspire some improvements in the healthcare. Interim results show that in the commercial sector Discrete-Event Simulation (DES) has been the most widely used technique with System Dynamics (SD) in second place. However in the defence sector, SD has gained relatively more attention. SD has been found quite useful for qualitative and soft factors analysis. From both the surveys it becomes clear that there is a growing trend towards using hybrid M&S approaches

Extracting the Italian output gap: a Bayesian approach

During the last decades particular effort has been directed towards understanding and predicting the relevant state of the business cycle with the objective of decomposing permanent shocks from those having only a transitory impact on real output. This trend--cycle decomposition has a relevant impact on several economic and fiscal variables and constitutes by itself an important indicator for policy purposes. This paper deals with trend--cycle decomposition for the Italian economy having some interesting peculiarities which makes it attractive to analyse from both a statistic and an historical perspective. We propose an univariate model for the quarterly real GDP, subsequently extended to include the price dynamics through a Phillips curve. This study considers a series of the Italian quarterly real GDP recently released by OECD which includes both the 1960s and the recent global financial crisis of 2007--2008. Parameters estimate as well as the signal extraction are performed within the Bayesian paradigm which effectively handles complex models where the parameters enter the log--likelihood function in a strongly nonlinear way. A new Adaptive Independent Metropolis--within--Gibbs sampler is then developed to efficiently simulate the parameters of the unobserved cycle. Our results suggest that inflation influences the Output Gap estimate, making the extracted Italian OG an important indicator of inflation pressures on the real side of the economy, as stated by the Phillips theory. Moreover, our estimate of the sequence of peaks and troughs of the Output Gap is in line with the OECD official dating of the Italian business cycle

Entrepreneurial profile of the UK in the light of the global entrepreneurship and development index

In this research summary, we provide a novel look into the entrepreneurial profile of the UK in an international context. We use a new method – the Global Entrepreneurship and Development Index GEDI [1] – to identify the entrepreneurial strengths and weaknesses of the UK economy, as well as to identify potential bottlenecks that hold back the performance of the UK relative to other advanced economies. We begin by providing an overview of the main findings

Forecasting OPEC oil price: a comparison of parametric stochastic models

Most academic papers on oil price forecasting have frequently focused on the use of WTI and European Brent oil price series with little focus on other equally important international oil price benchmarks such as the OPEC Reference Basket (ORB). The ORB is a weighted average of 11-member countries crude streams weighted according to production and exports to the main markets. This paper compares the forecasting accuracy of four stochastic processes and four univariate random walk models using daily data of OPEC Reference Basket series. The study finds that the random walk univariate model outperforms the other stochastic processes. An element of uncertainty was introduced into the point estimates by deriving probability distribution that describes the possible price paths on a given day and their likelihood of occurrence. This will help decision makers, traders and analysts to have a better understanding of the possible daily prices that could occur. JEL Classification Numbers: E64; C22; Q30 Keywords: Oil Price Forecasting, Probability Distributions, and Forecast Evaluation Statistics, Brownian Motion with Mean Reversion process, GARCH Model

Simulation in manufacturing and business: A review

Copyright @ 2009 Elsevier B.V.This paper reports the results of a review of simulation applications published within peer-reviewed literature between 1997 and 2006 to provide an up-to-date picture of the role of simulation techniques within manufacturing and business. The review is characterised by three factors: wide coverage, broad scope of the simulation techniques, and a focus on real-world applications. A structured methodology was followed to narrow down the search from around 20,000 papers to 281. Results include interesting trends and patterns. For instance, although discrete event simulation is the most popular technique, it has lower stakeholder engagement than other techniques, such as system dynamics or gaming. This is highly correlated with modelling lead time and purpose. Considering application areas, modelling is mostly used in scheduling. Finally, this review shows an increasing interest in hybrid modelling as an approach to cope with complex enterprise-wide systems

International Business Cycle Accounting

In this paper, I extend the business cycle accounting method a la Chari, Kehoe and McGrattan (2007) to a two-country international business cycle model and quantify the effect of the disturbances in relevant markets on the business cycle correlation between Japan and the US over the 1980-2008 period. I find that disturbances in the labor market and production efficiency are important in accounting for the recent increase in the cross-country output correlation. Financial globalization can be the cause of the recent increase in cross-country output correlation if it operated through an increase in the cross-country correlation of disturbances in the labor market and production efficiency, not in the domestic or international capital markets

A Framework To Develop Business Models For The Exploitation Of Disruptive Technology

Adopting new technology to expand business prospects is not a new trend. Certainly, this brings innovation and new opportunities to the business but also raises several challenges. This research addresses the challenges of business modelling in relation to disruptive technologies. Emerging technologies are very dynamic, resulting in continuous new developments. Therefore, businesses need to adjust their business models to stay sustained with this dynamic nature of technology. This research aims to create a conceptual framework and a related methodology to develop business models for the commercial use of disruptive technologies. The research evaluates the gaps in the major business model development methodologies and argues that these methodologies are not adequate for businesses that offer high-end products and services to their customers. It creates a framework to make a methodical comparison among different business model methodologies. Based on that framework, it conducts a systematic comparison of five significant business model development methodologies to identify possible flaws. It analyses business elements of two use cases, where a disruptive technology, in this case, cloud computing in the form of cloud-based simulation, offers significant value to customers. Thereafter, it compares the components of all the five identified methodologies with each other using business elements of the selected use case. While the analysis highlights the differences and the similarities between the methodologies, it also reveals the limitations of the current approaches and the need for further decomposing technological elements. Therefore, the study carries out an empirical investigation based on selective sampling. Seven real-life business use cases that execute the application of disruptive technology (i.e., cloud/HPC-based simulation as a solution based on cloud computing & high-performance computing) have been explored, involving 30 individual companies. Thenceforth, a thematic analysis of these use cases, based on a detailed report provided by a European research project, is conducted. Besides, three months of observation is carried out by participating in the same project as a ‘Research Associate’ from the period of July 2019 to September 2019. This three-month observation supports not only providing access to 26 business use cases and their relevant documents but also validating the information provided, as well as finding clarity in collected data. Moreover, the selected business use cases are particularly useful for identifying the technology elements that are required to create the proposed framework. The analysis has resulted in an understanding of the dynamics of the interrelationship of social and technical factors for developing new technological solutions that push the development of new business models devised for delivering solutions exploiting disruptive technologies. Based on this understanding, the research extends a widely used business model ontology (Osterwalder’s Business Model Ontology), and offers a new business model methodology with the introduction of new business model elements related to technology. The technological elements are being identified as the results of the above empirical analysis. Utilising this extended ontology, a novel methodology for developing business models for the exploitation of disruptive technologies is suggested and its applicability is demonstrated in the example of cloud-based simulation case studies. The research creates three main contributions. Firstly, it uses a systematic approach and identifies that the technological elements are not explicitly defined in the analysed business model methodologies, as well as the factors of disruption in the context of the socio-materiality view is missing. Secondly, it conducts an empirical analysis and defines the specific social and technological elements such as ‘Dynamic Capabilities’, ‘Competition Network’, ‘Technology Type’, ‘Technology Infrastructure’, ‘Technology Platform’, and ‘Technology Network’; that are needed to create a new business model methodology. Finally, it extends an existing business model ontology (which was developed by Alexander Osterwalder) and constructs a new ontological framework with an accompanying methodology to develop business models, particularly for organisations that introduce technological solutions as their main value using disruptive technologies

Long-run growth expectations and "global imbalances" : [January 5, 2011]

This paper examines to what extent the build-up of "global imbalances" since the mid-1990s can be explained in a purely real open-economy DSGE model in which agents’ perceptions of long-run growth are based on filtering observed changes in productivity. We show that long-run growth estimates based on filtering U.S. productivity data comove strongly with long-horizon survey expectations. By simulating the model in which agents filter data on U.S. productivity growth, we closely match the U.S. current account evolution. Moreover, with household preferences that control the wealth effect on labor supply, we can generate output movements in line with the data. JEL Classification: E13, E32, D83, O4