1,721,014 research outputs found
Cross asset class applications of functional data analysis: evaluation with controls for data snooping bias
Full text linkThis thesis applies functional data analysis techniques to address a number of specific research questions in financial markets. Data snooping bias controls are adopted in parallel to provide statistical robustness to our inferences. Firstly, we conduct an investigation into
U.S. exchange-traded fund outperformance during the 2008-2012 period. The funds are tested for net asset value premium, underlying index and market benchmark outperformance. The study serves as a platform to showcase the data snooping bias problem and application of generalised multiple hypothesis testing techniques, in advance of their use for functional data analysis evaluation. Secondly, as the first application of functional data analysis, we examine implied volatility, jump risk, and pricing dynamics within crude oil
markets. Strong evidence is found of converse jump dynamics during periods of demand and supply side weakness. Next, we demonstrate the performance advantage over traditional benchmarks of adopting a functional linear model to forecast EUR-USD implied volatility.
Our findings are shown to be robust across various moneyness segments, contract maturities and out-of-sample window lengths. The final chapter also uses a functional data framework to produce forecasts, demonstrating how information can be extracted from forward contracts to predict future spot foreign exchange rates. The evaluation of an out-of-sample framework leads to near systematic outperformance in terms of a direct comparison of performance measures, versus both the restricted vector error correction model and random walk. Overall, this thesis highlights the usefulness of adopting insightful and novel functional data analysis techniques across various asset classes where multiple hypothesis testing controls provide robustness around our conclusions. Each of the studies contributes to the literature individually, with the collection emphasising the benefits of adopting functional approaches to tackle a wide range of empirical finance problems
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Engineered nanomaterials: employer’s liability risk, REACH & governance
No full textNanotoxicology in relation to the effects of engineered nanomaterials [ENM] in the occupational setting has largely focused on the inhalation effects of ENM particularly on the respiratory tract. ENM are respirable allowing them to the interstitium of the lung and the blood and can be translocated to other organs. They have also been linked to cardiovascular effects. Carbon nanotubes appear to promote interstitial fibrosis. Multi-walled carbon nanotubes have been shown to promote lung cancer. This has given rise to legitimate fears of the potential for the causation of harm to workers. However, uncertainty still exists in terms of extrapolating the toxicity of studied ENM to all ENM and in translating that knowledge from the laboratory to humans. Epidemiological studies in the ENM context are nascent and logistically challenged.
The nanotoxicological research and the potential for the causation of harm to workers has also given rise to fears of latent civil liability claims and parallels have been drawn with the potential for claims on the scale and magnitude of the asbestos litigation. To date there have not been any reported employers’ liability claims arising from exposure to ENM. The risk governance literature has acknowledged the inevitability of latent civil liability claims and the uncertainty of the specific dynamics of this future litigation. But it has not gone beyond that to systematically address legal liability risk. This thesis fills this gulf in the literature.
Motivated and guided by the main objectives of the Sustainable Nanotechnologies project [SUN] which are: risk assessment, decision support for industry, regulators and the insurance sector to make informed decisions about nanotechnologies and risk management by designing process changes and technological solutions to reduce hazard and exposure, this thesis contributes to both the objectives and deliverables of the SUN project. Understanding the legal risks of ENM exposure is essential for effective risk governance because it informs legal risk assessment which in turn informs decision making in relation to the design and implementation of risk management strategies. Understanding legal liability risk in the ENM context is also important for decision support for liability risk insurance providers.
Effective risk analysis and management which includes legal liability risk will facilitate the realisation of the benefits of ENM while contemporaneously managing the potential risks. Ultimately this contributes to the sustainable development of ENM.
This thesis is interdisciplinary. It traverses the disciplines of law, regulation and governance. Following the establishment of the basis for inevitable legal liability in ENM occupational exposure scenarios, the thesis goes further to demonstrate the ineffectiveness of the EUs response to the regulation of ENMs as a risk management measure because its application to ENM essentially amounts to mapping a regulation designed for bulk materials onto the specific contours of ENM. An alternative approach to governance is posited. This approach is innovative because it fits the elements of ENM risk governance. Founded on anticipatory governance it is buttressed by John Rawls’model of wide reflective equilibrium and overlapping consensus. In its totality, this thesis contributes to the contemporary discourse in relation to ENM risk governance which constitutes legal risk, regulatory policy and governance
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Risk assessment of emerging technologies using Bayesian networks
No full textThe benefits of emerging technologies are often recognized prior to the acknowledgement of potential barriers such as legal restrictions, regulatory scrutiny or liability conundrums. Consequently, supervisory bodies and insurance companies often lag behind in the identification, assessment, response and control of new risks conceived by these innovations. Lack of empirical data on the likelihood or consequence of adverse events inhibits traditional risk analysis techniques. This reactive process poses a considerable threat to the safety of end-users, to insurance companies’ sustainability and to the public’s trust in the regulatory institutions responsible for the oversight of new technologies. This thesis contributes a novel methodology posited to overcome these limitations. Bayesian networks (BNs) are proposed as an effective technique for the proactive risk assessment of emerging technologies. BNs are probabilistic models of causes and effects, graphically expressing causal relationships (i.e. conditional probabilities) between different variables (Fenton and Neil, 2012). The method is suited to evaluate the risk of emerging technologies in its ability to dynamically update its belief as new information becomes available. Furthermore, BNs facilitate the use of expert judgement to bridge the informational gap where evidence is sparse, inconsistent or missing.
This thesis examines the efficacy of the BN methodology applied to the risk assessment paradigms of both nanomaterials and autonomous vehicles. Chapter 2 and Chapter 3 extends the state-of-the-art risk assessment approaches of nanomaterials (NMs). Large inconsistencies in characterisation data, toxicological measurements and exposure scenarios make it difficult to map and compare the risk associated with NMs based on physicochemical data, concentration and exposure route. Chapter 2 conducts a novel investigation into the influence of nanomaterial characterization, type, and exposure on the level of risk posed in an occupational setting using a BN model. This research contributes to the literature by being the first to map NM occupational risk probabilities derived from the BN model onto a control banding illustration. The third chapter is a comparative analysis of BNs ability and capacity to rank the hazard of different nanomaterials against more established methods. This comparative study investigates the efficacy of the quantitative weight of evidence and BNs in ranking the potential hazard of TiO2, Ag, and ZnO. This research finds that hazard ranking is consistent for both risk assessment approaches. Furthermore, this research adds to the academic literature by demonstrating that the BN exhibits more stability when the models are perturbed with new data.
Chapters 4 and 5 contribute novel insights into the probabilistic reasoning and forecasting ability of BNs for the assessment of the mutable and emerging risks inherent to the state-of-the-art in connected and autonomous vehicle innovations. The first application demonstrates a BN statistical risk estimation approach that can accommodate changing risk levels and the emergence of new risk structures. This method is applied to a Level 3 conditionally-autonomous vehicle for two scenarios, one where the driver is in control and one where the vehicle is in control. This approach is evaluated from the perspective of the insurer and it is recommended that a greater degree of collaboration is required between insurance companies and car manufacturers in order to develop a greater understanding of the risks underlying semi-autonomous and fully autonomous vehicles. Chapter 5 specifies a unique proactive connected and autonomous vehicle (CAV) cyber-risk classification model. This method uses a BN model, premised on the variables and causal relationships derived from the known software vulnerabilities within the National Vulnerability Database (NVD), to represent the probabilistic structure and parameterisation of CAV cyber-risk. The resulting model demonstrates nearly 100% prediction accuracy of the quantitative cyber-risk score and qualitative cyber-risk level. The model is then applied to the use-case of GPS systems of a CAV with and without cryptographic authentication. This demonstrates how the model can be used to predict the effect of risk reduction measures on the overall cyber-risk level.
The resulting applications contribute state-of-the-art risk assessment frameworks for nanomaterial occupational hazard, semi-autonomous vehicle accident risk, and cyber-risk for connected and autonomous vehicles. Each chapter represents a peer-reviewed published/accepted journal article with a mean 2016 journal impact factor of 3.12
The impact of connected automated vehicles on the insurance sector: a comprehensive analysis of legal and risk factors
No full textThe introduction of connected automated vehicles (CAV) offers significant societal benefits and economic opportunities while similarly posing major challenges to society, businesses, research, and regulatory bodies. In addition to directly affected markets, such as the automobile manufacturing and transportation sectors, insurance is one of the core downstream sectors acutely sensitive to the adoption of this emerging technology. In fact, the adoption of CAV technology has the potential to profoundly affect existing business models of insurers with key triggers arising from changes to liability frameworks, a changing risk landscape and changes of customer interfaces and market structure induced by a shift of societal mobility approaches.
Due to the facilitating role of insurers for the introduction of new technology, the strategic implications for this stakeholder have to be understood holistically and proactively to ensure a seamless introduction. Given that insurance as a subject of academic research is interdisciplinary by character and since the strategic implications emerging with CAV technology originate from both legal and risk factors, this thesis provides a multidimensional research approach linking different research disciplines and research methods.
Using the current German liability and insurance framework as a case study, this thesis confirms that the methodology to allocate liability based on the strict liability of the vehicle owner is generally compatible with peculiarities of automated driving. However, adjustments to the existing framework are necessary to maintain an adequate level of claimant protection for accidents caused by automated vehicles. In addition, this thesis highlights that an adequate ultimate allocation of liability costs is potentially inhibited because of several barriers that hinder the shift of liability costs to the manufacturer side. This is particularly because the ability and motivation of motor insurers to conduct subrogation claims is negatively affected by a lack of required technical and engineering know-how and because market-wide conduction of subrogation claims would erode the business volume of motor insurance.
In addition to legal challenges arising from existing liability and insurance frameworks, this thesis analyses data-driven use cases to present the access to in-vehicle data as another core CAV-related legal question from an insurance-perspective. Finding a status quo where OEMs begin to leverage their superior access to in-vehicle data for the expansion of their own business models, the analysis underlines that the increasing interconnection of modern automobile vehicles will have a significant strategic impact on insurance-related service offerings. However, by analysing this status quo from a business ecosystem perspective, it becomes apparent that taking the role of a physical dominator to extract maximum short-term value might be an obvious but not necessarily successful approach for OEMs on long-term. This is because the shift from a goods-dominant supply-chain perspective to a service-dominant perspective will also need a profound redefinition of OEMs´ supply-chain relationships. This finding supports the resolution of contrasting positions of OEMs and third-party providers and enables an unbiased and ix
farsighted approach of regulatory bodies to prevent that inadequate advantages of single actors result in market failure to the detriment of customers.
For analysing the potential impacts of CAV technology on insurance-relevant risk-factors, this thesis provides qualitative and semi-quantitative analyses of relevant drivers for motor insurance and automotive product recall risk. Referring to CAV technology´s impact on motor insurance risk exposure, the research concludes that automated driving vehicles indeed have the potential to significantly decrease the number of road accidents caused by human-error. However, as there is insufficient data available about the reliability of highly automated driving systems in real-world applications, reliable quantification of future accident risk exposure is inhibited. Therefore, assumptions of a sharply decreasing accident risk exposure are by no means straightforward nor statistically proven, especially as the provided analysis reveals risk-relevant peculiarities of every single level of automation. In addition, new risks such as the risk of automotive cyber-attacks are likely to emerge with the penetration of CAV technology which, in turn, introduce potential sources of yet unknown catastrophe-alike risk exposures to MTPL insurance.
For the analysis of automotive product recall risk, this thesis couples the qualitative assessment of CAV-induced risk drivers from legal and technology-related sources with an analysis of historical product recall data from different product recall databases. With this approach, this thesis finds an increasing risk of product recalls induced by CAV technology, which is triggered by the increasing complexity of vehicle hardware and software and by an increasing legal and reputational risk in the case that CAV technology fails.
With the provided multidimensional research approach, this thesis contributes to an improved understanding of legal frameworks regulating CAV technology´s introduction and enables regulatory bodies for a proactive and farsighted adaption of existing legislation. Particularly referring to the improved understanding of liability frameworks, the contribution to existing literature results from the fact that the provided in-depth analysis not only extends on liability law on a detached basis but considers important interdependencies resulting from motor insurance law and from motor insurers´ central role within the liability settlement process.
In addition to the contribution to an improved understanding of legal factors, the analysis of CAV technology´s impacts on motor insurance risk characteristics contributes to an improved understanding of CAV technology´s inherent risk-factors. This is particularly useful as existing research and public expectations often seem to be biased and not sufficiently granular in the analysis of idiosyncrasies of single levels of automation. Furthermore, the presented research on CAV technology´s implications to product recall risk contributes to a comprehensive academic discussion of relevant risk-factors and serves as a cornerstone for academic research on a largely unaddressed aspect.
From a business perspective, the findings of this thesis not only provide an holistic assessment of the impacts of CAV technology on the insurance sector enabling insurance entities to take proactive strategic measures for adapting existing business models to a probably changing business environment but also support stakeholders on the CAV technology supply side in implementing adequate risk management frameworks to cope with emerging risks exposures such as product liability and product recall
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