Education and events
Faculty of Actuarial Science and Insurance Research Seminars
Academic Year 2018/2019.
If you wish to attend a seminar, please book, using the link below the Seminar. Tea and other light refreshments will be available 15 minutes before the talk begins in the milling area outside conference room.
The FASI seminars are recognised by the Institute and Faculty of Actuaries as providing 1 hour of continuous professional development (CPD) training.
If you would like to be added to the seminar electronic mailing list, please send an e-mail stating so, containing your name to Faculty.Administration@city.ac.uk.
26th September - Prof Ermanno Pittacco
Professor Ermanno Pitacco
Director, Master in Insurance & Risk Management, MIB Trieste School of Management
Heterogeneity in mortality: A survey with an actuarial focus
10th October - Mathias Lindholm
Department of Mathematics, Stockholme University.
Estimation of conditional mean squared error of prediction for claims triangle reserving
Mathias Lindholm is an associate professor and senior lecturer at the department of mathematics, div. of mathematical statistics, at Stockholm University. He is working mainly with applied probability and statistics with an interest towards insurance mathematics. Mathias also has been working at major Swedish insurance companies with risk management, investment research and internal models under Solvency II.
The aim of the talk is to present a transparent approach to account for estimation error in the analysis of prediction error of reserving methods and to provide an explicit estimator of the prediction error, in the form of an estimator of the conditional mean squared error of prediction, MSEP. The method of obtaining this estimator relies on using a certain resampling scheme, where the resampling can be carried out either conditionally or unconditionally, together with a single first order Taylor expansion.
The applicability of the approach is illustrated by applying the method to both sequential (conditional) reserving models, e.g. the distribution-free chain ladder model, as well as non-sequential models, e.g. the over-dispersed chain ladder model. In particular, we show that the suggested approach retrieves Mack's famous MSEP formula for the ultimo claim amount and we provide a simple derivation of the MSEP for the claims development result for the distribution-free chain ladder model and discuss why the resulting formula differs from already known similar results. Further, we illustrate the effect of using either conditional or unconditional resampling in the calculation of MSEP for the ultimo claim amount for the distribution-free chain ladder model. The resulting difference turns out to be small - something which can be shown to hold asymptotically for a wider class of sequential linear models to which the distribution-free chain ladder model is a special case.
If time permits we will discuss how the method applies to point process based micro models, distribution-free model selection and Bayesian analyses.
This talk is based on joint work together with Filip Lindskog and Felix Wahl.
7th November - Cormac Bryce
Faculty of Actuarial Science & Insurance, Cass Business School.
The Dynamics of Researcher Journal Quality Perception and Ranking Divergence
Biography: Dr Cormac Bryce is a Senior Lecturer in Insurance at Cass, and is a member of the Faculty of Actuarial Science and Insurance. His multi-method research spans from human behaviour in financial organisations to the effect of regulation, incentive structures and employee benchmarking on organisational behaviour within the aviation and financial services industry. Cormac’s research focus has been grounded in the areas of error-reporting climate and the effects of risk events on the market sentiment of financial services organisations. In his most recent industry report written on behalf of the ACCA Cormac investigated to role of leadership in boardroom strategic decision making spanning from organisations in the FTSE 100 through to the third sector and SMEs.
Abstract: We explore the drivers of researcher's perceptions around academic journal quality, and how these perceptions converge or diverge with rankings through a large-scale survey of UK business school researchers. Our survey was conducted in advance of the release of the new Academic Journal Guide (AJG) rankings list in early 2018, and resulted in 19,250 individual journal rankings. Personal and institutional demographics as well as behavioural factors are major drivers of quality perception. Of particular importance is a researcher's connection to, and investment in, the AJG system of ranking. Individual journal linkages, such as being a reviewer or having submitted to a journal, are also linked to higher perception of journal quality at odds with actual journal rank. Our research thus provides new insights into how researchers interact with journal ranking systems in light of their own perceptions of quality. We propose how the key stakeholders in journal rankings; business schools, journal editors, ranking bodies, and the business and management community can incorporate these findings to ensure coherency between individual, school, and national assessments of research quality.
21st November - Cecile Proust-Lima
University of Bordeaux
Individual Dynamic Predictions of Disease Progression given Longitudinal Biomarker History
Abstract: After the diagnosis of a disease, one central objective is to predict cumulative probabilities of events (e.g. clinical relapse) from the individual information collected up to a prediction time, usually including biomarker repeated measurements. Even before a diagnosis, cumulative probability of disease can be computed from the individual screening history or exposure records. Such predictions based on information repeatedly collected over time can be (dynamically) updated as soon as new information becomes available. In this presentation, I will give a short overview about how dynamic predictions can be defined, and what are the difficulties with their computation and the evaluation of their predictive accuracy. For their computation, two main approaches have been proposed: the joint modelling approach which simultaneously models the longitudinal and the time-to-event processes, and the landmarking approach which directly focuses on the time to predict by conditioning on the repeated information collected up to the given landmark time. I will compare the two approaches notably in terms of predictive accuracy, efficiency and robustness to model assumptions using a simulation study. The presentation will be illustrated with the prediction of competing causes of prostate cancer progression from the history of prostate-specific antigen (PSA), the main biomarker in Prostate Cancer.
23rd January 2019 - Andrés M. Villegas
A Data Analytics Paradigm for the Construction, Selection, and Evaluation of Mortality Models
Humanity has made, and continues to make, significant progress in averting and delaying death, which burdens society with increased longevity costs. This has brought to the fore the critical importance of mortality forecasting for actuaries and demographers. Consequently, numerous mortality models have been proposed, with the most popular and commonly-referenced models belonging to a generalised age-period-cohort framework. These models decompose observed historical mortality rates across the dimensions of age, period, and cohort (or year-of-birth), which can then be extrapolated to forecast future outcomes. Recently, a large number of models have been proposed within this framework, many of which are over-parameterised and produce spurious forecasts, particularly over long horizons and for noisy data sets.
In this paper we exploit data analytics techniques to provide a comprehensive framework to construct, select, and evaluate discrete-time age-period-cohort mortality models. To devise this robust framework, we leverage two key statistical learning tools – cross validation and regularisation – to draw as much insight as possible from limited data sets. We first propose a cross validation framework for model selection, which can be tailored to determine the features of mortality models that are desired for different actuarial applications, including period and cohort-based forecasting. This enables the answering of questions regarding the effects of population size and structure, age, and forecasting basis and horizon on the preferred model selection. We also present a regularisation approach to construct bespoke mortality models by automatically selecting the most appropriate parametric forms to best describe and forecast particular data sets, using a trade-off between complexity and parsimony. We illustrate this using empirical data from the Human Mortality Database and simulated data sets.
Andrés Villegas is a Lecturer at the School of Risk and Actuarial Studies and an Associate Investigator at the ARC Centre of Excellence in Population Ageing Research (CEPAR) where he was previously a Research Fellow. Andrés completed his doctoral studies at Cass Business School in London focusing on the modelling and projection of mortality. Before his doctoral studies he obtained an MSc degree in Industrial Engineering from Universidad de Los Andes (Colombia) and worked as a risk analyst at one of the biggest Colombian life insurance companies. Andrés’s research interests include mortality modelling, longevity risk management and the application of optimisation techniques in actuarial science and finance.
30th January 2019 - Ahmed Barakat
Operational Risk and Reputation in Financial Institutions: Does Media Tone Make a Difference?
Operational risk announcements are unexpected adverse media news that potentially harm the reputation of financial institutions. This paper examines the equity-based and debt-based reputational effects of financial sentiment tones in operational risk announcements and shows how such reputational effects are moderated by alternative sources of public information. Our analysis reveals that the net negative tone and litigious tone have adverse reputational effects, and the uncertainty tone mitigates the adverse reputational impact. Additionally, alternative, simultaneous sources of information neutralize the reputational effects of textual tones. First, third-party information about the event (i.e. regulatory announcements and final settlements) dissolves the favorable (adverse) reputational impact of the uncertainty tone (litigious tone). Second, loss amount disclosure and firm recognition substitute the reputational effects of the net negative tone and uncertainty tone only in Anglo-Saxon countries and market-based economies. Overall, our findings indicate that the reputational effects of the media materialize most when there is lack of certain, quantifiable and regulated public information about the operational risk event.
Dr Ahmed Barakat is an Assistant Professor in Banking at Nottingham University Business School. He has written several research papers on risk management, corporate governance, banking, insurance and financial reporting quality which are published in high quality international academic journals such as Journal of Banking and Finance and International Review of Financial Analysis. He also serves as a referee for several reputable academic journals such as International Journal of Accounting, Corporate Governance: An International Review and Journal of Risk Research. He teaches undergraduate and postgraduate modules as well as supervises MSc dissertations, MBA projects and PhD theses in risk management, corporate governance, financial institutions, accounting and auditing. He has well-established experience in designing and delivering executive training courses and providing professional financial consultancy to international organisations in Europe and the Gulf area.
Seminars take place on Wednesdays 16:00 to 17:00 in Room 2005. Light refreshments available at 15:45 in seminar room. The seminars are open to everyone.
Please contact: firstname.lastname@example.org for further information.