Download or read book Actuarial Modelling of Extremal Events Using Transformed Generalized Extreme Value Distributions and Generalized Pareto Distributions written by Zhongxian Han and published by . This book was released on 2003 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: In 1928, Extreme Value Theory (EVT) originated in work of Fisher and Tippett describing the behavior of maximum of independent and identically distributed random variables. Various applications have been implemented successfully in many fields such as: actuarial science, hydrology, climatology, engineering, and economics and finance. This paper begins with introducing examples that extreme value theory comes to encounter. Then classical results from EVT are reviewed and the current research approaches are introduced. In particular, statistical methods are emphasized in detail for the modeling of extremal events. A case study of hurricane damages over the last century is presented using the "excess over threshold" (EOT) method. In most actual cases, the range of the data collected is finite with an upper bound while the fitted Generalized Extreme Value (GEV) and Generalized Pareto (GPD) distributions have infinite tails. Traditionally this is treated as trivial based on the assumption that the upper bound is so large that no significant result is affected when it is replaced by infinity. However, in certain circumstances, the models can be improved by implementing more specific techniques. Different transforms are introduced to rescale the GEV and GPD distributions so that they have finite supports. All classical methods can be applied directly to transformed models if the upper bound is known. In case the upper bound is unknown, we set up models with one additional parameter based on transformed distributions. Properties of the transform functions are studied and applied to find the cumulative density functions (cdfs) and probability density functions (pdfs) of the transformed distributions. We characterize the transformed distribution from the plots of their cdfs and mean residual life. Then we apply our findings to determine which transformed distribution should be used in the models. At the end some results of parameter estimation are obtained through the maximum likelihood method.

Download or read book Extreme Value Modeling and Risk Analysis written by Dipak K. Dey and published by CRC Press. This book was released on 2016-01-06 with total page 538 pages. Available in PDF, EPUB and Kindle. Book excerpt: Extreme Value Modeling and Risk Analysis: Methods and Applications presents a broad overview of statistical modeling of extreme events along with the most recent methodologies and various applications. The book brings together background material and advanced topics, eliminating the need to sort through the massive amount of literature on the subje

Download or read book Robust and Efficient Fitting of the Generalized Pareto Distribution with Actuarial Applications in View written by Vytaras Brazauskas and published by . This book was released on 2014 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to advances in extreme value theory, the generalized Pareto distribution (GPD) emerged as a natural family for modeling exceedances over a high threshold. Its importance in applications (e.g., insurance, finance, economics, engineering and numerous other fields) can hardly be overstated and is widely documented. However, despite the sound theoretical basis and wide applicability, fitting of this distribution in practice is not a trivial exercise. Traditional methods such as maximum likelihood and method-of-moments are undefined in some regions of the parameter space. Alternative approaches exist but they lack either robustness (e.g., probability-weighted moments) or efficiency (e.g., method-of-medians), or present significant numerical problems (e.g., minimum-divergence procedures). In this article, we propose a computationally tractable method for fitting the GPD, which is applicable for all parameter values and offers competitive trade-offs between robustness and efficiency. The method is based on 'trimmed moments'. Large-sample properties of the new estimators are provided, and their small-sample behavior under several scenarios of data contamination is investigated through simulations. We also study the effect of our methodology on actuarial applications. In particular, using the new approach, we fit the GPD to the Danish insurance data and apply the fitted model to a few risk measurement and ratemaking exercises.

Download or read book Actuarial Science written by Source Wikipedia and published by University-Press.org. This book was released on 2013-09 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Pages: 143. Chapters: Extreme value theory, Pareto distribution, Disease, Life expectancy, 100-year flood, Financial economics, Demography, Risk management, Discounting, Actuary, Value at risk, Time value of money, Workers' compensation, Regression analysis, Maximum life span, Enterprise risk management, Generalized linear model, Risk aversion, Copula, Reinsurance, Compound interest, Actuarial notation, Asset allocation, Medical underwriting, Wald's equation, Financial modeling, Life annuity, Retirement spend down, Failure rate, Stable and tempered stable distributions with volatility clustering - financial applications, Fictional actuaries, Mortality rate, Actuarial exam, RiskMetrics, Auto insurance risk selection, Model risk, Ulpian's life table, Coherent risk measure, Mathematical finance, Years of potential life lost, Ruin theory, Stochastic modelling, Insurance cycle, German Statutory Accident Insurance, Asset/liability modeling, Anders Lindstedt, Risk premium, Kaplan-Meier estimator, Lee-Carter Model, Worker's compensation, Mathematical statistics, Cresta, Catastrophe Risk Evaluating and Standardizing Target Accumulations, Actuarial present value, (a, b,0) class of distributions, Catastrophe modeling, Gompertz-Makeham law of mortality, Actuarial reserves, International Association of Black Actuaries, Compound annual growth rate, Liability-driven investment strategy, Decrement table, Risk modeling, Economic capital, Ogden tables, Financial engineering, John Graunt, Panjer recursion, Certified Risk Manager, Age stratification, Credibility theory, List of actuarial topics, Risk adjusted return on capital, De Moivre's law, Modeling and analysis of financial markets, Force of mortality, European Embedded Value, CAS Exam 7C 2009, Cohort, General insurance, Reliability theory, Tail value at risk, Insurable risk, Credit Valuation..

Download or read book Modeling of Extreme Events and Stress Testing Analysis written by Ronny Suarez and published by . This book was released on 2010 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book we estimated value at risk and return level using extreme value theory as an alternative mechanism to generate stress scenarios. The methodology is applied to S&P 500 Index and to dollar / euro exchange rate. Additionally, the estimation of a generalized extreme value distribution or a generalized pareto distribution combined with the mixing unconditional disturbances model is developed as a tool that can be used to create values for sensitivity test purpose.

Download or read book Extreme Value Index Estimation with Applications to Modeling Extreme Insurance Losses and Sea Surface Temperatures written by John B. Henry and published by . This book was released on 2009 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: The extreme value index (EVI) links the generalized extreme value (GEV) distribution and the generalized Pareto (GP) distribution. These two distributions are fundamental in extreme value theory (EVT), with the GEV distribution being the only possible non-degenerate limiting distribution of properly normalized maxima of iid random variables, and the GP distribution appearing as the limit distribution of scaled excesses over high thresholds. The reciprocal of the EVI is know as the Pareto tail index (provided the EVI is positive). A new tail index estimator is proposed here that is obtained by matching general theoretical harmonic moments with corresponding empirical moments. Theoretical properties of the estimator are provided along with applications and a comparison to other tail index estimators. A tail index estimator suitable for partitioned data is also given. Having only partitioned data to work with is a circumstance sometimes faced by actuaries. Strengths and weaknesses of this estimator are explored through simulation. An application of the estimator to real world partitioned insurance data is given. The sign of the EVI is of interest when one is interested in testing whether or not a distribution has a bounded tail. In this work, the controversial thermostat hypothesis for sea surface temperature (SST) is investigated in this framework. A GP model using SST data from the western Pacific warm pool provides some evidence of a temperature upper bound between 31.2C and 32.0C. This estimate is compared those obtained elsewhere using the ocean heat budget and other physical models.

Download or read book Modelling Operational Risk Using a Bayesian Approach to Extreme Value Theory written by María Elena Rivera Mancía and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "Extreme-value theory is concerned with the tail behaviour of probability distributions. In recent years, it has found many applications in areas as diverse as hydrology, actuarial science, and finance, where complex phenomena must often be modelled from a small number of observations.Extreme-value theory can be used to assess the risk of rare events either through the block maxima or peaks-over-threshold method. The choice of threshold is both influential and delicate, as a balance between the bias and variance of the estimates is required. At present, this threshold is often chosen arbitrarily, either graphically or by setting it as some high quantile of the data.Bayesian inference is an alternative to deal with this problem by treating the threshold as a parameter in the model. In addition, a Bayesian approach allows for the incorporation of internal and external observations in combination with expert opinion, thereby providing a natural probabilistic framework to evaluate risk models.This thesis presents a Bayesian inference framework for extremes. We focus on a model proposed by Behrens et al. (2004), where an analysis of extremes is performed using a mixture model that combines a parametric form for the centre and a Generalized Pareto Distribution (GPD) for the tail of the distribution. Our approach accounts for all the information available in making inference about the unknown parameters from both distributions, the threshold included. A Bayesian analysis is then performed by using expert opinions to determine the parameters for prior distributions; posterior inference is carried out through Markov Chain Monte Carlo methods. We apply this methodology to operational risk data to analyze its performance.The contributions of this thesis can be outlined as follows:-Bayesian models have been barely explored in operational risk analysis. In Chapter 3, we show how these models can be adapted to operational risk analysis using fraud data collected by different banks between 2007 and 2010. By combining prior information to the data, we can estimate the minimum capital requirement and risk measures such as the Value-at-Risk (VaR) and the Expected Shortfall (ES) for each bank.-The use of expert opinion plays a fundamental role in operational risk modelling. However, most of time this issue is not addressed properly. In Chapter 4, we consider the context of the problem and show how to construct a prior distribution based on measures that experts are familiar with, including VaR and ES. The purpose is to facilitate prior elicitation and reproduce expert judgement faithfully.-In Section 4.3, we describe techniques for the combination of expert opinions. While this issue has been addressed in other fields, it is relatively recent in our context. We examine how different expert opinions may influence the posterior distribution and how to build a prior distribution in this case. Results are presented on simulated and real data.-In Chapter 5, we propose several new mixture models with Gamma and Generalized Pareto elements. Our models improve upon previous work by Behrens et al. (2004) since the loss distribution is either continuous at a fixed quantile or it has continuous first derivative at the blend point. We also consider the cases when the scaling is arbitrary and when the density is discontinuous.-Finally, we introduce two nonparametric models. The first one is based on the fact that the GPD model can be represented as a Gamma mixture of exponential distributions, while the second uses a Dirichlet process prior on the parameters of the GPD model." --

Download or read book Extreme Value Theory and Applications written by J. Galambos and published by Springer Science & Business Media. This book was released on 2013-12-01 with total page 526 pages. Available in PDF, EPUB and Kindle. Book excerpt: It appears that we live in an age of disasters: the mighty Missis sippi and Missouri flood millions of acres, earthquakes hit Tokyo and California, airplanes crash due to mechanical failure and the seemingly ever increasing wind speeds make the storms more and more frightening. While all these may seem to be unexpected phenomena to the man on the street, they are actually happening according to well defined rules of science known as extreme value theory. We know that records must be broken in the future, so if a flood design is based on the worst case of the past then we are not really prepared against floods. Materials will fail due to fatigue, so if the body of an aircraft looks fine to the naked eye, it might still suddenly fail if the aircraft has been in operation over an extended period of time. Our theory has by now penetrated the so cial sciences, the medical profession, economics and even astronomy. We believe that our field has come of age. In or~er to fully utilize the great progress in the theory of extremes and its ever increasing acceptance in practice, an international conference was organized in which equal weight was given to theory and practice. This book is Volume I of the Proceedings of this conference. In selecting the papers for Volume lour guide was to have authoritative works with a large variety of coverage of both theory and practice.

Download or read book Computational Actuarial Science with R written by Arthur Charpentier and published by CRC Press. This book was released on 2014-08-26 with total page 652 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Hands-On Approach to Understanding and Using Actuarial Models Computational Actuarial Science with R provides an introduction to the computational aspects of actuarial science. Using simple R code, the book helps you understand the algorithms involved in actuarial computations. It also covers more advanced topics, such as parallel computing and C/C++ embedded codes. After an introduction to the R language, the book is divided into four parts. The first one addresses methodology and statistical modeling issues. The second part discusses the computational facets of life insurance, including life contingencies calculations and prospective life tables. Focusing on finance from an actuarial perspective, the next part presents techniques for modeling stock prices, nonlinear time series, yield curves, interest rates, and portfolio optimization. The last part explains how to use R to deal with computational issues of nonlife insurance. Taking a do-it-yourself approach to understanding algorithms, this book demystifies the computational aspects of actuarial science. It shows that even complex computations can usually be done without too much trouble. Datasets used in the text are available in an R package (CASdatasets).

Download or read book The Handbook of Graph Algorithms and Applications written by Krishnaiyan Thulasiraman and published by CRC Press. This book was released on 2015-05-12 with total page 656 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Handbook of Graph Algorithms, Volume II : Applications focuses on a wide range of algorithmic applications, including graph theory problems. The book emphasizes new algorithms and approaches that have been triggered by applications. The approaches discussed require minimal exposure to related technologies in order to understand the material. Each chapter is devoted to a single application area, from VLSI circuits to optical networks to program graphs, and features an introduction by a pioneer researcher in that particular field. The book serves as a single-source reference for graph algorithms and their related applications.

Download or read book Dissertation Abstracts International written by and published by . This book was released on 2004 with total page 882 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Actuarial Modelling of Claim Counts written by Michel Denuit and published by John Wiley & Sons. This book was released on 2007-07-27 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: There are a wide range of variables for actuaries to consider when calculating a motorist’s insurance premium, such as age, gender and type of vehicle. Further to these factors, motorists’ rates are subject to experience rating systems, including credibility mechanisms and Bonus Malus systems (BMSs). Actuarial Modelling of Claim Counts presents a comprehensive treatment of the various experience rating systems and their relationships with risk classification. The authors summarize the most recent developments in the field, presenting ratemaking systems, whilst taking into account exogenous information. The text: Offers the first self-contained, practical approach to a priori and a posteriori ratemaking in motor insurance. Discusses the issues of claim frequency and claim severity, multi-event systems, and the combinations of deductibles and BMSs. Introduces recent developments in actuarial science and exploits the generalised linear model and generalised linear mixed model to achieve risk classification. Presents credibility mechanisms as refinements of commercial BMSs. Provides practical applications with real data sets processed with SAS software. Actuarial Modelling of Claim Counts is essential reading for students in actuarial science, as well as practicing and academic actuaries. It is also ideally suited for professionals involved in the insurance industry, applied mathematicians, quantitative economists, financial engineers and statisticians.

Download or read book Modeling Extreme Events written by and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose a dynamic semi-parametric framework to study time variation in tail pa rameters. The framework builds on the Generalized Pareto Distribution (GPD) for modeling peaks over thresholds as in Extreme Value Theory, but casts the model in a conditional framework to allow for time-variation in the tail shape parameters. The score-driven updates used improve the expected Kullback-Leibler divergence between the model and the true data generating process on every step even if the GPD only fits approximately and the model is mis-specified, as will be the case in any finite sample. This is confirmed in simulations. Using the model, we find that Eurosystem sovereign bond purchases during the euro area sovereign debt crisis had a beneficial impact on extreme upper tail quantiles, leaning against the risk of extremely adverse market out comes while active.

Download or read book Statistics of Extremes written by Jan Beirlant and published by John Wiley & Sons. This book was released on 2006-03-17 with total page 522 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research in the statistical analysis of extreme values has flourished over the past decade: new probability models, inference and data analysis techniques have been introduced; and new application areas have been explored. Statistics of Extremes comprehensively covers a wide range of models and application areas, including risk and insurance: a major area of interest and relevance to extreme value theory. Case studies are introduced providing a good balance of theory and application of each model discussed, incorporating many illustrated examples and plots of data. The last part of the book covers some interesting advanced topics, including time series, regression, multivariate and Bayesian modelling of extremes, the use of which has huge potential.

Download or read book Effective Statistical Learning Methods for Actuaries I written by Michel Denuit and published by Springer Nature. This book was released on 2019-09-03 with total page 441 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book summarizes the state of the art in generalized linear models (GLMs) and their various extensions: GAMs, mixed models and credibility, and some nonlinear variants (GNMs). In order to deal with tail events, analytical tools from Extreme Value Theory are presented. Going beyond mean modeling, it considers volatility modeling (double GLMs) and the general modeling of location, scale and shape parameters (GAMLSS). Actuaries need these advanced analytical tools to turn the massive data sets now at their disposal into opportunities. The exposition alternates between methodological aspects and case studies, providing numerical illustrations using the R statistical software. The technical prerequisites are kept at a reasonable level in order to reach a broad readership. This is the first of three volumes entitled Effective Statistical Learning Methods for Actuaries. Written by actuaries for actuaries, this series offers a comprehensive overview of insurance data analytics with applications to P&C, life and health insurance. Although closely related to the other two volumes, this volume can be read independently.

Download or read book Laws of Small Numbers Extremes and Rare Events written by Michael Falk and published by Birkhäuser. This book was released on 2013-11-11 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the publication of the first edition of this seminar book, the theory and applications of extremes and rare events have seen increasing interest. Laws of Small Numbers gives a mathematically oriented development of the theory of rare events underlying various applications. The new edition incorporates numerous new results on about 130 additional pages. Part II, added in the second edition, discusses recent developments in multivariate extreme value theory.

Download or read book Extreme Value Models in a Conditional Duration Intensity Framework written by Rodrigo Herrera and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The analysis of return series from financial markets is often based on the Peaks-over-threshold (POT) model. This model assumes independent and identically distributed observations and therefore a Poisson process is used to characterize the occurrence of extreme events. However, stylized facts such as clustered extremes and serial dependence typically violate the assumption of independence. In this paper we concentrate on an alternative approach to overcome these difficulties. We consider the stochastic intensity of the point process of exceedances over a threshold in the framework of irregularly spaced data. The main idea is to model the time between exceedances through an Autoregressive Conditional Duration (ACD) model, while the marks are still being modelled by generalized Pareto distributions. The main advantage of this approach is its capability to capture the short-term behaviour of extremes without involving an arbitrary stochastic volatility model or a prefiltration of the data, which certainly impacts the estimation. We make use of the proposed model to obtain an improved estimate for the Value at Risk. The model is then applied and illustrated to transactions data from Bayer AG, a blue chip stock from the German stock market index DAX. -- Extreme value theory ; autoregressive conditional duration ; value at risk ; self-exciting point process ; conditional intensity