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Estimation Of Conditional Mean Squared Error Of Prediction For Claims Reserving
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Book Synopsis Estimation of Conditional Mean Squared Error of Prediction for Claims Reserving by : Mathias Lindholm
Download or read book Estimation of Conditional Mean Squared Error of Prediction for Claims Reserving written by Mathias Lindholm and published by . This book was released on 2019 with total page 39 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper studies estimation of conditional mean squared error of prediction, conditional on what is known at the time of prediction. The particular problem considered is the assessment of actuarial reserving methods given data in the form of runoff triangles (trapezoids), where the use of prediction assessment based on out-of-sample performance is not an option. The prediction assessment principle advocated here can be viewed as a generalization of Akaike's final prediction error. A direct application of this simple principle in the setting of a data generating process given in terms of a sequence of general linear models yields an estimator of conditional mean square error of prediction that can be computed explicitly for a wide range of models within this model class. Mack's distribution-free chain ladder model and the corresponding estimator of the prediction error for the ultimate claim amount is shown to be a special case. It is demonstrated that the prediction assessment principle easily applies to quite different data generating processes and results in estimators that have been studied in the literature.
Book Synopsis Stochastic Claims Reserving Methods in Insurance by : Mario V. Wüthrich
Download or read book Stochastic Claims Reserving Methods in Insurance written by Mario V. Wüthrich and published by John Wiley & Sons. This book was released on 2008-04-30 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: Claims reserving is central to the insurance industry. Insurance liabilities depend on a number of different risk factors which need to be predicted accurately. This prediction of risk factors and outstanding loss liabilities is the core for pricing insurance products, determining the profitability of an insurance company and for considering the financial strength (solvency) of the company. Following several high-profile company insolvencies, regulatory requirements have moved towards a risk-adjusted basis which has lead to the Solvency II developments. The key focus in the new regime is that financial companies need to analyze adverse developments in their portfolios. Reserving actuaries now have to not only estimate reserves for the outstanding loss liabilities but also to quantify possible shortfalls in these reserves that may lead to potential losses. Such an analysis requires stochastic modeling of loss liability cash flows and it can only be done within a stochastic framework. Therefore stochastic loss liability modeling and quantifying prediction uncertainties has become standard under the new legal framework for the financial industry. This book covers all the mathematical theory and practical guidance needed in order to adhere to these stochastic techniques. Starting with the basic mathematical methods, working right through to the latest developments relevant for practical applications; readers will find out how to estimate total claims reserves while at the same time predicting errors and uncertainty are quantified. Accompanying datasets demonstrate all the techniques, which are easily implemented in a spreadsheet. A practical and essential guide, this book is a must-read in the light of the new solvency requirements for the whole insurance industry.
Book Synopsis Bayesian Claims Reserving Methods in Non-life Insurance with Stan by : Guangyuan Gao
Download or read book Bayesian Claims Reserving Methods in Non-life Insurance with Stan written by Guangyuan Gao and published by Springer. This book was released on 2018-12-31 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book first provides a review of various aspects of Bayesian statistics. It then investigates three types of claims reserving models in the Bayesian framework: chain ladder models, basis expansion models involving a tail factor, and multivariate copula models. For the Bayesian inferential methods, this book largely relies on Stan, a specialized software environment which applies Hamiltonian Monte Carlo method and variational Bayes.
Book Synopsis Discounting Over the Triangle by : Luca Passalacqua
Download or read book Discounting Over the Triangle written by Luca Passalacqua and published by . This book was released on 2008 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper we address the computation of the mean square error of prediction in stochastic claim reserving methods when future claims are discounted. We derive the expression of the covariances between diagonals of the development triangle, which are needed in order to perform the estimation, also providing a particularly compact form. The mean square error of the prediction can then be used to compute risk margins in the determination of technical provisions required by the Solvency II framework.
Book Synopsis Market-Consistent Actuarial Valuation by : Mario V. Wüthrich
Download or read book Market-Consistent Actuarial Valuation written by Mario V. Wüthrich and published by Springer. This book was released on 2016-10-22 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the third edition of this well-received textbook, presenting powerful methods for measuring insurance liabilities and assets in a consistent way, with detailed mathematical frameworks that lead to market-consistent values for liabilities. Topics covered are stochastic discounting with deflators, valuation portfolio in life and non-life insurance, probability distortions, asset and liability management, financial risks, insurance technical risks, and solvency. Including updates on recent developments and regulatory changes under Solvency II, this new edition of Market-Consistent Actuarial Valuation also elaborates on different risk measures, providing a revised definition of solvency based on industry practice, and presents an adapted valuation framework which takes a dynamic view of non-life insurance reserving risk.
Book Synopsis On Estimating Conditional Mean-Squared Prediction Error in Autoregressive Models by : Ching-Kang Ing
Download or read book On Estimating Conditional Mean-Squared Prediction Error in Autoregressive Models written by Ching-Kang Ing and published by . This book was released on 2003 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Zhang and Shaman considered the problem of estimating the conditional mean-squared prediciton error (CMSPE) for a Gaussian autoregressive (AR) process. They used the final prediction error (FPE) of Akaike to estimate CMSPE and proposed that FPE's effectiveness be judged by its asymptotic correlation with CMSPE. However, as pointed out by Kabaila and He, the derivation of this correlation by Zhang and Shaman is incomplete, and the performance of FPE in estimating CMSPE is also poor in Kabaila and He's simulation study. Kabaila and He further proposed an alternative estimator of CMSPE, V, in the stationary AR(1) model. They reported that V has a larger normalized correlation with CMSPE through Monte Carlo simulation results. In this paper, we propose a generalization of V, V, in the higher-order AR model, and obtain the asymptotic correlation of FPE and V with CMSPE. We show that the limit of the normalized correlation of V with CMSPE is larger than that of FPE with CMSPE, and hence Kabaila and He's finding is justified theoretically. In addition, the performances of the above estimators of CMSPE are re-examined in terms of mean-squared errors (MSE). Our main conclusion is that from the MSE point of view, V is the best choice among a family of asymptotically unbiased estimators of CMSPE including FPE and V as its special cases.
Book Synopsis Stochastic Loss Reserving Using Generalized Linear Models by : Greg Taylor
Download or read book Stochastic Loss Reserving Using Generalized Linear Models written by Greg Taylor and published by . This book was released on 2016-05-04 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this monograph, authors Greg Taylor and Gráinne McGuire discuss generalized linear models (GLM) for loss reserving, beginning with strong emphasis on the chain ladder. The chain ladder is formulated in a GLM context, as is the statistical distribution of the loss reserve. This structure is then used to test the need for departure from the chain ladder model and to consider natural extensions of the chain ladder model that lend themselves to the GLM framework.
Book Synopsis Claims Reserving in General Insurance by : David Hindley
Download or read book Claims Reserving in General Insurance written by David Hindley and published by Cambridge University Press. This book was released on 2017-10-26 with total page 514 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a comprehensive and accessible reference source that documents the theoretical and practical aspects of all the key deterministic and stochastic reserving methods that have been developed for use in general insurance. Worked examples and mathematical details are included, along with many of the broader topics associated with reserving in practice. The key features of reserving in a range of different contexts in the UK and elsewhere are also covered. The book contains material that will appeal to anyone with an interest in claims reserving. It can be used as a learning resource for actuarial students who are studying the relevant parts of their professional bodies' examinations, as well as by others who are new to the subject. More experienced insurance and other professionals can use the book to refresh or expand their knowledge in any of the wide range of reserving topics covered in the book.
Download or read book Claim Models written by Greg Taylor and published by MDPI. This book was released on 2020-04-15 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: This collection of articles addresses the most modern forms of loss reserving methodology: granular models and machine learning models. New methodologies come with questions about their applicability. These questions are discussed in one article, which focuses on the relative merits of granular and machine learning models. Others illustrate applications with real-world data. The examples include neural networks, which, though well known in some disciplines, have previously been limited in the actuarial literature. This volume expands on that literature, with specific attention to their application to loss reserving. For example, one of the articles introduces the application of neural networks of the gated recurrent unit form to the actuarial literature, whereas another uses a penalized neural network. Neural networks are not the only form of machine learning, and two other papers outline applications of gradient boosting and regression trees respectively. Both articles construct loss reserves at the individual claim level so that these models resemble granular models. One of these articles provides a practical application of the model to claim watching, the action of monitoring claim development and anticipating major features. Such watching can be used as an early warning system or for other administrative purposes. Overall, this volume is an extremely useful addition to the libraries of those working at the loss reserving frontier.
Book Synopsis Issues in Insurance and Risk Management: 2011 Edition by :
Download or read book Issues in Insurance and Risk Management: 2011 Edition written by and published by ScholarlyEditions. This book was released on 2012-01-09 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: Issues in Insurance and Risk Management / 2011 Edition is a ScholarlyEditions™ eBook that delivers timely, authoritative, and comprehensive information about Insurance and Risk Management. The editors have built Issues in Insurance and Risk Management: 2011 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about Insurance and Risk Management in this eBook to be deeper than what you can access anywhere else, as well as consistently reliable, authoritative, informed, and relevant. The content of Issues in Insurance and Risk Management: 2011 Edition has been produced by the world’s leading scientists, engineers, analysts, research institutions, and companies. All of the content is from peer-reviewed sources, and all of it is written, assembled, and edited by the editors at ScholarlyEditions™ and available exclusively from us. You now have a source you can cite with authority, confidence, and credibility. More information is available at http://www.ScholarlyEditions.com/.
Book Synopsis Handbook on Loss Reserving by : Michael Radtke
Download or read book Handbook on Loss Reserving written by Michael Radtke and published by Springer. This book was released on 2016-10-26 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook presents the basic aspects of actuarial loss reserving. Besides the traditional methods, it also includes a description of more recent ones and a discussion of certain problems occurring in actuarial practice, like inflation, scarce data, large claims, slow loss development, the use of market statistics, the need for simulation techniques and the task of calculating best estimates and ranges of future losses. In property and casualty insurance the provisions for payment obligations from losses that have occurred but have not yet been settled usually constitute the largest item on the liabilities side of an insurer's balance sheet. For this reason, the determination and evaluation of these loss reserves is of considerable economic importance for every property and casualty insurer. Actuarial students, academics as well as practicing actuaries will benefit from this overview of the most important actuarial methods of loss reserving by developing an understanding of the underlying stochastic models and how to practically solve some problems which may occur in actuarial practice.
Book Synopsis On the Prediction Error in Several Claims Reserves Estimation Methods by :
Download or read book On the Prediction Error in Several Claims Reserves Estimation Methods written by and published by . This book was released on 2009 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Addendum to "Analytic and Bootstrap Estimates of Prediction Errors in Claims Reserving" by : Peter D. England
Download or read book Addendum to "Analytic and Bootstrap Estimates of Prediction Errors in Claims Reserving" written by Peter D. England and published by . This book was released on 2001 with total page 19 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Claims Reserving with a Stochastic Vector Projection by : Luis Portugal
Download or read book Claims Reserving with a Stochastic Vector Projection written by Luis Portugal and published by . This book was released on 2018 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the last three decades, a variety of stochastic reserving models has been proposed in the general insurance literature mainly using (or reproducing) the eminent Chain-Ladder claims reserving estimates. In practice, when the data doesn't satisfy the Chain-Ladder assumptions, high prediction errors might occur. Thus, in this paper, a combined methodology is proposed which is based on the stochastic vector projection method and uses the regression through the origin approach of Murphy (1994), but with heteroscedastic errors instead, and different to those that used by Mack (1993, 1994). Furthermore, the Mack (1993) distribution-free model appears to have higher prediction errors when it is compared with the pro-posed one, particularly, for data sets with increasing (regular) trends. Finally, three empirical examples with irregular and regular data sets illustrate the theoretical findings, and the concepts of best estimate and risk margin are reported.
Book Synopsis Non-Life Insurance Mathematics by : Erwin Straub
Download or read book Non-Life Insurance Mathematics written by Erwin Straub and published by Springer Science & Business Media. This book was released on 1997-06-19 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: A good mixture of practical problems and their solutions. Addresses students with no knowledge of insurance and insurance practitioners who recall mathematics only from some distance. Prerequisites are basic calculus and probability theory. Annotation copyrighted by Book News, Inc., Portland, OR
Download or read book ASTIN Bulletin written by and published by . This book was released on 2006 with total page 688 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Advances in Computational Intelligence, Part IV by : Salvatore Greco
Download or read book Advances in Computational Intelligence, Part IV written by Salvatore Greco and published by Springer. This book was released on 2012-07-23 with total page 707 pages. Available in PDF, EPUB and Kindle. Book excerpt: These four volumes (CCIS 297, 298, 299, 300) constitute the proceedings of the 14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2012, held in Catania, Italy, in July 2012. The 258 revised full papers presented together with six invited talks were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on fuzzy machine learning and on-line modeling; computing with words and decision making; soft computing in computer vision; rough sets and complex data analysis: theory and applications; intelligent databases and information system; information fusion systems; philosophical and methodological aspects of soft computing; basic issues in rough sets; 40th anniversary of the measures of fuziness; SPS11 uncertainty in profiling systems and applications; handling uncertainty with copulas; formal methods to deal with uncertainty of many-valued events; linguistic summarization and description of data; fuzzy implications: theory and applications; sensing and data mining for teaching and learning; theory and applications of intuitionistic fuzzy sets; approximate aspects of data mining and database analytics; fuzzy numbers and their applications; information processing and management of uncertainty in knowledge-based systems; aggregation functions; imprecise probabilities; probabilistic graphical models with imprecision: theory and applications; belief function theory: basics and/or applications; fuzzy uncertainty in economics and business; new trends in De Finetti's approach; fuzzy measures and integrals; multi criteria decision making; uncertainty in privacy and security; uncertainty in the spirit of Pietro Benvenuti; coopetition; game theory; probabilistic approach.
