Loss Reserving

Loss Reserving
Author: Gregory Taylor
Publisher: Springer Science & Business Media
Total Pages: 396
Release: 2012-12-06
Genre: Business & Economics
ISBN: 1461545838

All property and casualty insurers are required to carry out loss reserving as a statutory accounting function. Thus, loss reserving is an essential sphere of activity, and one with its own specialized body of knowledge. While few books have been devoted to the topic, the amount of published research literature on loss reserving has almost doubled in size during the last fifteen years. Greg Taylor's book aims to provide a comprehensive, state-of-the-art treatment of loss reserving that reflects contemporary research advances to date. Divided into two parts, the book covers both the conventional techniques widely used in practice, and more specialized loss reserving techniques employing stochastic models. Part I, Deterministic Models, covers very practical issues through the abundant use of numerical examples that fully develop the techniques under consideration. Part II, Stochastic Models, begins with a chapter that sets up the additional theoretical material needed to illustrate stochastic modeling. The remaining chapters in Part II are self-contained, and thus can be approached independently of each other. A special feature of the book is the use throughout of a single real life data set to illustrate the numerical examples and new techniques presented. The data set illustrates most of the difficult situations presented in actuarial practice. This book will meet the needs for a reference work as well as for a textbook on loss reserving.

Handbook on Loss Reserving

Handbook on Loss Reserving
Author: Michael Radtke
Publisher: Springer
Total Pages: 317
Release: 2016-10-26
Genre: Business & Economics
ISBN: 3319300563

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.

Stochastic Loss Reserving Using Generalized Linear Models

Stochastic Loss Reserving Using Generalized Linear Models
Author: Greg Taylor
Publisher:
Total Pages: 100
Release: 2016-05-04
Genre:
ISBN: 9780996889704

In this monograph, authors Greg Taylor and GrĂ¡inne 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.

Loss Reserving

Loss Reserving
Author: Gregory Clive Taylor
Publisher:
Total Pages: 60
Release: 2003
Genre: Insurance
ISBN: 9780734028983

Loss Reserving

Loss Reserving
Author: Greg Taylor
Publisher:
Total Pages: 35
Release: 2015
Genre:
ISBN:

The paper reviews the development of loss reserving models over the past, classifying them according to an elementary taxonomy. The taxonomic components include (1) the algebraic structure of the model, (2) the form of its parameter estimation, (3) whether or not it is explicitly stochastic, and (4) whether or not its parameters evolve over time. Particular attention is given to one of the higher species of model, involving complex structure, optimal estimation, and evolutionary parameters. A generalisation of the Kalman filter is considered as a basis of adaptive loss reserving in this case. Real life numerical examples are provided.Some implications of this type of data analysis for loss reserving are discussed, with particular reference to the form of data set used. The use of triangular arrays is questioned, and alternatives examined. Again, real life numerical examples are provided.