Operational Risk or Computational Methods for the Study of Dynamic Economies

Operational Risk: Modeling Analytics

Author: Harry H Panjer

Discover how to optimize business strategies from both qualitative and quantitative points of view

Operational Risk: Modeling Analytics is organized around the principle that the analysis of operational risk consists, in part, of the collection of data and the building of mathematical models to describe risk. This book is designed to provide risk analysts with a framework of the mathematical models and methods used in the measurement and modeling of operational risk in both the banking and insurance sectors.

Beginning with a foundation for operational risk modeling and a focus on the modeling process, the book flows logically to discussion of probabilistic tools for operational risk modeling and statistical methods for calibrating models of operational risk. Exercises are included in chapters involving numerical computations for students' practice and reinforcement of concepts.

Written by Harry Panjer, one of the foremost authorities in the world on risk modeling and its effects in business management, this is the first comprehensive book dedicated to the quantitative assessment of operational risk using the tools of probability, statistics, and actuarial science.

In addition to providing great detail of the many probabilistic and statistical methods used in operational risk, this book features:
* Ample exercises to further elucidate the concepts in the text
* Definitive coverage of distribution functions and related concepts
* Models for the size of losses
* Models for frequency of loss
* Aggregate loss modeling
* Extreme value modeling
* Dependency modeling using copulas
* Statistical methods in model selection andcalibration

Assuming no previous expertise in either operational risk terminology or in mathematical statistics, the text is designed for beginning graduate-level courses on risk and operational management or enterprise risk management. This book is also useful as a reference for practitioners in both enterprise risk management and risk and operational management.

Book about: Global Capital Markets or Greening the Industrial Facility

Computational Methods for the Study of Dynamic Economies

Author: Ramon Marimon

Economists are increasingly using computer simulations to understand the implications of their theoretical models and to make policy recommendations. This volume brings together leaders in the field who explain how to implement the computational techniques needed to solve dynamic economics models.

Table of Contents:

	Contributors
1	Introduction: From pipeline economics to computational economics	1
Pt. I	Almost linear methods
2	Linear quadratic approximations: An introduction	13
3	A toolkit for analysing nonlinear dynamic stochastic models easily	30
4	Solving nonlinear rational expectations models by eigenvalue - eigenvector decompositions	62
Pt. II	Nonlinear methods
5	Discrete state-space methods for the study of dynamic economies	95
6	Application of weighted residual methods to dynamic economic models	114
7	The parameterized expectations approach: Some practical issues	143
8	Finite-difference methods for continuous-time dynamic programming	172
Pt. III	Solving some dynamic economies
9	Optimal fiscal policy in a linear stochastic economy	197
10	Computing models of social security	221
11	Computation of equilibria in heterogeneous-agent models	238
	References	265
	Subject index	275
	Author index	279