Melnikov | Risk Analysis in Finance and Insurance, Second Edition | E-Book | www2.sack.de
E-Book

E-Book, Englisch, 328 Seiten

Reihe: Chapman & Hall/CRC Financial Mathematics Series

Melnikov Risk Analysis in Finance and Insurance, Second Edition


2. Auflage 2012
ISBN: 978-1-4200-7053-8
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 328 Seiten

Reihe: Chapman & Hall/CRC Financial Mathematics Series

ISBN: 978-1-4200-7053-8
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)



Risk Analysis in Finance and Insurance, Second Edition presents an accessible yet comprehensive introduction to the main concepts and methods that transform risk management into a quantitative science. Taking into account the interdisciplinary nature of risk analysis, the author discusses many important ideas from mathematics, finance, and actuarial science in a simplified manner. He explores the interconnections among these disciplines and encourages readers toward further study of the subject. This edition continues to study risks associated with financial and insurance contracts, using an approach that estimates the value of future payments based on current financial, insurance, and other information.
New to the Second Edition

- Expanded section on the foundations of probability and stochastic analysis

- Coverage of new topics, including financial markets with stochastic volatility, risk measures, risk-adjusted performance measures, and equity-linked insurance

- More worked examples and problems

Reorganized and expanded, this updated book illustrates how to use quantitative methods of stochastic analysis in modern financial mathematics. These methods can be naturally extended and applied in actuarial science, thus leading to unified methods of risk analysis and management.

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Zielgruppe


Financial analysts and researchers; students in mathematical finance, actuarial science, and risk management.


Autoren/Hrsg.


Weitere Infos & Material


Financial Risk Management and Related Mathematical Tools

Introductory concepts of the securities market

Probabilistic foundations of financial modelling and pricing of contingent claims

Elements of probability theory and stochastic analysis

Financial Risk Management in the Binomial Model

The binomial model of a financial market. Absence of arbitrage, uniqueness of a risk-neutral probability measure, martingale representation

Hedging contingent claims in the binomial market model. The Cox-Ross-Rubinstein formula

Pricing and hedging American options

Utility functions and St. Petersburg’s paradox. The problem of optimal investment
The term structure of prices, hedging and investment strategies in the Ho-Lee model

The transition from the binomial model of a financial market to a continuous model. The Black-Scholes formula and equation

Advanced Analysis of Financial Risks: Discrete Time Models

Fundamental theorems on arbitrage and completeness. Pricing and hedging contingent claims in complete and incomplete markets
The structure of options prices in incomplete markets and in markets with constraints
Hedging contingent claims in mean square

Gaussian model of a financial market in discrete time. Insurance appreciation and discrete version of the Black-Scholes formula

Analysis of Risks: Continuous Time Models

The Black-Scholes model. "Greek" parameters in risk management, hedging and optimal investment
Beyond the Black-Scholes model

Imperfect hedging and risk measures

Fixed Income Securities: Modeling and Pricing
Elements of deterministic theory of fixed income instruments

Stochastic modelling and pricing bonds and their derivatives

Implementations of Risk Analysis in Various Areas of Financial Industry

Real options: pricing long-term investment projects

Technical analysis in risk management

Performance measures and their applications

Insurance and Reinsurance Risks

Modelling risk in insurance and methodologies of premium calculations
Risks transfers via reinsurance

Elements of traditional life insurance

Risk modelling and pricing in innovative life insurance

Solvency Problem for an Insurance Company

Ruin probability as a measure of solvency of an insurance company
Solvency of an insurance company and investment portfolios

Solvency problem in a generalized Cramér-Lundberg model
Appendix A: Problems
Appendix B: Bibliographic Remarks

Bibliography

Glossary of Notation

Index


Alexander Melnikov is a professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. Dr. Melnikov’s research interests include mathematical finance and risk management, insurance and actuarial science, statistics and stochastic analysis, and stochastic differential equations and their applications.



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