Historically, financial and insurance risks were separate subjects most often analyzed using qualitative methods. The development of quantitative methods based on stochastic analysis is an important achievement of modern financial mathematics, one that can naturally be extended and applied in actuarial mathematics. Risk Analysis in Finance
This book provides professional-level information on how to analyze the financial and business well-being of all types of insurance company, including Lloyd's of London syndicates. The proposed risk-based assessment framework will enable better Credit, Investment, Policy and other decisions, subject to the risk-averse stance of decision-makers.
This new edition of the Handbook of Insurance reviews the last forty years of research developments in insurance and its related fields. A single reference source for professors, researchers, graduate students, regulators, consultants and practitioners, the book starts with the history and foundations of risk and insurance theory, followed by a review of prevention and precaution, asymmetric information, risk management, insurance pricing, new financial innovations, reinsurance, corporate governance, capital allocation, securitization, systemic risk, insurance regulation, the industrial organization of insurance markets and other insurance market applications. It ends with health insurance, longevity risk, long-term care insurance, life insurance financial products and social insurance. This second version of the Handbook contains 15 new chapters. Each of the 37 chapters has been written by leading authorities in risk and insurance research, all contributions have been peer reviewed, and each chapter can be read independently of the others.
This paper explores insurance as a source of financial system vulnerability. It provides a brief overview of the insurance industry and reviews the risks it faces, as well as several recent failures of insurance companies that had systemic implications. Assimilation of banking-type activities by life insurers appears to be the key systemic vulnerability. Building on this experience and the experience gained under the FSAP, the paper proposes key indicators that should be compiled and used for surveillance of financial soundness of insurance companies and the insurance sector as a whole.
The aim of the book is to provide an overview of risk management in life insurance companies. The focus is twofold: (1) to provide a broad view of the different topics needed for risk management and (2) to provide the necessary tools and techniques to concretely apply them in practice. Much emphasis has been put into the presentation of the book so that it presents the theory in a simple but sound manner. The first chapters deal with valuation concepts which are defined and analysed, the emphasis is on understanding the risks in corresponding assets and liabilities such as bonds, shares and also insurance liabilities. In the following chapters risk appetite and key insurance processes and their risks are presented and analysed. This more general treatment is followed by chapters describing asset risks, insurance risks and operational risks - the application of models and reporting of the corresponding risks is central. Next, the risks of insurance companies and of special insurance products are looked at. The aim is to show the intrinsic risks in some particular products and the way they can be analysed. The book finishes with emerging risks and risk management from a regulatory point of view, the standard model of Solvency II and the Swiss Solvency Test are analysed and explained. The book has several mathematical appendices which deal with the basic mathematical tools, e.g. probability theory, stochastic processes, Markov chains and a stochastic life insurance model based on Markov chains. Moreover, the appendices look at the mathematical formulation of abstract valuation concepts such as replicating portfolios, state space deflators, arbitrage free pricing and the valuation of unit linked products with guarantees. The various concepts in the book are supported by tables and figures.
This book presents statistics and data science methods for risk analytics in quantitative finance and insurance. Part I covers the background, financial models, and data analytical methods for market risk, credit risk, and operational risk in financial instruments, as well as models of risk premium and insolvency in insurance contracts. Part II provides an overview of machine learning (including supervised, unsupervised, and reinforcement learning), Monte Carlo simulation, and sequential analysis techniques for risk analytics. In Part III, the book offers a non-technical introduction to four key areas in financial technology: artificial intelligence, blockchain, cloud computing, and big data analytics. Key Features: Provides a comprehensive and in-depth overview of data science methods for financial and insurance risks. Unravels bandits, Markov decision processes, reinforcement learning, and their interconnections. Promotes sequential surveillance and predictive analytics for abrupt changes in risk factors. Introduces the ABCDs of FinTech: Artificial intelligence, blockchain, cloud computing, and big data analytics. Includes supplements and exercises to facilitate deeper comprehension.
Over the last decade, stress testing has become a central aspect of the Fund’s bilateral and multilateral surveillance work. Recently, more emphasis has also been placed on the role of insurance for financial stability analysis. This paper reviews the current state of system-wide solvency stress tests for insurance based on a comparative review of national practices and the experiences from Fund’s FSAP program with the aim of providing practical guidelines for the coherent and consistent implementation of such exercises. The paper also offers recommendations on improving the current insurance stress testing approaches and presentation of results.
This book presents the main valuation approaches that can be used to value financial institutions. By sketching 1) the different business models of banks (both commercial and investment banks) and insurance companies (life, property and casualty and reinsurance); 2) the structure and peculiarities of financial institutions’ reporting and financial statements; and 3) the main features of regulatory capital frameworks for banking and insurance (ie Basel III, Solvency II), the book addresses why such elements make the valuation of financial institutions different from the valuation of non-financial companies. The book then features the valuation models that can be used to determine the value of banks and insurance companies including the Discounted Cash Flow, Dividend Discount Model, and Residual Income Model (with the appropriate estimation techniques for the cost of capital and cash flow in financial industries). The main techniques to perform the relative valuation of financial institutions are then presented: along the traditional multiples (P/E, P/BV, P/TBV, P/NAV), the multiples based on industry-specific value drivers are discussed (for example, P/Pre Provision Profit, P/Deposits, P/Premiums, P/Number of branches). Further valuation tools such as the “Value Maps” or the “Warranted Equity Method” will be explained and discussed. The closing section of the book will briefly focus on the valuation of specific financial companies/vehicles such as closed-end funds, private equity funds, leasing companies, etc.
