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Quantitative Risk Management: Concepts, Techniques, and Tools

TLDR
The most comprehensive treatment of the theoretical concepts and modelling techniques of quantitative risk management can be found in this paper, where the authors describe the latest advances in the field, including market, credit and operational risk modelling.
Abstract
This book provides the most comprehensive treatment of the theoretical concepts and modelling techniques of quantitative risk management. Whether you are a financial risk analyst, actuary, regulator or student of quantitative finance, Quantitative Risk Management gives you the practical tools you need to solve real-world problems. Describing the latest advances in the field, Quantitative Risk Management covers the methods for market, credit and operational risk modelling. It places standard industry approaches on a more formal footing and explores key concepts such as loss distributions, risk measures and risk aggregation and allocation principles. The book's methodology draws on diverse quantitative disciplines, from mathematical finance and statistics to econometrics and actuarial mathematics. A primary theme throughout is the need to satisfactorily address extreme outcomes and the dependence of key risk drivers. Proven in the classroom, the book also covers advanced topics like credit derivatives. Fully revised and expanded to reflect developments in the field since the financial crisis Features shorter chapters to facilitate teaching and learning Provides enhanced coverage of Solvency II and insurance risk management and extended treatment of credit risk, including counterparty credit risk and CDO pricing Includes a new chapter on market risk and new material on risk measures and risk aggregation

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Pair-copula constructions of multiple dependence

TL;DR: This work uses the pair-copula decomposition of a general multivariate distribution and proposes a method for performing inference, which represents the first step towards the development of an unsupervised algorithm that explores the space of possible pair-Copula models, that also can be applied to huge data sets automatically.
Book

Quantitative Risk Management

TL;DR: The book’s methodology draws on diverse quantitative disciplines, from mathematical finance and statistics to econometrics and actuarial mathematics, to satisfactorily address extreme outcomes and the dependence of key risk drivers.

Introduction To Robust Estimation And Hypothesis Testing

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Against the Gods: The Remarkable Story of Risk

TL;DR: The history of Lloyd's of London, the famous insurance firm, is described in this paper, where the authors describe the many intriguing people and tantalizing mysteries so peculiar to the mathematics of chance.
References
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Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach

TL;DR: The second edition of this book is unique in that it focuses on methods for making formal statistical inference from all the models in an a priori set (Multi-Model Inference).
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The Pricing of Options and Corporate Liabilities

TL;DR: In this paper, a theoretical valuation formula for options is derived, based on the assumption that options are correctly priced in the market and it should not be possible to make sure profits by creating portfolios of long and short positions in options and their underlying stocks.
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The Cost of Capital, Corporation Finance and the Theory of Investment

TL;DR: In this article, the effect of financial structure on market valuations has been investigated and a theory of investment of the firm under conditions of uncertainty has been developed for the cost-of-capital problem.
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Generalized Linear Models

Eric R. Ziegel
- 01 Aug 2002 - 
TL;DR: This is the Ž rst book on generalized linear models written by authors not mostly associated with the biological sciences, and it is thoroughly enjoyable to read.
Trending Questions (1)
What are the best easiest quantitative risk management tools?

The book discusses various quantitative risk management tools, emphasizing practicality and real-world problem-solving. It covers market, credit, and operational risk modelling techniques, enhancing understanding and application.