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Hubbert S. Essential Mathematics for Market Risk Management

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Hubbert S. Essential Mathematics for Market Risk Management
Wiley, 2012. — 350 p. — ISBN: 1119979528, 9781119979524.
Everything you need to know in order to manage risk effectively within your organization.
You cannot afford to ignore the explosion in mathematical finance in your quest to remain competitive. This exciting branch of mathematics has very direct practical implications: when a new model is tested and implemented it can have an immediate impact on the financial environment.
With risk management top of the agenda for many organizations, this book is essential reading for getting to grips with the mathematical story behind the subject of financial risk management. It will take you on a journey—from the early ideas of risk quantification up to today's sophisticated models and approaches to business risk management.
To help you investigate the most up-to-date, pioneering developments in modern risk management, the book presents statistical theories and shows you how to put statistical tools into action to investigate areas such as the design of mathematical models for financial volatility or calculating the value at risk for an investment portfolio.
Respected academic author Simon Hubbert is the youngest director of a financial engineering program in the U.K. He brings his industry experience to his practical approach to risk analysis.
Captures the essential mathematical tools needed to explore many common risk management problems.
Website with model simulations and source code enables you to put models of risk management into practice.
Plunges into the world of high-risk finance and examines the crucial relationship between the risk and the potential reward of holding a portfolio of risky financial assets.
This book is your one-stop-shop for effective risk management.
Applied Linear Algebra for Risk Managers.
Probability Theory for Risk Managers.
Optimization Tools.
Portfolio Theory I.
Portfolio Theory II.
The Capital Asset Pricing Model (CAPM).
Risk Factor Modelling.
The Value at Risk Concept.
Value at Risk under a Normal Distribution.
Advanced Probability Theory for Risk Managers.
A Survey of Useful Distribution Functions.
A Crash Course on Financial Derivatives.
Non-linear Value at Risk.
Time Series Analysis.
Maximum Likelihood Estimation.
The Delta Method for Statistical Estimates.
Hypothesis Testing.
Statistical Properties of Financial Losses.
Modelling Volatility.
Extreme Value Theory.
Simulation Models.
Alternative Approaches to VaR.
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