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.
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.
€41.99
O autorze
About the authorDR SIMON HUBBERT is a lecturer in Mathematics and Mathematical Finance at Birkbeck College, University of London, where he is currently the programme director for the graduate diploma in Financial Engineering. He has taught masters level courses on Risk Management and Financial Mathematics for many years and also has valuable experience in the financial industry having engaged in consultation work with IBM global business services and as a risk analyst for the debt management office, a branch of HM Treasury.
Język Angielski ● Format EPUB ● Strony 416 ● ISBN 9781119953029 ● Rozmiar pliku 10.3 MB ● Wydawca John Wiley & Sons ● Opublikowany 2011 ● Ydanie 1 ● Do pobrania 24 miesięcy ● Waluta EUR ● ID 2357641 ● Ochrona przed kopiowaniem Adobe DRM
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