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Simulation Techniques in Financial Risk Management / Ngai Hang Chan and Hoi Ying Wong

By: Contributor(s): Material type: TextTextSeries: Wiley Series in Statistics in PracticePublication details: New Jersey Wiley 2015Edition: 2nd edDescription: xviii, 205 p., 24 cmISBN:
  • 9781118735817 (hbk.)
Subject(s): DDC classification:
  • 338.5 CHA
Summary: This is the introductory chapter of the book, which discusses some of the features of simulations. One can see that simulation is a powerful tool for analyzing complex situations. The book also explains different techniques in simulations and their applications in risk management. Practical implementation of risk management methods usually requires substantial computations. The computational requirement comes from calculating summaries, such as value-at-risk, hedging ratio, market β, and so on. Therefore, many of the simulation techniques developed by statisticians for summarizing data are equally applicable in the risk management context. The book gives some typical examples. In risk management, one often encounters stochastic processes such as Brownian motions, geometric Brownian motion, and lognormal distributions. Although some of these entities may be understood analytically, quantities derived from them are often less tractable.
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Item type Current library Collection Call number Status Date due Barcode
Books Books H.T. Parekh Library GSB Collection 518.245 CHA (Browse shelf(Opens below)) Available B2568

Bookwell/1268/180118/114.95$

This is the introductory chapter of the book, which discusses some of the features of simulations. One can see that simulation is a powerful tool for analyzing complex situations. The book also explains different techniques in simulations and their applications in risk management. Practical implementation of risk management methods usually requires substantial computations. The computational requirement comes from calculating summaries, such as value-at-risk, hedging ratio, market β, and so on. Therefore, many of the simulation techniques developed by statisticians for summarizing data are equally applicable in the risk management context. The book gives some typical examples. In risk management, one often encounters stochastic processes such as Brownian motions, geometric Brownian motion, and lognormal distributions. Although some of these entities may be understood analytically, quantities derived from them are often less tractable.

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