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Probability for finance / Ekkehard Kopp, University of Hull, Hull, UK, Jan Malczak, AGH University of Science and Technology, Kraków, Poland, Tomasz Zastawniak, University of York, York, UK.

By: Contributor(s): Material type: TextSeries: Mastering mathematical financePublisher: Cambridge : Cambridge University Press, 2014Description: 1 online resource (viii, 188 pages) : digital, PDF file(s)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781139035026 (ebook)
Subject(s): Additional physical formats: Print version: : No titleDDC classification:
  • 332.01519 23
LOC classification:
  • HF5691 .K7125 2014
Online resources: Summary: Students and instructors alike will benefit from this rigorous, unfussy text, which keeps a clear focus on the basic probabilistic concepts required for an understanding of financial market models, including independence and conditioning. Assuming only some calculus and linear algebra, the text develops key results of measure and integration, which are applied to probability spaces and random variables, culminating in central limit theory. Consequently it provides essential prerequisites to graduate-level study of modern finance and, more generally, to the study of stochastic processes. Results are proved carefully and the key concepts are motivated by concrete examples drawn from financial market models. Students can test their understanding through the large number of exercises and worked examples that are integral to the text.
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Item type Current library Collection Status Barcode
eBooks Central Library Statistics & Probability Available EB0866

Title from publisher's bibliographic system (viewed on 05 Oct 2015).

Students and instructors alike will benefit from this rigorous, unfussy text, which keeps a clear focus on the basic probabilistic concepts required for an understanding of financial market models, including independence and conditioning. Assuming only some calculus and linear algebra, the text develops key results of measure and integration, which are applied to probability spaces and random variables, culminating in central limit theory. Consequently it provides essential prerequisites to graduate-level study of modern finance and, more generally, to the study of stochastic processes. Results are proved carefully and the key concepts are motivated by concrete examples drawn from financial market models. Students can test their understanding through the large number of exercises and worked examples that are integral to the text.

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