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Elementary Calculus of Financial Mathematics [Pehme köide]

  • Formaat: Paperback, 140 pages, kõrgus x laius x paksus: 229x152x8 mm, kaal: 260 g, ill
  • Sari: Mathematical Modeling and Computation
  • Ilmumisaeg: 30-Dec-2008
  • Kirjastus: Society for Industrial & Applied Mathematics,U.S.
  • ISBN-10: 0898716675
  • ISBN-13: 9780898716672
Teised raamatud teemal:
  • Formaat: Paperback, 140 pages, kõrgus x laius x paksus: 229x152x8 mm, kaal: 260 g, ill
  • Sari: Mathematical Modeling and Computation
  • Ilmumisaeg: 30-Dec-2008
  • Kirjastus: Society for Industrial & Applied Mathematics,U.S.
  • ISBN-10: 0898716675
  • ISBN-13: 9780898716672
Teised raamatud teemal:
Modern financial mathematics relies on the theory of random processes in time, reflecting the erratic fluctuations in financial markets.This book introduces the fascinating area of financial mathematics and its calculus in an accessible manner geared toward undergraduate students. Using little high-level mathematics, the author presents the basic methods for evaluating financial options and building financial simulations. By emphasizing relevant applications and illustrating concepts with colour graphics, Elementary Calculus of Financial Mathematics presents the crucial concepts needed to understand financial options among these fluctuations. Among the topics covered are the binomial lattice model for evaluating financial options, the Black-Scholes and Fokker-Planck equations, and the interpretation of Ito's formula in financial applications. Each chapter includes exercises for student practice and the appendices offer MATLAB(R) and SCILAB code as well as alternate proofs of the Fokker-Planck equation and Kolmogorov backward equation.
Preface ix
List of Algorithms
xi
Financial Indices Appear to Be Stochastic Processes
1(38)
Brownian motion is also called a Wiener process
3(6)
Stochastic drift and volatility are unique
9(5)
Basic numerics simulate an SDE
14(6)
A binomial lattice prices call option
20(12)
Summary
32(7)
Exercises
33(6)
Ito's Stochastic Calculus Introduced
39(22)
Multiplicative noise reduces exponential growth
39(4)
Ito's formula solves some SDEs
43(5)
The Black-Scholes equation prices options accurately
48(8)
Summary
56(5)
Exercises
57(4)
The Fokker-Planck Equation Describes the Probability Distribution
61(32)
The probability distribution evolves forward in time
65(11)
Stochastically solve deterministic differential equations
76(8)
The Kolmogorov backward equation completes the picture
84(2)
Summary
86(7)
Exercises
87(6)
Stochastic Integration Proves Ito's Formula
93(22)
The Ito integral nbaf dW
95(11)
The Ito formula
106(6)
Summary
112(3)
Exercises
113(2)
Appendix A Extra MATLAB/SCILAB Code
115(4)
Appendix B Two Alternate Proofs
119(6)
B.1 Fokker-Planck equation
119(2)
B.2 Kolmogorov backward equation
121(4)
Bibliography 125(2)
Index 127
A. J. Roberts is a Professor and Chair in the School of Mathematical Sciences at the University of Adelaide. He has lectured and conducted research at the University of New South Wales and the University of Southern Queensland, and has published over 100 refereed international journal articles. As a leader in developing and applying a branch of modern dynamical systems theory, in conjunction with new computer algebra algorithms in scientific computing, Professor Roberts derives and interprets mathematical and computational models of complex multiscale systems, both deterministic and stochastic.