Monte Carlo Methods in Financial EngineeringSpringer Science & Business Media, 2004 - 596 sidor Monte Carlo simulation has become an essential tool in the pricing of derivative securities and in risk management. These applications have, in turn, stimulated research into new Monte Carlo methods and renewed interest in some older techniques. This book develops the use of Monte Carlo methods in finance and it also uses simulation as a vehicle for presenting models and ideas from financial engineering. It divides roughly into three parts. The first part develops the fundamentals of Monte Carlo methods, the foundations of derivatives pricing, and the implementation of several of the most important models used in financial engineering. The next part describes techniques for improving simulation accuracy and efficiency. The final third of the book addresses special topics: estimating price sensitivities, valuing American options, and measuring market risk and credit risk in financial portfolios. The most important prerequisite is familiarity with the mathematical tools used to specify and analyze continuous-time models in finance, in particular the key ideas of stochastic calculus. Prior exposure to the basic principles of option pricing is useful but not essential. The book is aimed at graduate students in financial engineering, researchers in Monte Carlo simulation, and practitioners implementing models in industry. Mathematical Reviews, 2004: "... this book is very comprehensive, up-to-date and useful tool for those who are interested in implementing Monte Carlo methods in a financial context." |
Innehåll
112 First Examples | 1 |
I | 3 |
II | 9 |
121 Pricing and Replication | 19 |
III | 21 |
IV | 25 |
V | 32 |
VI | 36 |
53 Lattice Rules | 314 |
LXIII | 316 |
LXIV | 320 |
LXV | 323 |
LXVII | 331 |
LXVIII | 335 |
LXIX | 339 |
LXX | 344 |
VII | 39 |
IX | 43 |
X | 44 |
215 Combined Generators and Other Methods | 47 |
XI | 49 |
XII | 53 |
XIII | 54 |
XIV | 58 |
232 Generating Univariate Normals | 63 |
XV | 65 |
XVI | 71 |
XVII | 79 |
XIX | 90 |
XX | 93 |
XXI | 96 |
XXII | 104 |
XXIII | 108 |
XXV | 111 |
34 SquareRoot Diffusions | 118 |
XXVI | 120 |
XXVII | 121 |
XXVIII | 125 |
XXIX | 128 |
XXX | 131 |
XXXI | 134 |
XXXIII | 142 |
XXXIV | 149 |
XXXV | 150 |
XXXVI | 155 |
XXXVII | 160 |
XXXVIII | 165 |
XXXIX | 166 |
373 Simulation | 172 |
XL | 174 |
XLI | 180 |
XLII | 185 |
XLIV | 196 |
414 Nonlinear Controls | 200 |
XLV | 202 |
XLVI | 205 |
XLVII | 209 |
XLVIII | 220 |
XLIX | 232 |
L | 236 |
LI | 243 |
LII | 244 |
LIII | 251 |
LIV | 255 |
LV | 267 |
LVI | 276 |
511 Discrepancy | 281 |
512 Van der Corput Sequences | 283 |
513 The KoksmaHlawka Bound | 285 |
LVII | 287 |
LVIII | 290 |
LIX | 293 |
LXI | 297 |
LXII | 303 |
LXXI | 348 |
LXXIII | 351 |
LXXIV | 357 |
63 Extensions | 360 |
LXXV | 362 |
633 Convergence of Mean Square Error | 363 |
LXXVI | 365 |
LXXVII | 366 |
LXXVIII | 371 |
Estimating Sensitivities | 375 |
LXXX | 377 |
LXXXI | 378 |
LXXXIII | 381 |
LXXXIV | 386 |
LXXXVI | 393 |
LXXXVII | 396 |
LXXXVIII | 401 |
XC | 407 |
734 Approximations and Related Methods | 411 |
XCI | 413 |
XCII | 418 |
XCIII | 421 |
XCIV | 426 |
831 High Estimator | 430 |
832 Low Estimator | 432 |
XCV | 434 |
XCVI | 437 |
85 Stochastic Mesh Methods | 441 |
XCVII | 443 |
XCVIII | 450 |
XCIX | 459 |
CI | 465 |
CII | 470 |
CIII | 478 |
CIV | 481 |
CV | 484 |
CVI | 492 |
922 Importance Sampling | 493 |
CVII | 495 |
CVIII | 500 |
CIX | 506 |
933 Variance Reduction | 512 |
CX | 514 |
CXI | 520 |
CXIII | 525 |
CXIV | 529 |
CXV | 535 |
A2 Central Limit Theorem and Confidence Intervals | 539 |
CXVI | 541 |
CXVII | 545 |
B3 Martingales | 548 |
CXIX | 550 |
CXX | 553 |
CXXI | 559 |
CXXIII | 564 |
569 | |
587 | |
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Handbooks in Operations Research and Management Science: Simulation Shane G. Henderson,Barry L. Nelson Begränsad förhandsgranskning - 2006 |