Monte Carlo Methods in Financial Engineering

Framsida
Springer Science & Business Media, 2004 - 596 sidor
5 Recensioner

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."

 

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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
CXXIV
569
CXXV
587
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