Simulation and Monte Carlo: With Applications in Finance and MCMC

Simulation and Monte Carlo: With Applications in Finance and MCMC

Volumn , Issue , 2007, Pages 1-333

  Dagpunar, J S ^a  

^a UNIVERSITY OF EDINBURGH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

DISCRETE EVENT SIMULATION; FINANCE; MARKOV PROCESSES; OPERATIONS RESEARCH;

EDINBURGH UNIVERSITY; FINANCE INDUSTRIES; FINANCIAL MATHEMATICS; MARKOV CHAIN MONTE-CARLO; OPERATIONAL RESEARCH; SIMULATION PROCEDURES; THEORY AND PRACTICE; VARIANCE REDUCTION TECHNIQUES;

MONTE CARLO METHODS;

EID: 67650695100     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9780470061336     Document Type: Book

References (89)

1. Ahrens, J.H. and Dieter, U. (1980) Sampling from binomial and Poisson distributions: a method with bounded computation times. Computing, 25: 193-208.
2. Allen, L.J.S. (2003) An Introduction to Stochastic Processes with Applications to Biology. London: Pearson.
3. Anderson, S.L. (1990) Random number generators on vector supercomputers and other advanced architectures. SIAM Review, 32: 221-251.
4. Atkinson, A.C. (1979) The computer generation of Poisson random variables. Applied Statistics, 28: 29-35.
5. Atkinson, A.C. (1982) The simulation of generalised inverse Gaussian and hyperbolic random variables. SIAM Journal of Scientific and Statistical Computing, 3: 502-517.
6. Banks, J., Carson, J.S., Nelson, B.L. and Nicol, D.M. (2005) Discrete Event System Simulation, 4th edn. Upper Saddle River, New Jersey: Prentice Hall.
7. Barndorff-Nielson, O. (1977) Exponentially decreasing distributions for the logarithm of particle size. Proceedings of the Royal Society, A353: 401-419.
8. Beasley, J.D. and Springer, S.G. (1977) The percentage points of the normal distribution. Applied Statistics, 26: 118-121.
9. Besag, J. (1974) Spatial interaction and the statistical analysis of lattice systems (with discussion). Journal of the Royal Statistical Society, Series B, 36: 192-326.
10. Black, F. and Scholes, M. (1973) The pricing of options and corporate liabilities. Journal of Political Economy, 81: 637-654.
11. Borosh, I. and Niederreiter, H. (1983) Optimal multipliers for pseudo-random number generation by the linear congruential method. BIT, 23: 65-74.
12. Butcher, J.C. (1961) Random sampling from the normal distribution. Computer Journal, 3: 251-253.
13. Castledine, B.J. (1981) A Bayesian analysis of multiple-recapture sampling for a closed population. Biometrika, 67: 197-210.
14. Cheng, R.C.H. (1977) The generation of gamma variables with non-integral shape parameter. Applied Statistics, 26: 71-76.
15. Cheng, R.C.H. (1978) Generating beta variates with nonintegral shape parameters. Communications of the Association of Computing Machinery, 21: 317-322.
16. Cochran, W.G. (1977) Sampling Techniques. Chichester: John Wiley & Sons, Ltd.
17. Cox, D.R. and Miller, H.D. (1965) The Theory of Stochastic Processes. London: Methuen.
18. Dagpunar, J.S. (1978) Sampling of variates from a truncated gamma distribution. Journal of Statistical Computation and Simulation, 8: 59-64.
19. Dagpunar, J.S. (1988a) Principles of Random Variate Generation. Oxford: Oxford University Press.
20. Dagpunar, J.S. (1988b). Computer generation of random variates from the tail of t and normal distributions. Communications in Statistics, Simulation and Computing, 17: 653-661.
21. Dagpunar, J.S. (1989a) A compact and portable Poisson random variate generator. Journal of Applied Statistics, 16: 391-393.
22. Dagpunar, J.S. (1989b) An easily implemented generalised inverse Gaussian generator. Communications in Statistics, Simulation and Computing, 18: 703-710.
23. Devroye, L. (1986). Non-uniform Random Variate Generation. Berlin: Springer-Verlag.
24. Devroye, L. (1996). Random variate generation in one line of code. In J.M. Charnes, D.J. Morrice, D.T. Brunner and J.J Swain (eds), Proceedings of the 1996 Winter Simulation Conference, pp. 265-272. ACM Press
25. Eberlein, E. (2001) Application of generalized hyperbolic Lévy motions to finance. In O.E. Barndorff-Nielson, T. Mikosch and S. Resnick (eds), Lévy Processes: Theory and Applications, pp. 319-337. Birkhauser.
26. Entacher, K. (2000) A collection of classical pseudorandom number generators with linear structures - advanced version. In http://crypto.mat.sbg.ac.at/results/karl/server/.
27. Fishman, G.S. (1978) Principles of Discrete Event Simulation. Chichester: John Wiley & Sons, Ltd.
28. Fishman, G.S. (1979) Sampling from the binomial distribution on a computer. Journal of the American Statistical Association, 74: 418-423.
29. Fishman, G.S. and Moore, L.R. (1986) An exhaustive analysis of multiplicative congruential random number generators with modulus 231-1. SIAM Journal on Scientific and Statistical Computing, 7: 24-45.
30. Fouque, J.-P. and Tullie, T.A. (2002) Variance reduction for Monte Carlo simulation in a stochastic volatility environment. Quantitative Finance, 2: 24-30.
31. Gamerman, D. (1997) Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference. London: Chapman and Hall.
32. Gaver, D.P. and O'Muircheartaigh, I.G. (1987) Robust empirical Bayes analyses of event rates. Technometrics, 29: 1-15.
33. Gelfand, A.E. and Smith, A.F.M. (1990) Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association, 85: 398-409.
34. Gentle, J.E. (2003) Random Number Generation and Monte Carlo Methods, 2nd edn. Berlin: Springer.
35. George, E.I. and Robert C.P. (1992) Capture-recapture estimation via Gibbs sampling. Biometrika, 79: 677-683.
36. Gilks, W.R. and Wild, P. (1992) Adaptive rejection sampling for Gibbs sampling. Applied Statistics, 41: 337-348.
37. Gilks, W.R., Best, N.G. and Tan, K.K.C. (1995) Adaptive rejection Metropolis sampling within Gibbs sampling. Applied Statistics, 44: 455-472.
38. Gilks, W.R., Richardson, S., Spiegelhalter, D.J. (1996) Markov Chain Monte Carlo in Practice. London: Chapman and Hall.
39. Glasserman, P. (2004) Monte Carlo methods in Financial Engineering. Berlin: Springer.
40. Glasserman, P., Heidelberger, P., Shahabuddin, P. (1999) Asymptotically optimal importance sampling and stratification for pricing path-dependent options. Mathematical Finance, 2: 117-152.
41. Hammersley, J.M. and Handscombe, D.C. (1964) Monte Carlo Methods. London: Methuen.
42. Hastings, W.K. (1970) Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57: 97-109.
43. Hobson, D.G. (1998) Stochastic volatility. In D.J. Hand and S.D. Jacka (eds), Statistics and Finance, Applications of Statistics Series, pp. 283-306. London: Arnold.
44. Hull, J.C. (2006) Options, Futures and Other Derivatives, 6th edn. Upper Saddle River, New Jersey: Prentice Hall.
45. Hull, T.E. and Dobell, A.R. (1962) Random number generators. SIAM Review, 4: 230-254.
46. Jöhnk, M.D. (1964) Erzeugung von Betaverteilten und Gammaverteilten Zufallszahlen. Metrika, 8: 5-15.
47. Johnson, N.L., Kotz, S. and Balakrishnan, N. (1995) Continuous Univariate Distributions, Volume 2, 2nd edn. Chichester: John Wiley & Sons, Ltd.
48. Karian, Z.A. and Goyal, R. (1994) Random Number Generation and Testing, Maple Technical Newsletter, Volume 1, Number 1, 32-37, Spring 1994. Birkhauser.
49. Kemp, A.W. (1981) Efficient generation of logarithmically distributed pseudo-random variables. Applied Statistics, 30: 249-253.
50. Kinderman, A.J. and Monahan, J.F. (1977) Computer generation of random variables using the ratio of uniform deviates. ACM Transactions on Mathematical Software, 3: 257-260.
51. Knuth, D.E. (1998) The art of computer programming, Volume 2, Seminumerical Algorithms, 3rd edn. Reading, Massachusetts: Addison-Wesley.
52. Lavenberg, S.S., Moeller, T.L. and Sauer, C.H. (1979) Concomitant control variables applied to the regenerative simulation of queueing systems. Operations Research, 27: 134-160.
53. Law, A.M. and Kelton, W.D. (2000) Simulation Modeling and Analysis, 3rd edn. London: McGraw-Hill.
54. L'Ecuyer, P. (1999) Tables of linear congruential generators of different sizes and good lattice structure. Mathematics of Computation, 68: 249-260.
55. Lewis, J.G. and Payne, W.H. (1973) Generalised feedback shift register pseudo-random number algorithm. Journal of the Association of Computing Machinery, 20: 456-458.
56. Lewis, P.A.W. and Shedler, G.S. (1976) Simulation of non-homogeneous Poisson Processes with log linear rate function. Biometrika, 63: 501-505.
57. Lewis, P.A.W. and Shedler, G.S. (1979a) Simulation of non-homogeneous Poisson Processes by thinning. Naval Research Logistics Quarterly, 26: 403-413.
58. Lewis, P.A.W. and Shedler, G.S. (1979b) Simulation of non-homogeneous processes with degree 2 exponential polynomial rate function. Operations Research, 27: 1026-1040.
59. Marsaglia, G. (1977) The squeeze method for generating gamma variates. Computers and Mathematics with Applications, 3: 321-325.
60. Marsaglia, G. (1972) The structure of linear congruential sequences. In S.K. Zaremba (ed.), Applications of Number Theory to Numerical Analysis, pp. 248-285. New York: Academic Press.
61. Marsaglia, G. and Zaman, A. (1993) Monkey tests for random number generators. Computers and Mathematics with Applications, 26: 1-10.
62. Marsaglia, G., Anathanarayanan, K. and Paul, N. (1972) Random Number Generator Package - 'Super Duper'. Uniform, normal, and random number generator. School of Computer Science, McGill University.
63. Merton, R.C. (1973) The theory of rational option pricing. Bell Journal of Economics and Management Science, 4: 141-183.
64. Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H. and Teller, E. (1953) Equation of state calculations by fast computing machine. Journal of Chemical Physics, 21: 1087-1091.
65. Morgan, B.J.T. (2000) Applied stochastic modelling. London: Arnold.
66. Moro, B. (1995) The full Monte. Risk, 8 (February): 57-58.
67. Neave, H.R. (1973) On using the Box-Müller transformation with multiplicative congruential pseudo-random number generators. Applied Statistics, 22: 92-97.
68. Percy, D.F. (2002) Bayesian enhanced strategic decision making for reliability. European Journal of Operational Research, 139: 133-145.
69. Pidd, M. (1998) Computer Simulation in Management Science, 4th edn. Chichester: John Wiley & Sons, Ltd.
70. Rand Corporation (1955) A Million Random Digits with 100 000 Normal Deviates. Glencoe: Free Press.
71. Ripley, B.D. (1983a) The lattice structure of pseudo-random generators. Proceedings of the Royal Society, A389: 197-204.
72. Ripley, B.D. (1983b) Computer generation of random variables: a tutorial. International Statistical Review, 51: 301-319.
73. Robert, C.P. (2001) The Bayesian Choice: From decision theoretic foundations to computational implementation, 2nd edn. New York and London: Springer-Verlag.
74. Robert, C.P. and Casella, G. (2004) Monte Carlo Statistical Methods, 2nd edn. New York: Springer-Verlag.
75. Ross, S.M. (2002) Simulation, 3rd edn. New York: Academic Press.
76. Rubinstein, R.Y. (1981) Simulation and the Monte Carlo Method. Chichester: John Wiley & Sons, Ltd.
77. Schrage, L. (1979) A more portable FORTRAN random number generator. ACM Transactions on Mathematical Software, 5: 132-138.
78. Shedler, G.S. (1993) Regenerative Stochastic Simulation. New York: Academic Press.
79. Tauseworthe, R.C. (1965) Random numbers generated by linear recurrence modulo two. Mathematics of Computing, 19: 201-209.
80. Tierney, L. (1994) Markov chains for exploring posterior distributions (with discussion). Annals of Statistics, 22: 1701-1728.
81. Tijms, H.C. (2003) A first Course in Stochastic Models. Chichester: John Wiley & Sons, Ltd.
82. Tin, M. (1965) Comparison of some ratio estimators. Journal of the American Statistical Association, 60: 294-307.
83. Tocher, K.D. (1963) The Art of Simulation. London: Hodder and Stoughton.
84. Toothill, J.P.R., Robinson, W.D. and Adams, A.G. (1971) The runs up-and-down performance of Tauseworthe pseudo-random number generators. Journal of Association of Computing Machinery, 18: 381-399.
85. Toothill, J.P.R., Robinson, W.D. and Eagle, D.J. (1973) An asymptotically random Tauseworthe sequence. Journal of Association of Computing Machinery, 20: 469-481.
86. Ulrich, G. (1984) Computer generation of distributions on the m-sphere. Applied Statistics, 33: 158-163.
87. Wichmann, B.A. and Hill, I.D. (1982) Algorithm AS 183. An efficient and portable pseudorandom number generator. Applied Statistics, 31: 188-190.
88. Wichmann, B.A. and Hill, I.D. (1984) Correction to algorithm AS 183. Applied Statistics, 33: 123.
89. Wilmott, P. (1998) Derivatives. The Theory and Practice of Financial Engineering. Chichester: John Wiley & Sons, Ltd.