Matematik simulation and monte carlo with applications in finance and mcmc phần 5 ppsx
Matematik simulation and monte carlo with applications in finance and mcmc phần 1 pps
... 51 3.747 454 ], [21, 52 3.217922], [22, 53 1.9 755 60], [23, 53 6.741344], [24,
54 2.647 658 ], [ 25, 54 8.69 653 8], [26, 54 5.193483], [27, 53 5.41 751 6], [28, 52 5.926680],
[29, 51 3.869 654 ], [30, 499.246436]
Now these ... xi
Glossary xiii
1 Introduction to simulation and Monte Carlo 1
1.1 Evaluating a definite integral 2
1.2 Monte Carlo is integral estimation 4
1....
Matematik simulation and monte carlo with applications in finance and mcmc phần 2 pdf
... not
communicate with the other sequence comprising the remaining m/4 odd numbers. For
example, X
i
=
3X
i−1
mod 2
4
gives either
1
15
3
15
9
15
11
15
1
15
or
5
15
15
15
13
15
7
15
5
15
depending ... on inversion for generating variates from a
distribution with density
f
x
= e
−x
on support
0
.
Simulation and...
Matematik simulation and monte carlo with applications in finance and mcmc phần 3 ppsx
... independent standard normal random variables, X
1
and X
2
. The
joint density is
f
X
1
X
2
x
1
x
2
=
1
2
e
−
x
2
1
+x
2
2
/2
Simulation and Monte Carlo: With applications in finance and MCMC ... ×3 455 5
06 25
= 5 79
where t
1
is the time to generate 2000 variates using naive Monte Carlo and t
2
the time
using the combined method. This means that usin...
Matematik simulation and monte carlo with applications in finance and mcmc phần 4 pptx
... 95 05
Stratified sampling 95
Using procedure ‘weibullstrat’ in Appendix 5. 3.2 with N = 100 and K = 200 (and with
the same seed as in the naive simulation) , the results were
PS
= 216644
and
ese
PS
= ... Wiener process, will be described.
Simulation and Monte Carlo: With applications in finance and MCMC J. S. Dagpunar
© 2007 John Wiley &...
Matematik simulation and monte carlo with applications in finance and mcmc phần 5 ppsx
... sortd:
print(arrival_times);
end do:
lambda = 2
T = 3 5
m = 70
[0.4683920028, 0. 858 4469999, 1.3248481 95, 1 .56 4463 956 , 2.34210 358 9, 2. 753 604 757 ,
3.161013 255 , 3. 355 203918]
[0.31 054 258 25, 0.6 851 910142, ... simulation models include those by Banks
et al. (20 05) , Fishman (1978), Law and Kelton (2000), and Pidd (1998).
Simulation and Monte Carlo: With...
Matematik simulation and monte carlo with applications in finance and mcmc phần 6 doc
... number
in [0,1) insert R= x/61. Answers: (a) = 60, (b) = 30.
Simulation and Monte Carlo: With applications in finance and MCMC J. S. Dagpunar
© 2007 John Wiley & Sons, Ltd
184 Markov chain ... (8.21)
In the original study by Gaver and O’Muircheartaigh (1987) and also in several follow-
up analyses of this data set, including those by Robert and Casella...
Matematik simulation and monte carlo with applications in finance and mcmc phần 7 ppt
... random number generator
‘schrage’. The final execution group sets an (initial) seed and calls ‘expon’
three times.
2
6
6
6
6
6
6
6
4
Simulation and Monte Carlo: With applications in finance and ... randomize(1 35) ;n:=1000;
1 35
n:=1000
2
6
4
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
4
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6...
Matematik simulation and monte carlo with applications in finance and mcmc phần 8 pot
... randomize(13 753 );asiannaive(0. 05, 50,0.3,1,16, 250 00 ,50 );
randomize(13 753 );asiannaive(0. 05, 50,0.3,1,16,
250 00, 45) ;
randomize(13 753 );asiannaive(0. 05, 50,0.1,1,16, 250 00 ,55 );
randomize(13 753 );asiannaive(0. 05, 50,
0.1,1,16, 250 00 ,50 );
randomize(13 753 );asiannaive(0. 05, 50,0.1,1,16, 250 00, 45) ;
13 753
‘K’ =50 , ... seed:=randomize(341):
print("seed"=seed);...
Matematik simulation and monte carlo with applications in finance and mcmc phần 9 ppt
... 9624 651
‘lambda’ ¼ 4:700068 356 ; ‘beta’ ¼
0:772172 051 123948266
0:38 052 3 654 34193 358 2
0 :51 3898302 957 2342 65
0:3014 051 897630771 75
2
6
6
4
3
7
7
5
‘K’=660, ‘spot’=660., ‘r’=0.04, ‘n’=4, ‘t’=0, ‘T’=0 .5,
‘x’ ... 4:mu:=0. 15: beta:=2 .5:
n:= 25:
beta1:=1/beta:
bet
a2:=evalf(GAMMA(beta1+1)):
beta3:=evalf(GAMMA(2/beta+1)):
t0:=
[
3.3,1.4 ,5. 7,3.3,0.1,1.2,2.1 ,5. 6,3.2,2,6,7....
Matematik simulation and monte carlo with applications in finance and mcmc phần 10 potx
... 56
Cauchy 55
exponential 46
normal 47, 55
relocated mode 55 56
Regeneration points 1 45, 154
G/G/1 queue 1 45, 147
M/G/1 queue 1 45
M/M/1 queue 154
Regenerative analysis 144–146, 155 – 156
Regression, ... purchasing foreign 116
Customers, rejected 149– 151 , 155
Delta, see Hedging parameter (delta)
Delta hedging, see Hedging
Derivative, financial 1, 107, 111, 129
Simulation an...