Matematik simulation and monte carlo with applications in finance and mcmc phần 1 pps

Matematik simulation and monte carlo with applications in finance and mcmc phần 1 pps

Matematik simulation and monte carlo with applications in finance and mcmc phần 1 pps

... =1 2 (2 .1) Choose X 0 ∈0 15 , say X 0 =3. Then X 1 =30 mod 2 4 =14 X 2 =12 9 mod 2 4 =1 . The following sequence for  R i  is obtained: 3 16  14 16  1 16  12 16  15 16  10 16  13 16  8 16  11 16  6 16  9 16  4 16  7 16  2 16  5 16  0 16  3 16  ... appear random. Simulation and Monte Carlo: With appli...

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Matematik simulation and monte carlo with applications in finance and mcmc phần 2 pdf

Matematik simulation and monte carlo with applications in finance and mcmc phần 2 pdf

... Odd 1 06 1 29 1 30 2 31 1 630360 016 0 1 29 292 1 64 2 16 293 Odd 1 20 1 07 1 45 2 31 1 950,706,376 0 2 31 1 742,938,285 0 2 31 1 1,226,874 ,15 9 0 2 31 1 62,089, 911 0 2 31 1 1,343, 714 ,438 ... aX i 1 +c. For example, in the generator (2 .1) X 7 = 9 13 +3mod 16  In binary arithmetic, X 7 =  10 01  × 11 01  + 11   mod 10 000. . Now 10 01  ×...

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Matematik simulation and monte carlo with applications in finance and mcmc phần 3 ppsx

Matematik simulation and monte carlo with applications in finance and mcmc phần 3 ppsx

... e.s.e.      vrr 1 525 12 6.2 0 013 82 000092 3 0 013 99 0000 31 29 1 525 12 6.5 000474 000032 9 000498 000 012 70 2 51 512 9 0 019 37 000057 12 0 018 72 000039 24 2 51 52 418 00 017 6 000008 50 00 015 6 ... +2 1/ √ 2 + 1/ √ 2 = 1 4 e √ 2x +1  x<− √ 2 2   1/ √ 2 +x +1/ √ 2 1/ √ 2 +2 1/ √ 2 +1/ √ 2 = √ 2x +2 4  x≤ √ 2 2   4/ √ 2 − 1/ √...

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Matematik simulation and monte carlo with applications in finance and mcmc phần 4 pptx

Matematik simulation and monte carlo with applications in finance and mcmc phần 4 pptx

... (£)  13 34 0 0 12 7 13  41 0 15 1 45 13 14 00 005 475 515 30 009 867 317 27 0 13 13 2 2 21 54 022 22 18 11 8 Simulation and finance 6.4 Asian options 6.4 .1 Naive simulation Result (6 .15 ) showed ... call asiannaive a asianimppoststrat b K  c 1   Var   c 1   c 2   Var   c 2  v.r.r. c 03552 214 9 00300 2 211 6 0000 313 (6 81, 119 2) 03504 16...

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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 5 ppsx

... 1 054 2 3 3 015 0 3 20302 1 9524 1 9 913 2 1 903 2 15 7 1 839 2066 10 11 11 449 11 10 3273 10 15 97 10 2435 10 9087 11 5688897 11 422 numbers. For best effect a given random number and its ... 0.8584469999, 1. 32484 819 5, 1. 564463956, 2.34 210 3589, 2.753604757, 3 .16 1 013 255, 3.355203 918 ] [0. 310 5425825, 0.68 519 1 014 2, 1. 02550 615 2, 1. 0363...

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Matematik simulation and monte carlo with applications in finance and mcmc phần 6 doc

Matematik simulation and monte carlo with applications in finance and mcmc phần 6 doc

... 2303 317 10 66 11 81 923 7756 2656 879 12 32 697 3368 486 6767 484 438 18 60 11 3 6062 590 16 33 2425 367 712 953 19 89 768 600 30 41 1 814 14 1 10 511 7796 14 62 Single component MH and Gibbs sampling 16 7 a ... of (8.22),  10 + 1 ≥u 11 leads to  10 + 1 ≥ R 11  10 + 1 0 , that is  ≥  0 R 1/ 10 + 1 11  Finally, e − ≥ u 12 leads to e − ≥ R 12...

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Matematik simulation and monte carlo with applications in finance and mcmc phần 7 ppt

Matematik simulation and monte carlo with applications in finance and mcmc phần 7 ppt

... distance:=proc(n) local j,x1,x2,y1,y2,d; for j from 1 to n do; x1:= rand() /10 ^12 ; y1:=rand() /10 ^12 ; x2:=rand() /10 ^12 ; y2:=rand() /10 ^12 ; d [ j ] :=sqrt((x1-x2)^2+(y1-y2)^2); end do; seq(d [ j ] ,j =1 n); end proc: 2 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 4 > ... 0.64, 1. 39, 0.62, 0.22, 0 .15 , 2. 21, 0.76, 0.80, 0.97, 1. 06, 0.26, 0.47, 0 .13 , 0.28, 0 .18 , 0.57, 1. 35, 0. 61,...

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Matematik simulation and monte carlo with applications in finance and mcmc phần 8 pot

Matematik simulation and monte carlo with applications in finance and mcmc phần 8 pot

... j,r1,r2,w,v,y,m,sum1,sum2,k1,s1, mean; sum1:=0;sum2:=0; for k1 from 1 to k do; s1:=0; for j f rom 1 to n do; for m from 1 to n do; r1:=(j -1+ evalf(rand() /10 ^12 ))/n; r2:=(m -1+ evalf(rand() /10 ^12 ))/n; w:=(-ln(r2)) ^(2/3); v:=(-ln(r1))^(2/3); y:=(v+w)^(5/4); s1:=s1+y; end ... with( finance) : h > B:=blackscholes (10 0, 97, 0.05, 10 3/252, 0.2); evalf(B); B : ¼50 erf 0:0242 71...

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Matematik simulation and monte carlo with applications in finance and mcmc phần 9 ppt

Matematik simulation and monte carlo with applications in finance and mcmc phần 9 ppt

... then; na:=na +1; randomize(seed1); seed1:=rand(); r1:=evalf(seed1 /10 ^12 ); a:=clock-ln(r1)/lambda; q: =1; else # BOUND STATE CHANGE:DEPARTURE; t [ 1 ] :=infinity; t:=sort(t); nocc:=nocc -1; nout1:=nout1 +1; end ... 2.5,4.4,2.5,4.8,3.5,infinity,infinity,infinit y, infinity,infinity ] : print("_______________________INPUT DATA____________________"); seeda:=873 41: seedb:=6428...

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Matematik simulation and monte carlo with applications in finance and mcmc phần 10 potx

Matematik simulation and monte carlo with applications in finance and mcmc phần 10 potx

... 11 2, 11 3, 11 6, 11 7 Hedging 11 2, 11 5 11 7, 12 9, 13 1 13 2 Hedging parameter (delta) 11 2, 13 0 Hyperparameters 16 7, 16 9, 17 2, 17 4 Hyperprior density 17 2 Importance sampling 4, 82–86 Asian option 11 9 12 1 basket ... 590, 16 33, 2425, 367, 712 , 953, 19 89, 768, 600, 30 41, 18 14, 14 1, 10 511 , 7796, 14 62 ] ; x :¼ [293, 19 02, 12 72, 2987, 469, 318 5, 17 1...

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