,.,.,,. [15],.,.,,., , 1., , 1., 1,., 1,,., 1. i
|
|
- えつと みょうだに
- 7 years ago
- Views:
Transcription
1 ( ) 19 1
2 ,.,.,,. [15],.,.,,., , 1., , 1., 1,., 1,,., 1. i
3 t t ii
4 iii
5 ,000-3, ,000-3, , (2,000, ) (2,000, ) (2,000, ) (2,000, ) (2,000, ) (2,000, ) (2,000-3,000, ) (2,000-3,000, ) (3,000-30,000, ) (3,000-30,000, ) ,000 ( 1) ,000 ( 2) ,000 ( 3) ,000-3, ,000-30, ,000 ( 1) ,000 ( 2) ,000 ( 3) ,000-3, ,000-30, Up Down t t t t iv
6 1 1.1,.,,.,.,,., ( ),.,,,,.,., [14], ( ), ( ), ,.,,,. [15],,.,,, (Up Down). Up Down 1. Up Down 1,., 1, , , : 2 :, ; 3 :,, ; 1
7 4 :., 1, , 1., 1, 2 ; 5 : 1,, ; 6 :. 2
8 2 2.1, 2.,,., ( ). (CDA: continuous double auction ), Cohen et al. [2], Luckock [7], Tang and Tian [13], Maslov [8], Slanina [11], Daniels et al. [3], Farmer et al. [4], Smith et al. [12].,,.,,,., [15], ( ) ( ),. 2.2,.,, /.,.,., ( ) ( ) ( ),,.,,.,,., 2.1.,,. 3
9 2.1:,, 2.,.,,.,. (depth). (Ask) (Bid) ( 2.2). 2.2:,.,., ( ) 4
10 ( ) ( ), ( ) ( ) ( )., :,, 2 ( ) ( ) 4.,. M/M/1.,., : (µa ) ra r A (2.1) f A (t) = t e (µ A+λ A )t I ra (2 µ A λ A t). (2.2) f B (t) =, λ A (µb λ B f A (t), f B (t) : ( ) ; ) rb r B t e (µ B+λ B )t I rb (2 µ B λ B t). r A, r B : ; µ A, λ A, µ B, λ B : 4 ; I r (z) 1, I r (z) = I r (z) = ( ( z )r z 2n 2) 2 n=0 n!(r + n)!., { 1, ρ A 1, (2.3) P A = P (T A ) = r ρ A A, ρ A > 1. { 1, ρ B 1, (2.4) P B = P (T B ) = r ρ B B, ρ B > 1. 5
11 , T A, T B P A, P B : ( ) ; : ( ) ; ρ A, ρ B : (ρ A = λ A /µ A, ρ B = λ B /µ B ).,, : ( (2.5) f U (t) = f A (t) 1 ( (2.6) f D (t) = f B (t) 1 t 0 t 0 ) f B (τ)dτ, ) f A (τ)dτ., ( ) P U (P D ), (2.7) P U = P (T U ) = (2.8) P D = P (T D ) = f U (t)dt = P A f A (t) t f D (t)dt = P B f B (t) t f B (τ)dτ. f A (τ)dτ.,,,, 1,.,,,,.., ( ), ( ),., ( ), ( ), ( ) 2 ( ),.,,., 2, r U A,r U B,r D A,r D B, 1. : 6
12 (2.9) P = ( ) P UU P UD P DU P DD (2.10) P UU = P U (r A U, r B U ) = f U (t)dt = P A f A (t)dt t f B (τ)dτ. (2.11) P DU = P U (r A D, r B D ) = f U (t)dt = P A f A (t)dt t f B (τ)dτ. (2.12) P UD = P D (r A U, r B U ) = f D (t)dt = P B f B (t)dt t f A (τ)dτ. (2.13) P DD = P D (r A D, r B D ) = f D t)dt = P B f B (t)dt t f A (τ)dτ. r A U, r B U, r A D, r B D :, ; P UU :, ; P UD :, ; P DU :, ; P DD :, 7
13 3 3.1,.,,., Niederhoffer and Osborne [10], Fielitz and Bhargava [6], Fielitz [5], McQuenn and Thorley [9].,,,. Fielitz and Bhargava [6], ,, 3 (Up, Down, No movement). 2., 200 (Order) ;,.,. McQuenn and Thorley [9], NYSE,, 2 (Up and Down) 2. 2., Niederhoffer and Osborne [10],., 6, /8dollar, Anderson and Goodman [1],.,,.,,, 1, , Nonparametric,,.,,,.,. 8
14 , 2 ( ), 2 1. : 1. S = U, D : ; 2. X t, t = 0, 1, 2,... :. X t = U X t = D; 3. n ijk : i, j, k ( n U,U,D 2 ); 4. n ijkl : i, j, k, l ; 5. P ij = P {X t = j X t 1 = i}; 6. P ijk = P {X t = k X t 1 = j, X t 2 = i}; 7. P ijkl = P {X t = l X t 1 = k, X t 2 = j, X t 3 = i};, Anderson and Goodman [1] 1 2.,,.,,,., (null hypothesis) u (uth-order), (alternative hypothesis) u + 1.,, P ijk = P jk, for i = 1, 2,..., m (i, k S; j S u ). (likelihood ratio criterion) : (3.1) λ = i,j,k( ˆP jk / ˆP ijk ) n ijk. P jk ˆP jk. (3.2) ˆPjk = i S n ijk / i S n ijk. k S 2 log λ, m u (m 1) 2 χ 2. m 2 (U D), u. 5%.,., m m m (3.3) 2 log λ = 2 n ijk (log P ijk log P jk ) i=1 j=1 k=1 9
15 m(m 1) 2 = 2, 2 log λ > 5.991,., 2. (3.4) 2 log λ = 2 m m m i=1 j=1 k=1 l=1 m n ijkl (log P ijkl log P jkl ) m 2 (m 1) 2 = 4, 2 log λ > 9.488,.,.,,., 5%, K. (3.5) K k=0 ( ) n 0.95 n k 0.05 k = k, n. K,. K, (, 1 ), (, 2 ) (, 3 ). 1,., : 1. ; 2. ;
16 , 1 1,497, 2 1,527, 3 1,609.,,,,., 1,, 1.,. : 1. :.. (a) :. (b) :. 2. :,.,, 3.1., 1.,,. 30,000,. 30,000, 2,000, 2,000 3,000 3,000 30, ,000 3,000 3,000 30,000,.,,. 3.1: 2, ,000,000 1,000 3, ,000,000 10,000 30, ,000,000 50,000 50, ,000, , ,
17 3.3.2,,, 1,.,.,,., 2.. 9,.,,,. 5%,.,.,,,,.., ( ) 20,,.,
18 4 4.1,,. 4.2,,,,. Group. Total. G1 G5,, 300 1, 5. G1 300, G5 4. Top100 Top : 1 ( ) 2 ( ) ( ) Group Total 1 76,808 1, ,633 1, ,459 2,190 Top50 9,116 76,808 18,248 8, ,633 19,591 12, ,459 31,305 Top100 5,503 76,808 12,671 5, ,633 13,348 7, ,459 20,592 G1 1,461 76,808 6,125 1, ,633 6,552 2, ,459 9,682 G , ,752 1, ,305 1,361 G G G ,., 3 2,.,, 2,000 Group Top50.,, (,, ).,,.,,.,,., 2,000-3,000 3,000-30,000 G1(Top300). 13
19 4.3: 1-1.5tick 1.5-2tick 2-3tick 3-4tick 4-5tick >=5tick K-tick. K-tick, K., K-tick,,., 3. 6, K-tick, 6,., %. 30%.,, : 3,000-3,
20 4.5: 2,000-3, : 2,
21 4.2, 1.,,,, 1,, , ( ) 2 (Up Down),, 1., 5% ,. : ( (4.1), P UU = P DU = n UU n UU + n UD ; P UD = n DU n DU + n DD ; P DD = P = n UD n UU + n UD ; P UU P DU n DD n DU + n DD n ij i, j. P UD P DD,, 1 ( 4511, ). ( ) , ,. 1,. ) 16
22 4.4: - 4.5: - 17
23 . 4.7: - 1(2,000, ) P uu P ud P du P dd P uu P ud P du P dd Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD
24 4.8: - 1(2,000, ) P uu P ud P du P dd P uu P ud P du P dd Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD
25 4.9: - 2(2,000, ) P uu P ud P du P dd P uu P ud P du P dd Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD
26 4.10: - 2(2,000, ) P uu P ud P du P dd P uu P ud P du P dd Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD
27 4.11: - 3(2,000, ) P uu P ud P du P dd P uu P ud P du P dd Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD
28 4.12: - 3(2,000, ) P uu P ud P du P dd P uu P ud P du P dd Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD
29 4.13: (2,000-3,000, ) P uu P ud P du P dd P uu P ud P du P dd Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD : (2,000-3,000, ) P uu P ud P du P dd P uu P ud P du P dd Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD
30 4.15: (3,000-30,000, ) P uu P ud P du P dd P uu P ud P du P dd Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD : (3,000-30,000, ) P uu P ud P du P dd P uu P ud P du P dd Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD
31 , ( ) ( ), , ( ) ( ), , 2 ( ).,, ,,,.,.. T, 1, F, : 1 2,000 ( 1) T T T T T T T T T T F T T T T T T T T T T T T T T T 26
32 4.18: 2,000 ( 2) F F T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T 27
33 4.19: 1 2,000 ( 3) F F T T T T F F F F F F T T F F T T F F T T F F T T T T F F T T F F F T F F F F 28
34 4.20: 1 2,000-3, T T F T T T T T F F T T T F F F F F 4.21: 1 3,000-30, T T T T T T F F F F T T T T T T T F, , 3, , 1, 2,., 1 2.,, 1 1., 1, 2. 2, 1 29
35 . 1, 1, 3 (3.3), 2, 3 (3.4).,. 4.22: 2 2,000 ( 1) T T T T T T T T T T T T T T T T T T T T T T T T T T 30
36 4.23: 2 2,000 ( 2) T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T 4.24: 2 2,000 ( 3) T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T T F 31
37 4.25: 2 2,000-3, T T T T T T T T T T T T T T F T T T 4.26: 2 3,000-30, T T T T T T F F T T T T T T T T T T, 1, 2,. 2, 1.,,. 32
38 5,, 1.,,, , 3., 3 11,.,.,, 2, ,. 5.28,., u : ; d : ; u i d i : i ; : i ; R u ui = u i u ; R d d i = d i d.,. 4, 6. R u ui R d di,., 1, ( ). 33
39 5.27: Up u2 R u u2 u3 R u u3 u4 R u u4 u5 R u u5 u6 R u u6 u7 R u u u2 R u u2 u3 R u u3 u4 R u u4 u5 R u u5 u6 R u u6 u7 R u u
40 5.28: Down d2 R d d2 d3 R d d3 d4 R d d4 d5 R d d5 d6 R d d6 d7 R d d d2 R d d2 d3 R d d3 d4 R d d4 d5 R d d5 d6 R d d6 d7 R d d
41 5.2, 1,.,.,,,,.,, 1.,, 1., 1,,., ( ),, ( )., ( ), ( ),.,, 1, 1., 1, t t,,, 1., 1. ( ) ( ), 5% t , 5%. F,, T, 36
42 5.29: t t , , F , , T , , F , ,153, F , , F , , F ,690, ,380, T , , F , , F , , F , , F , , F , , T , , F , , F , , F , , F , , T , , F , , F , , F , , F 37
43 5.30: t t , , T , , T , , F , ,007, F , , F , , F ,411, ,344, T 127 2,214, ,779, F , , F , , F , , F , , F , , T , , F , , F , , F , , F , , F , , F , , F , , F , , F 38
44 ,.,, 1.,, t, 1 1., 1. ( ) 1, 5% t. 5.31: 1 t t ,406 21,506 21,101 16, F 3,153 23,239 19,259 16, F ,314 52,658 63,261 62, T 10,185 49,008 66,734 57, T ,614 12,251 25,201 14, T 1,975 9,155 23,244 12, T ,364 97,176 10,725 81, F 3,010 71,060 10,542 95, T ,010 71,060 10,542 95, T 4,182 12,339 23,851 14, T ,252 10,421 46,141 11, T 6,592 11,597 43,218 11, T ,781 13,952 60,832 12, F 3,964 10,626 59,209 12, T ,900 13,133 21,847 12, F 4,466 11,284 21,422 12, T ,287 23,237 36,739 19, F 6,580 23,927 40,447 19, F ,112 19,222 35,113 21, T 4,004 13,167 35,809 20, T ,162 7,130 96,878 6, F 16,156 6, ,484 6, T 39
45 5.32: 1 t t ,195 20,823 20,203 14, F 3,725 18,099 19,480 14, F ,138 50,597 64,602 47, F 7,682 44,458 67,734 46, T ,898 12,556 24,767 12, T 2,766 10,816 22,691 10, T , ,408 9,680 80, F 2,741 92,273 9,356 73, F ,297 13,423 24,393 14, T 5,888 12,674 20,669 13, T ,040 9,217 47,488 9, T 4,993 10,505 45,256 9, F ,478 11,882 59,013 11, F 5,463 12,987 55,716 11, F ,316 9,556 20,564 10, T 4,183 10,025 19,779 10, T ,661 18,885 35,919 16, F 6,166 20,367 38,427 16, F ,721 15,028 33,647 18, T 3,424 12,293 31,756 18, T ,769 5,740 93,533 6, T 15,955 6, ,641 5, F,. 1 1., 1. 1.,. 40
46 6 [2006] 1.,. 1, 1, ,,.,, , 1., , 1., 1., 1,,., 1.,,. 41
47 [1] Anderson, T.W., Goodman, L.A., Statistical inference about Markov chains, Annals of Mathematical Statistics 28(1957) [2] Cohen, K., Conroy, R. and Maier, S., Market Making and the Changing Structure of the Securities Industry, Rowman and Littlefield, Lanham (1985) [3] Daniels, M., Farmer, J., Iori, G. and Smith, E., Quantitative Model of Price Diffusion and Market Friction Based on Trading as a Mechanistic Random Process, Physical Review Letters, 90(2003) [4] Farmer, J., Patelli, P. and Zovko, I., The Predictive Power of Zero Intelligence in Financial Markets, Technical report, Santa Fe Institute Working Paper (2003). [5] Fielitz, B.D., On the stationarity of transition matrices of common stocks, Journal of Financial and Quantitative Analysis, 10(1975) [6] Fielitz, B.D., Bhargava, T.N., The behavior of stock-price relatives: A Markovian analysis, Operations Research, 21(1973) [7] Luckock, H., A Steady-State Model of the Continuous Double Auction, Quantitative Finance, 3(2003) [8] Maslov, S., Simple Model of a Limit Order-Driven Market, Physica A: Statistical Mechanics and Its Applications, 278(2000) [9] McQuenn, G., Thorley, S., Are stock returns predictable? A test using Markov chains, Journal of Finance, 46(1991) [10] Niederhoffer, V., Osborne, M., Market making and reversal on the stock exchange, Journal of the American Statistical Association, 61(1966) [11] Slanina, F., Mean-Field Approximation for a Limit Order Driven Market Model, Physical Review E, 64(2001) [12] Smith, E., Farmer, J., Gillemot, L. and Krishnamurthy, S., Statistical Theory of the Continuous Double Auction, Quantitative Finance, 3(2003) [13] Tang, L. and Tian, G., Reaction-Diffusion-Branching Models of Stock Price Fluctuations, Physica A: Statistical Mechanics and Its Applications, 264(1999)
48 [14],, [15],,, 16(2006)
49 ,,,.,,.,,.,. 44
082_rev2_utf8.pdf
3 1. 2. 3. 4. 5. 1 3 3 3 2008 3 2008 2008 3 2008 2008, 1 5 Lo and MacKinlay (1990a) de Jong and Nijman (1997) Cohen et al. (1983) Lo and MacKinlay (1990a b) Cohen et al. (1983) de Jong and Nijman (1997)
More informationautocorrelataion cross-autocorrelataion Lo/MacKinlay [1988, 1990] (A)
Discussion Paper Series A No.425 2002 2 186-8603 iwaisako@ier.hit-u.ac.jp 14 1 24 autocorrelataion cross-autocorrelataion Lo/MacKinlay [1988, 1990] 1990 12 13 (A) 12370027 13 1 1980 Lo/MacKinlay [1988]
More informationVol.57 No.4 March 2008 : () 1 () () (1 ) (2 ) ( ) 1 Takagi (1989)Hamao (1992) (1998)
Title Author(s) 東京証券取引所における株式取引 : 2001 年から 2003 年 太田, 亘 Citation 大阪大学経済学. 57(4) P.242-P.262 Issue Date 2008-03 Text Version publisher URL https://doi.org/10.18910/15995 DOI 10.18910/15995 rights Vol.57
More information4 ( ) NATURE SCIENCE [Battiston 16] 2008 ( ) 5 JPX [ 13] [ 15a, 15b] [ 15,Mizuta 16c] [ 15a, 15b] δt (δt =1) (δt > 1) 4 [ 09, 12] 5 [LeBaron 06,Chen 1
1 Takanobu Mizuta 2 Kiyoshi Izumi 1 SPARX Asset Management Co., Ltd. 2 School of Engineering, The University of Tokyo 1. 2000 2010 1 () ( ) [Farmer 12, Budish 15] [Budish 15] ( ) [Budish 15] : mizutata@gmail.com
More information- - - - - - - - - - - - - - - - - - - - - - - - - -1 - - - - - - - - - - - - - - - - - - - - - - - - - - - - -2...2...3...4...4...4...5...6...7...8...
取 扱 説 明 書 - - - - - - - - - - - - - - - - - - - - - - - - - -1 - - - - - - - - - - - - - - - - - - - - - - - - - - - - -2...2...3...4...4...4...5...6...7...8...9...11 - - - - - - - - - - - - - - - - -
More information2016 Institute of Statistical Research
2016 Institute of Statistical Research 2016 Institute of Statistical Research 2016 Institute of Statistical Research 2016 Institute of Statistical Research 2016 Institute of Statistical Research 2016 Institute
More information「産業上利用することができる発明」の審査の運用指針(案)
1 1.... 2 1.1... 2 2.... 4 2.1... 4 3.... 6 4.... 6 1 1 29 1 29 1 1 1. 2 1 1.1 (1) (2) (3) 1 (4) 2 4 1 2 2 3 4 31 12 5 7 2.2 (5) ( a ) ( b ) 1 3 2 ( c ) (6) 2. 2.1 2.1 (1) 4 ( i ) ( ii ) ( iii ) ( iv)
More informationk3 ( :07 ) 2 (A) k = 1 (B) k = 7 y x x 1 (k2)?? x y (A) GLM (k
2012 11 01 k3 (2012-10-24 14:07 ) 1 6 3 (2012 11 01 k3) kubo@ees.hokudai.ac.jp web http://goo.gl/wijx2 web http://goo.gl/ufq2 1 3 2 : 4 3 AIC 6 4 7 5 8 6 : 9 7 11 8 12 8.1 (1)........ 13 8.2 (2) χ 2....................
More information上場変更と株価:株主分散と流動性変化のインパクト
Merton Amihud and Mendelson NASDAQ JASDAQ JASDAQ JASDAQ QUICK E-mailjuno@waseda.jp E-mailshibata-mai@c.metro-u.ac.jp E-mailtakeshi.shimatani@boj.or.jp E-mailtokiko.shimizu@boj.or.jp JASDAQ JASDAQ Merton
More information人工知能学会研究会資料 SIG-FPAI-B Predicting stock returns based on the time lag in information diffusion through supply chain networks 1 1 Yukinobu HA
人工知能学会研究会資料 SIG-FPAI-B508-08 - - Predicting stock returns based on the time lag in information diffusion through supply chain networks 1 1 Yukinobu HAMURO 1 Katsuhiko OKADA 1 1 1 Kwansei Gakuin University
More informationuntitled
2011 59 1 67 87 c 2011 2010 9 24 2011 1 25 1 26 unbiaseness regression unbiaseness regression 1. Kyle 1985 inicative quote Biais et al. 1999 unbiaseness regression Cao et al. 2000 Mahavan an Panchapagesan
More information44 4 I (1) ( ) (10 15 ) ( 17 ) ( 3 1 ) (2)
(1) I 44 II 45 III 47 IV 52 44 4 I (1) ( ) 1945 8 9 (10 15 ) ( 17 ) ( 3 1 ) (2) 45 II 1 (3) 511 ( 451 1 ) ( ) 365 1 2 512 1 2 365 1 2 363 2 ( ) 3 ( ) ( 451 2 ( 314 1 ) ( 339 1 4 ) 337 2 3 ) 363 (4) 46
More informationi ii i iii iv 1 3 3 10 14 17 17 18 22 23 28 29 31 36 37 39 40 43 48 59 70 75 75 77 90 95 102 107 109 110 118 125 128 130 132 134 48 43 43 51 52 61 61 64 62 124 70 58 3 10 17 29 78 82 85 102 95 109 iii
More informationMicrosoft Word _DOCMAS docx
的板モデルを用いた株式市場の統計性の分析 Analysing Statistical Properties of Stock Markets Using Statistical Order-Book Model 1* 1,2 1,3 宮崎文吾和泉潔山田健太 Bungo MIYAZAKI 1, Kiyoshi IZUMI 1,2 and Kenta YAMADA 1,3 1 東京大学大学院工学系研究科
More informationuntitled
2009 57 2 393 411 c 2009 1 1 1 2009 1 15 7 21 7 22 1 1 1 1 1 1 1 1. 1 1 1 2 3 4 12 2000 147 31 1 3,941 596 1 528 1 372 1 1 1.42 350 1197 1 13 1 394 57 2 2009 1 1 19 2002 2005 4.8 1968 5 93SNA 6 12 1 7,
More informationわが国企業による資金調達方法の選択問題
* takeshi.shimatani@boj.or.jp ** kawai@ml.me.titech.ac.jp *** naohiko.baba@boj.or.jp No.05-J-3 2005 3 103-8660 30 No.05-J-3 2005 3 1990 * E-mailtakeshi.shimatani@boj.or.jp ** E-mailkawai@ml.me.titech.ac.jp
More information130 Oct Radial Basis Function RBF Efficient Market Hypothesis Fama ) 4) 1 Fig. 1 Utility function. 2 Fig. 2 Value function. (1) (2)
Vol. 47 No. SIG 14(TOM 15) Oct. 2006 RBF 2 Effect of Stock Investor Agent According to Framing Effect to Stock Exchange in Artificial Stock Market Zhai Fei, Shen Kan, Yusuke Namikawa and Eisuke Kita Several
More informationdvi
2017 65 1 87 111 c 2017 2016 7 15 12 27 2017 1 23 2015 9 Hawkes 10 Hawkes 1. HFT 2010 1 arrowhead 40% 2012 2012 2013 2014 2015 UFJ 107 0052 4 2 6 88 65 1 2017 1 2015 7 1 2015 12 29 2015 2015 1 2015 9 1
More information03.Œk’ì
HRS KG NG-HRS NG-KG AIC Fama 1965 Mandelbrot Blattberg Gonedes t t Kariya, et. al. Nagahara ARCH EngleGARCH Bollerslev EGARCH Nelson GARCH Heynen, et. al. r n r n =σ n w n logσ n =α +βlogσ n 1 + v n w
More information23_02.dvi
Vol. 2 No. 2 10 21 (Mar. 2009) 1 1 1 Effect of Overconfidencial Investor to Stock Market Behaviour Ryota Inaishi, 1 Fei Zhai 1 and Eisuke Kita 1 Recently, the behavioral finance theory has been interested
More information論文08.indd
* 1 はじめに,, TOPIX TOPIX, TOPIX TOPIX Shelor Anderson and Cross C Japan Society of Monetary Economics 図 1 東日本大震災前後の株価 (TOPIX) の推移 1,000 950 900 850 800 750 700 図 2 阪神大震災前後の株価 (TOPIX) の推移 1,650 1,550 1,450
More informationカルマンフィルターによるベータ推定( )
β TOPIX 1 22 β β smoothness priors (the Capital Asset Pricing Model, CAPM) CAPM 1 β β β β smoothness priors :,,. E-mail: koiti@ism.ac.jp., 104 1 TOPIX β Z i = β i Z m + α i (1) Z i Z m α i α i β i (the
More informationjohnny-paper2nd.dvi
13 The Rational Trading by Using Economic Fundamentals AOSHIMA Kentaro 14 2 26 ( ) : : : The Rational Trading by Using Economic Fundamentals AOSHIMA Kentaro abstract: Recently Artificial Markets on which
More informationStepwise Chow Test * Chow Test Chow Test Stepwise Chow Test Stepwise Chow Test Stepwise Chow Test Riddell Riddell first step second step sub-step Step
Stepwise Chow Test * Chow Test Chow Test Stepwise Chow Test Stepwise Chow Test Stepwise Chow Test Riddell Riddell first step second step sub-step Stepwise Chow Test a Stepwise Chow Test Takeuchi 1991Nomura
More informationuntitled
Amazon.co.jp 2008.09.02 START Amazon.co.jp Amazon.co.jp Amazon.co.jp Amazon Internet retailers are extremely hesitant about releasing specific sales data 1( ) ranking 500,000 100,000 Jan.1 Mar.1 Jun.1
More information本邦株式市場の流動性に関する動学的考察―東京証券取引所のティック・データ分析―
tightnessdepth resiliency BIS Market Liquidity: Research Findings and Selected Policy Implications BISWorld Wide Web http://www.bis.org E-mail: jun.muranaga@boj.or.jp Muranaga and Shimizu O Hara Muranaga
More information01.Œk’ì/“²fi¡*
AIC AIC y n r n = logy n = logy n logy n ARCHEngle r n = σ n w n logσ n 2 = α + β w n 2 () r n = σ n w n logσ n 2 = α + β logσ n 2 + v n (2) w n r n logr n 2 = logσ n 2 + logw n 2 logσ n 2 = α +β logσ
More informationHi-Stat Discussion Paper Series No.248 東京圏における 1990 年代以降の住み替え行動 住宅需要実態調査 を用いた Mixed Logit 分析 小林庸平行武憲史 March 2008 Hitotsubashi University Research Unit
Hi-Stat Discussion Paper Series No.248 東京圏における 1990 年代以降の住み替え行動 住宅需要実態調査 を用いた Logit 分析 小林庸平行武憲史 March 2008 Hitotsubashi University Research Unit for Statistical Analysis in Social Sciences A 21st-Century
More informationJournal of Economic Behavior & Organization Quarterly Journal of Economics Review of Economics and Statistics Internal Labor Markets and Manpower Analysis Economics of Education Review Journal of Political
More informationuntitled
financial report - 1 - - 2 - - 3 - - 4 - - 5 - - 6 - - 7 - - 8 - - 9 - - 10 - - 11 - - 12 - - 13 - - 14 - - 15 - - 16 - - 17 - - 18 - - 19 - - 20 - - 21 - - 22 - - 23 - - 24 - - 25 - - 26 - - 27 - -
More informationThe Options Clearing Corporation International Securities Exchange Chicago Board Options Exchange
The Options Clearing Corporation International Securities Exchange Chicago Board Options Exchange Nasdaq OMX Phlx Amex Arca Phlx Dividends Arbitrage Trade S&P 500 Russell 2000 Futures Industry Association
More informationi
14 i ii iii iv v vi 14 13 86 13 12 28 14 16 14 15 31 (1) 13 12 28 20 (2) (3) 2 (4) (5) 14 14 50 48 3 11 11 22 14 15 10 14 20 21 20 (1) 14 (2) 14 4 (3) (4) (5) 12 12 (6) 14 15 5 6 7 8 9 10 7
More information講義のーと : データ解析のための統計モデリング. 第5回
Title 講義のーと : データ解析のための統計モデリング Author(s) 久保, 拓弥 Issue Date 2008 Doc URL http://hdl.handle.net/2115/49477 Type learningobject Note この講義資料は, 著者のホームページ http://hosho.ees.hokudai.ac.jp/~kub ードできます Note(URL)http://hosho.ees.hokudai.ac.jp/~kubo/ce/EesLecture20
More informationAbout Starzen
About Starzen Top message Financial Review 3, 2, 1, 2,387 2,628 2,593 2,55 1,162 1,28 1,283 1,251 4 3 2 1 38.4 17.1 29.3 11.9 26.7 7.6 1. 2.3 4 3 2 1 4.1 33.6 19.3 15. 3.5 11.4 15. 2 15 1 5 14.4 4.2
More informationM&A の経済分析:M&A はなぜ増加したのか
RIETI Discussion Paper Series 06-J-034 RIETI Discussion Paper Series 06-J-034 M&A の経済分析 :M&A はなぜ増加したのか 蟻川靖浩 宮島英昭 ( 早稲田大学 RIETI) 2006 年 4 月 要旨 1990 年代以降の M&A の急増の主要な要因は 産業や企業の成長性や収益性へのショックである とりわけ M&A を活発に行っている産業あるいは企業の特性としては
More information第1部 一般的コメント
(( 2000 11 24 2003 12 31 3122 94 2332 508 26 a () () i ii iii iv (i) (ii) (i) (ii) (iii) (iv) (a) (b)(c)(d) a) / (i) (ii) (iii) (iv) 1996 7 1996 12
More informationyasi10.dvi
2002 50 2 259 278 c 2002 1 2 2002 2 14 2002 6 17 73 PML 1. 1997 1998 Swiss Re 2001 Canabarro et al. 1998 2001 1 : 651 0073 1 5 1 IHD 3 2 110 0015 3 3 3 260 50 2 2002, 2. 1 1 2 10 1 1. 261 1. 3. 3.1 2 1
More informationfiúŁÄ”s‘ê‡ÌŁª”U…−…X…N…v…„…~…A…•‡Ì ”s‘ê™´›ß…−…^†[…fiŠ‚ª›Âfl’«
2016/3/11 Realized Volatility RV 1 RV 1 Implied Volatility IV Volatility Risk Premium VRP 1 (Fama and French(1988) Campbell and Shiller(1988)) (Hodrick(1992)) (Lettau and Ludvigson (2001)) VRP (Bollerslev
More informationISSN NII Technical Report Patent application and industry-university cooperation: Analysis of joint applications for patent in the Universit
ISSN 1346-5597 NII Technical Report Patent application and industry-university cooperation: Analysis of joint applications for patent in the University of Tokyo Morio SHIBAYAMA, Masaharu YANO, Kiminori
More informationMicrosoft Word - Šv”|“Å‘I.DOC
90 ª ª * E-mailshinobu.nakagawa@boj.or.jp i ii iii iv SNA 1 70 80 2 80 90 80 80 90 1 80 90 98 6 1 1 SNA 2 1 SNA 80 1SNA 1 19931998 1 2-190 1,2 2 2-2 2-3,4 3 2-5 4 2030 2-3 3 2-15 97 20 90 2-15 9198 1.
More information証券市場の機能と証券業務
I Kiyoshi Nikami / 2012 3 277 1 111 166 60% 2006 3 288 228 60 21% 2008 9 1 1 28 1 300 30 FX 072 2012 winter / No.394 II 1 1 2004 34 3 2012 3 2 2 1 2004 3 2012 3 2004 3 2012 3 108 97 3,096,639 2,472,091
More informationCOE-RES Discussion Paper Series Center of Excellence Project The Normative Evaluation and Social Choice of Contemporary Economic Systems Graduate Scho
COE-RES Discussion Paper Series Center of Excellence Project The Normative Evaluation and Social Choice of Contemporary Economic Systems Graduate School of Economics and Institute of Economic Research
More informationL Y L( ) Y0.15Y 0.03L 0.01L 6% L=(10.15)Y 108.5Y 6%1 Y y p L ( 19 ) [1990] [1988] 1
1. 1-1 00 001 9 J-REIT 1- MM CAPM 1-3 [001] [1997] [003] [001] [1999] [003] 1-4 0 . -1 18 1-1873 6 1896 L Y L( ) Y0.15Y 0.03L 0.01L 6% L=(10.15)Y 108.5Y 6%1 Y y p L 6 1986 ( 19 ) -3 17 3 18 44 1 [1990]
More information表1票4.qx4
iii iv v 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 22 23 10 11 24 25 26 27 10 56 28 11 29 30 12 13 14 15 16 17 18 19 2010 2111 22 23 2412 2513 14 31 17 32 18 33 19 34 20 35 21 36 24 37 25 38 2614
More information競売不動産からみた首都圏地価の動向
E-mail : yumi.saita@boj.or.jp http://bit.sikkou.jp STYLE m m LancasterRosen Suzaki and Ohta Nagai, Kondo and Ohta i P i n lnp i = + jln X ij + k D ik + TD i + i. m j =1 k =1 X ij j D ik k TD i
More informationMantel-Haenszelの方法
Mantel-Haenszel 2008 6 12 ) 2008 6 12 1 / 39 Mantel & Haenzel 1959) Mantel N, Haenszel W. Statistical aspects of the analysis of data from retrospective studies of disease. J. Nat. Cancer Inst. 1959; 224):
More information第1章 国民年金における無年金
1 2 3 4 ILO ILO 5 i ii 6 7 8 9 10 ( ) 3 2 ( ) 3 2 2 2 11 20 60 12 1 2 3 4 5 6 7 8 9 10 11 12 13 13 14 15 16 17 14 15 8 16 2003 1 17 18 iii 19 iv 20 21 22 23 24 25 ,,, 26 27 28 29 30 (1) (2) (3) 31 1 20
More information橡表紙参照.PDF
CIRJE-J-58 X-12-ARIMA 2000 : 2001 6 How to use X-12-ARIMA2000 when you must: A Case Study of Hojinkigyo-Tokei Naoto Kunitomo Faculty of Economics, The University of Tokyo Abstract: We illustrate how to
More informationkubostat2018d p.2 :? bod size x and fertilization f change seed number? : a statistical model for this example? i response variable seed number : { i
kubostat2018d p.1 I 2018 (d) model selection and kubo@ees.hokudai.ac.jp http://goo.gl/76c4i 2018 06 25 : 2018 06 21 17:45 1 2 3 4 :? AIC : deviance model selection misunderstanding kubostat2018d (http://goo.gl/76c4i)
More information自殺の経済社会的要因に関する調査研究報告書
17 1 2 3 4 5 11 16 30,247 17 18 21,024 +2.0 6 12 13 WHO 100 14 7 15 2 5 8 16 9 10 17 11 12 13 14 15 16 17 II I 18 Durkheim(1897) Hamermesh&Soss(1974)Dixit&Pindyck(1994) Becker&Posner(2004) Rosenthal(1993)
More informationDocuPrint C5450 ユーザーズガイド
1 2 3 4 5 6 7 8 1 10 1 11 1 12 1 13 1 14 1 15 1 16 17 1 1 18 1 19 1 20 1 21 1 22 1 23 1 24 1 25 1 26 27 1 1 28 1 29 1 30 1 31 1 2 12 13 3 2 10 11 4 9 8 7 6 5 34 24 23 14 15 22 21 20 16 19 18 17 2 35
More information…K…E…X„^…x…C…W…A…fi…l…b…g…‘†[…N‡Ì“‚¢−w‘K‡Ì‹ê™v’«‡É‡Â‡¢‡Ä
2009 8 26 1 2 3 ARMA 4 BN 5 BN 6 (Ω, F, µ) Ω: F Ω σ 1 Ω, ϕ F 2 A, B F = A B, A B, A\B F F µ F 1 µ(ϕ) = 0 2 A F = µ(a) 0 3 A, B F, A B = ϕ = µ(a B) = µ(a) + µ(b) µ(ω) = 1 X : µ X : X x 1,, x n X (Ω) x 1,,
More informationAbstract Gale-Shapley 2 (1) 2 (2) (1)
( ) 2011 3 Abstract Gale-Shapley 2 (1) 2 (2) (1) 1 1 1.1........................................... 1 1.2......................................... 2 2 4 2.1................................... 4 2.1.1 Gale-Shapley..........................
More informationKobe University Repository : Kernel タイトル Title 著者 Author(s) 掲載誌 巻号 ページ Citation 刊行日 Issue date 資源タイプ Resource Type 版区分 Resource Version 権利 Rights DOI
Kobe University Repository : Kernel タイトル Title 著者 Author(s) 掲載誌 巻号 ページ Citation 刊行日 Issue date 資源タイプ Resource Type 版区分 Resource Version 権利 Rights DOI 平均に対する平滑化ブートストラップ法におけるバンド幅の選択に関する一考察 (A Study about
More informationTomorrow Next th draft version MEW SWET
Research Center for Price Dynamics A Research Project Concerning Prices and Household Behaviors Based on Micro Transaction Data Working Paper Series No.7 Tomorrow Next を用いた金融政策の分析 青野幸平 June 14, 2012 4th
More information物流からみた九州地方の地域的都市システムの変容
Working Paper Series Vol. 2009-05 2009 2 Working Paper 14 10 13 18 194-0298 4342 E-mail pakugen69@hosei.ac.jp 1 Pred 1977 1985 1994 2001 Murayama 1982,1984 Friedmann 1986 1994 2001 1 2 1979 1984 1991 2005
More informationHFT 1. はじめに A liquid market is a market where participants can rapidly execute large-volume transactions with a small impact on prices. Bank for Inter
HFT 1. はじめに A liquid market is a market where participants can rapidly execute large-volume transactions with a small impact on prices. Bank for International Settlements HFT 2. 市場の流動性の特徴 Brunnermeier
More informationわが国証券市場、証券業界の戦後70年
I 70 Kiyoshi Nikami / / 1 70 2 2 3 4 70 1 1987 1990 2 072 2015 winter / No.406 5 II 4 4 3 18 Loan Contractor 3 2011 6 391 2012 spring 70 073 4 4 originating Manager 4 M.H. 1965 6 1987 12 074 2015 winter
More information20 15 14.6 15.3 14.9 15.7 16.0 15.7 13.4 14.5 13.7 14.2 10 10 13 16 19 22 1 70,000 60,000 50,000 40,000 30,000 20,000 10,000 0 2,500 59,862 56,384 2,000 42,662 44,211 40,639 37,323 1,500 33,408 34,472
More information- 2 -
- 2 - - 3 - (1) (2) (3) (1) - 4 - ~ - 5 - (2) - 6 - (1) (1) - 7 - - 8 - (i) (ii) (iii) (ii) (iii) (ii) 10 - 9 - (3) - 10 - (3) - 11 - - 12 - (1) - 13 - - 14 - (2) - 15 - - 16 - (3) - 17 - - 18 - (4) -
More information2 1980 8 4 4 4 4 4 3 4 2 4 4 2 4 6 0 0 6 4 2 4 1 2 2 1 4 4 4 2 3 3 3 4 3 4 4 4 4 2 5 5 2 4 4 4 0 3 3 0 9 10 10 9 1 1
1 1979 6 24 3 4 4 4 4 3 4 4 2 3 4 4 6 0 0 6 2 4 4 4 3 0 0 3 3 3 4 3 2 4 3? 4 3 4 3 4 4 4 4 3 3 4 4 4 4 2 1 1 2 15 4 4 15 0 1 2 1980 8 4 4 4 4 4 3 4 2 4 4 2 4 6 0 0 6 4 2 4 1 2 2 1 4 4 4 2 3 3 3 4 3 4 4
More informationI? 3 1 3 1.1?................................. 3 1.2?............................... 3 1.3!................................... 3 2 4 2.1........................................ 4 2.2.......................................
More information1 (1) (2)
1 2 (1) (2) (3) 3-78 - 1 (1) (2) - 79 - i) ii) iii) (3) (4) (5) (6) - 80 - (7) (8) (9) (10) 2 (1) (2) (3) (4) i) - 81 - ii) (a) (b) 3 (1) (2) - 82 - - 83 - - 84 - - 85 - - 86 - (1) (2) (3) (4) (5) (6)
More informationC. R. McKenzie 2003 3 2004 ( 38 ) 2 2004 2005 (Keio Household Panel Survey) control group treatment group 1
KEIO UNIVERSITY MARKET QUALITY RESEARCH PROJECT (A 21 st Century Center of Excellence Project) DP2005-020 * C. R. McKenzie** 2003 3 2004 ( 38 ) 2 2004 2005 (Keio Household Panel Survey) control group treatment
More informationOECD Benartzi and Thaler Brown et al. Mottla and Utkus Rooiji et al. Atkinson et al. MacFarland et al. Elton et al. Tang et al. Benartzi and Thaler Br
IFRS. OECD Benartzi and Thaler Brown et al. Mottla and Utkus Rooiji et al. Atkinson et al. MacFarland et al. Elton et al. Tang et al. Benartzi and Thaler Brown et al. /n Benartzi and Thaler n /n Benartzi
More information山形大学紀要
x t IID t = b b x t t x t t = b t- AR ARMA IID AR ARMAMA TAR ARCHGARCH TARThreshold Auto Regressive Model TARTongTongLim y y X t y Self Exciting Threshold Auto Regressive, SETAR SETARTAR TsayGewekeTerui
More information1. 2 Blank and Winnick (1953) 1 Smith (1974) Shilling et al. (1987) Shilling et al. (1987) Frew and Jud (1988) James Shilling Voith (1992) (Shilling e
Estimation of the Natural Vacancy Rate and it s Instability: Evidence from the Tokyo Office Market * ** *** Sho Kuroda*, Morito Tsutsumi**, Toyokazu Imazeki*** * ** *** rent adjustment mechanismnatural
More informationIMES DISCUSSION PAPER SERIES Discuss ssion Paper No. 98-J-2 INSTITUTE FOR MONETARY AND ECONOMIC STUDIES BANK OF JAPAN 100-8630 203 IMES Discuss ssion Paper Series 98-J-2 1998 1 VaRVWAP E-mail: ohsawa@boj.co.uk
More information第3章.DOC
000 Ben-Akiva and Lerman, 1985 1996 1996 4 1997 Banister, 1978; Verplanken et al., 1998 1 5 1996 3 () (I n ) 1 18 I n n P n (1) P n ( 1) = exp exp ( Vn 1 ) I n 1 ( V ) + exp µ ln exp ( V ) n1 + i= ni (3.1)
More informationNetcommunity SYSTEM αNXⅡ typeS/typeM 取扱説明書
2 3 4 5 6 7 1 2 3 4 5 6 8 3 3-38 1 2 3 4 5 9 1 2 3 10 4 5 11 6 12 1 1-2 1 1-3 1 1-4 1 1-5 1 micro SD 1-6 1 1-7 1 1 1-8 1 1-9 1 100 10 TEN 1 1-10 1 1-11 1 1-12 1 1-13 1 1-14 1 1 2 7 8 9 1 3 4 5 6 1-15 1
More information10:30 12:00 P.G. vs vs vs 2
1 10:30 12:00 P.G. vs vs vs 2 LOGIT PROBIT TOBIT mean median mode CV 3 4 5 0.5 1000 6 45 7 P(A B) = P(A) + P(B) - P(A B) P(B A)=P(A B)/P(A) P(A B)=P(B A) P(A) P(A B) P(A) P(B A) P(B) P(A B) P(A) P(B) P(B
More information本 ディスカッションペーパーの 内 容 や 意 見 は 全 て 執 筆 者 の 個 人 的 見 解 であり 金 融 庁 あるいは 金 融 研 究 センターの 公 式 見 解 を 示 すものではありません
FSA Institute Discussion Paper Series エージェントシミュレーションを 用 いた 価 格 規 制 と ネイキッド ショート セ リングの 禁 止 の 有 効 性 の 検 証 大 井 朋 子 DP 2012-5 2012 年 12 月 金 融 庁 金 融 研 究 センター Financial Research Center (FSA Institute) Financial
More informationリカレンスプロット : 時系列の視覚化を越えて (マクロ経済動学の非線形数理)
1768 2011 150-162 150 : Recurrence plots: Beyond visualization of time series Yoshito Hirata Institute of Industrial Science, The University of Tokyo voshito@sat. t.u\cdot tokvo.ac.ip 1 1. 1987 (Eckmann
More information2 I- I- (1) 2 I- (2) 2 I- 1 [18] I- I-. 1 I- I- Jensen [11] I- FF 3 I- FF 3 2 2.1 CAPM n ( i = 1,..., n) M t R i,t, i = 1,..., n R M,t ( ) R i,t = r i
1 Idiosyncratic,, Idiosyncratic (I- ) I- 1 I- I- Jensen I- Fama-French 3 I- Fama-French 3 1 Fama-French (FF) 3 [6] (Capital Asset Pricing Model; CAPM [12, 15]) CAPM ( [2, 10, 14, 16]) [18] Idiosyncratic
More information( β K ) p β W W p β K K aβ β W W β β K K ) 1/(βW +β K ) 3 ln C =lnα + 1 β W + β K ln Q (3) 1/(β W + β K ) ( β W + β K ) 4 ( ) ( ) (1998 2 1 3 ) ( 1998
3 1 1993-1995 ( Cobb-Douglas ) (1998 2 3 ) ( ) 17 (1998 2 1 ) 1 Christensen, Jorgensonand Lau (1973) 1983 ( ) 2 W = K = β W,β K > 0 Q = aw βw K βk (1) C = αq 1/(βW +βk) (2) 10 ( (A) (A03) ) ( ) ( ) 1 2
More information1 Nelson-Siegel Nelson and Siegel(1987) 3 Nelson-Siegel 3 Nelson-Siegel 2 3 Nelson-Siegel 2 Nelson-Siegel Litterman and Scheinkman(199
Nelson-Siegel Nelson-Siegel 1992 2007 15 1 Nelson and Siegel(1987) 2 FF VAR 1996 FF B) 1 Nelson-Siegel 15 90 1 Nelson and Siegel(1987) 3 Nelson-Siegel 3 Nelson-Siegel 2 3 Nelson-Siegel 2 Nelson-Siegel
More informationDEIM Forum 2009 C8-4 QA NTT QA QA QA 2 QA Abstract Questions Recomme
DEIM Forum 2009 C8-4 QA NTT 239 0847 1 1 E-mail: {kabutoya.yutaka,kawashima.harumi,fujimura.ko}@lab.ntt.co.jp QA QA QA 2 QA Abstract Questions Recommendation Based on Evolution Patterns of a QA Community
More information2 / 24
2017 11 9 1 / 24 2 / 24 Solow, 1957 total factor productivity; TFP 5% 経済成長率の要因分解 4% 3% 2.68% 2.51% 2% 1% 0% 1.63% 1.50% 0.34% 0.42% 0.55% 0.97% 1.14% 0.86% 0.13% -0.59% -0.59% -0.09% 0.01% -1% 1970-80
More information相互相関を考慮した非線形予測モデルに基づく 札幌市気温と北海道大学構内電力需要の同時推定
Title 相互相関を考慮した非線形予測モデルに基づく札幌市気温と北海道大学構内電力需要の同時推定 Author(s) 岩山, 浩将 Issue Date 212-3-22 Doc URL http://hdl.handle.net/2115/52278 Type theses (bachelor) File Information Iwayama_BachelorThesis211.pdf Instructions
More information1 1 3 1.1 (Frequecy Tabulatios)................................ 3 1........................................ 8 1.3.....................................
1 1 3 1.1 (Frequecy Tabulatios)................................ 3 1........................................ 8 1.3........................................... 1 17.1................................................
More information平成○○年度知能システム科学専攻修士論文
A Realization of Robust Agents in an Agent-based Virtual Market Makio Yamashige 3 7 A Realization of Robust Agents in an Agent-based Virtual Market Makio Yamashige Abstract There are many people who try
More informationコピー ~ 5-森保.txt
NAOSITE: Nagasaki University's Ac Title 先物市場における高速取引が現物市場の流動性に与える影響 Author(s) 森保, 洋 Citation 経営と経済, 95(3-4), pp.95-115; 2016 Issue Date 2016-03-25 URL http://hdl.handle.net/10069/36324 Right This document
More information