L 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
|
|
- しげじろう うづき
- 5 years ago
- Views:
Transcription
1 J-REIT 1- MM CAPM 1-3 [001] [1997] [003] [001] [1999] [003] 1-4 0
2 L 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
3 /10 00 DCF Sein Sollen Sein Sein
4 Sollen 3 Sollen Sein J-REIT J-REIT GS [1975 ] P [197 ] P P9~P10 3
5 J-REIT REIT H17 7 REIT 1.5 4
6 * * * * Z = E 0[ Z ] + E0[ Z ] + + E0[ Z ] ( Z = Z/ R ) 0 1 T V V = E[ m Z] ( m : Z : f 5
7 8 1 α UC ' t Ut () = Ct Mt+ 1 = 1 α UC ' t C t+ 1 mt+ 1 = α log Ct Dt T 1 Pt = Dt exp( α s µ c) exp s( µ d + σd ασ c ) s= [004] JAREFE004 6
8 3-5-4 β SML 9 4 µ CML µ SML µ C C B µ M M µ C µ µ = µ + β ( µ µ ) i f i M f B C µ A A µ A A µ f µ f σ σ β σ M A C σ A βc β Fama, French[199] The Cross-section of Expected Stock Returns NYSE β 7
9
10 y = + 5 ln ln β1 ln 0.035β t(4.553) (5.089) (3.6431) p(0.0000) (0.009) (0.0066) R R DW AIC = = 0.88 =.055 = y = β = β = R Y R = Y g 11 [1991] [00] GWR 9
11 4-3- (A) (B) (C) (D) (E) DSCR (A)(D) (A) (B) (C) (C) Y g ε R a V = R g R = g = a = 10
12 4-3-4 DCF DCF a RP a + + n k n+ 1 PV = + k n RP = ak k = 1 (1 r) (1 r) Rt r RP DCF DCF DCF DDCF DCF DDCF ν 1- ( ) ( ) k CF t 1 ν RP CF t 1 ν V 0 = + RP = t k t= 1 1+ r + r 1+ r + r Rt ( ) f p f p ( ) 1 11
13 Crystal Ball 13 Var REIT DCF β * V * ( ) Vland = V K V : β : * V 14 * β β > V K ( ) K NPV Decisioneering, Inc EXCEL [004] 15 1
14 R = Rm Wm + Re We 16 MM LTV 16 [003] 17 [004] BBB
15 REIT 5- RETI NOI y = β β β3 t(30.9) (5.4) (7.3) (4.) p(0.00) (0.00) (0.00) (0001) R R DW AIC = 0.91 = = 1.87 = 89.3 y = NOI β1 = β = NOI β =
16 GDP GDP q R r = i π g q= R/( r g) [1995] 15
17 REIT 5-5 TOPIX [004] PVR REPORT [000]
18 [1990] 17
19 (A) (B) 6. 18
20 [1997] 19
21 A g r ag 1 * ** Pt () = + C+ + + ( z z), z z Capzza & Sick r r ar r 8 1 ( z * z ) r r ag ( * )/ C e a z z r ar r ag ar g r A g a ( z * z )/ r e r r * ** z () t = z () t 6 [1976] 7 8 Capzza & Sick [1994] The Risk Structure of Land Markets JOUNAL OF URBAN ECONOMICS 35 0
22 A g r ag 1 * Pt () = + C+ + + ( z z) r r ar r A g = C = = r r r ag 1 * = ( z z ) = ar r * * a A g a( z z )/ r r ag a( z z )/ r P ( z) = + e + e r r ar Cap rate R P i = D = ( R, Economy) = S D θ Z S P = ( ) f C C S = ( S = C δ S) δ 17 DiPasquale & Wheaton 9 [001] David Geltner Norman G. Miler [001] Commercial Real Estate Analysis and Investments P7P37. 1
23 4 i REIT Pt = f( S) + F( Pt+ 1) ( : S f( S) F( Pt + 1) [001] P [00] 8
24 [00] 33 S Cole, R., D.Guilkey, and M Miles. Toward an Assessment of the Reliability of Commercial Appraisals The Appraisal Journal, Vol. LIV, July Sein Sollen 3
25 Geltner n n n LnP = α + β x + β dx + δ d + ε t it it it it t t t i= 1 i= 1 i= 1 βit dxit : dt : δ : ε : t t 37 David Geltner [1989] Estimating Real Estate s Systematic Risk From Aggregate Level Appraisal-Based Returns AREUEA Journal, Vol.17.NO4, 38 [001] 4
26 7... t t P BP ν P = BP+ ν P 39 T T FP [ 1, P] = α0 + α1p T EPP [ ] E[ ( P+ ν ) P] EP [ ] α1 = = =, α 0 = 0 T EP [ ] EP [ + ν] EP [ ] + E[ ν ] Geltner * r ω t r * t = ω0rt + ω1rt 1+ ωrt +, CCAPM * COV[ rt, It] = ω0cov[ rt, It] + ω1cov[ rt 1, It] +, COV [ rt k, It] = 0, ( all k > 0) * COV[ rt, It] = ω0cov [ rt, It] CAPM ( β i) β () i 1 β * () i = * β () i ω i REIT 1 39 David Geltner 40 HOON CHO[003] Fisher-Geltner-Webb IKOMA-MTB 300% 5
27 1 8. REIT Gerald R. Brown 4 UK Office, Retail, Industry 41 Alastair Adair, Mary Lou Downnie [1998] EUROPEAN VALUATION PRACTICE THEORY AND TECHNIQUES 4 Gerald R. Brown & George A.Matysiak [000] Real Estate Investment A Capital Market Approach 6
28 8-3 Gerald R. Brown r = α + β r + ε x i i i m i Er [ ] xα xβ r n n n p i i i j ij i= 1 i= 1 j= 1 i j Var[ r ] = x σ + x x σ n p i i i i m i= 1 i= 1 n = i n n n n n 1 n = i + jk = + jk j= 1 n j= 1 k= 1 n n n n k j σ σ σ σ σ σ n σ σ n RR n = β σ σ σ m e R = 1 R = n βσm 1 σ e = 1 n ρ R 1 n = 1 (1 R ) ρ 8-4 Gerald R. Brown σ σ n 7
29 18 IPD (19 ) Marvin L Wolverton [1998] 8
30 Brown 8-6 REIT 0 REIT 11.5% IT 9
31 9-1-3 REIT REIT REIT REIT 9--3 REIT REIT 43 Ko Wang and Marvin L. Wolverton[00] 30
32 REIT NOI NCF MAI David C. Ling[1999] REIT
33 EXCEL EXCEL E-views EXCEL 3
34 11. [000] [004] JAREFE004 [000] [1964] [003] 003 [00] NO44. [1997] [003] [005 ] [001] [001] [004] Vol.56 NO6 [1969] [1990] [00] [004] Evaluation NO1 [1966] [001] [005] H13 6 H14 6 [003] [004]J-REIT puzzle Evaluation NO1 [004] [004] NO.04-J-7 [004] [1964] [001] 33
35 [1991] [001] [003] [1995] [000] [1998] [1995] [1973] [004] NO.04-J-15 [00] [1995] [1990] [000] [1990] [1995]80 [003] [00] Evaluation NO7 [003] JAREFE Vol.1 [003] [1994] [1999] [199] [1995] [000] [000] [197] [1976] 34
36 [199] [1995] Appraisal Institute [001] The Appraisal of Real Estate 1edition Cole,R., D.Guilkey, and M Miles. [1986] Toward an Assessment of the Reliability of Commercial Appraisals The Appraisal Journal, Vol. LIV, July Dennis R.Cappoza [1994] The Risk Structure of Land Market Journal of Urban Economics David Geltner Norman G. Miler [001] Commercial Real Estate Analysis and Investments P43P13 P751P770 David Geltner [1989] Estimating Real Estate s Systematic Risk from Aggregate Level Appraisal-Based Returns AREUEA journal Vol.17, NO4 David C. Ling and Andy Naranjo [1999] The Integration of Commercial Real Estate Markets and Stock Markets REAL ESTATE ECONOMICS V7,3 Gerald R. Brown & George A.Matysiak[000] Real Estate Investment A Capital Market Approach P09370ISBN HOON CHO YUICHIRO KAWAGUCHI JAMES D.SHILLING [003] Unsmoothing Commercial Real Property Returns: A Revision to Fisher-Geltner-Webb s Unsmoothing Methodology Journal of Real Estate Finance and Economics NO003;7,3 Ko Wang and Marvin L. Wolverton[00] REAL ESTATE VALUATION THEORY Appraisal Institute American Real Estate Society ISBN Marvin L Wolverton Ping Cheng [1998] Real Estate Portfolio Risk Reduction through Intracity Diversification journal of real estate portfolio management 1998;4,1 35
37
38 1- DDCF k CF ( ) t 1 ν RP CF t ( 1 ν) V 0 = + RP = t t ( ) t= 1 1+ rf + rp ( 1+ rf + r ) R p t 37
39 1-3 38
40 1-4 39
1 CAPM: I-,,, I- ( ) 1 I- I- I- ( CAPM) I- CAPM I- 1 I- Jensen Fama-French 3 I- Fama-French 3 I- Fama-MacBeth I- SMB-FL, HML-FL Fama-MacBeth 1 Fama-Fr
1 CAPM: I-,,, I- ( ) 1 I- I- I- ( CAPM) I- CAPM I- 1 I- Jensen Fama-French 3 I- Fama-French 3 I- Fama-MacBeth I- SMB-FL, HML-FL Fama-MacBeth 1 Fama-French (FF) 3 [5] (Capital Asset Pricing Model; CAPM
More informationVol.8 No (July 2015) 2/ [3] stratification / *1 2 J-REIT *2 *1 *2 J-REIT % J-REIT J-REIT 6 J-REIT J-REIT 10 J-REIT *3 J-
Vol.8 No.2 1 9 (July 2015) 1,a) 2 3 2012 1 5 2012 3 24, 2013 12 12 2 1 2 A Factor Model for Measuring Market Risk in Real Estate Investment Hiroshi Ishijima 1,a) Akira Maeda 2 Tomohiko Taniyama 3 Received:
More informationI- Fama-French 3, Idiosyncratic (I- ) I- ( ) 1 I- I- I- 1 I- I- Jensen Fama-French 3 SMB-FL, HML-FL I- Fama-French 3 I- Fama-MacBeth Fama-MacBeth I- S
I- Fama-French 3, Idiosyncratic (I- ) I- ( ) 1 I- I- I- 1 I- I- Jensen Fama-French 3 SMB-FL, HML-FL I- Fama-French 3 I- Fama-MacBeth Fama-MacBeth I- SMB-FL, HML-FL Fama-MacBeth 2, 3, 5 I- HML-FL 1 Fama-French
More information1 (Berry,1975) 2-6 p (S πr 2 )p πr 2 p 2πRγ p p = 2γ R (2.5).1-1 : : : : ( ).2 α, β α, β () X S = X X α X β (.1) 1 2
2005 9/8-11 2 2.2 ( 2-5) γ ( ) γ cos θ 2πr πρhr 2 g h = 2γ cos θ ρgr (2.1) γ = ρgrh (2.2) 2 cos θ θ cos θ = 1 (2.2) γ = 1 ρgrh (2.) 2 2. p p ρgh p ( ) p p = p ρgh (2.) h p p = 2γ r 1 1 (Berry,1975) 2-6
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 informationy = x x R = 0. 9, R = σ $ = y x w = x y x x w = x y α ε = + β + x x x y α ε = + β + γ x + x x x x' = / x y' = y/ x y' =
y x = α + β + ε =,, ε V( ε) = E( ε ) = σ α $ $ β w ( 0) σ = w σ σ y α x ε = + β + w w w w ε / w ( w y x α β ) = α$ $ W = yw βwxw $β = W ( W) ( W)( W) w x x w x x y y = = x W y W x y x y xw = y W = w w
More informationII III II 1 III ( ) [2] [3] [1] 1 1:
2015 4 16 1. II III II 1 III () [2] [3] 2013 11 18 [1] 1 1: [5] [6] () [7] [1] [1] 1998 4 2008 8 2014 8 6 [1] [1] 2 3 4 5 2. 2.1. t Dt L DF t A t (2.1) A t = Dt L + Dt F (2.1) 3 2 1 2008 9 2008 8 2008
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 information64 3 g=9.85 m/s 2 g=9.791 m/s 2 36, km ( ) 1 () 2 () m/s : : a) b) kg/m kg/m k
63 3 Section 3.1 g 3.1 3.1: : 64 3 g=9.85 m/s 2 g=9.791 m/s 2 36, km ( ) 1 () 2 () 3 9.8 m/s 2 3.2 3.2: : a) b) 5 15 4 1 1. 1 3 14. 1 3 kg/m 3 2 3.3 1 3 5.8 1 3 kg/m 3 3 2.65 1 3 kg/m 3 4 6 m 3.1. 65 5
More information商品流動性リスクの計量化に関する一考察(その2)―内生的流動性リスクを考慮したストレス・テスト―
E-mail: shigeru_yoshifuji@btm.co.jp E-mail: fuminobu_otake@btm.co.jp Bangia et al. G Bangia et al. exogenous liquidity risk endogenous liquidity risk et al LTCMLong Term Capital Management Fed G G T
More information19 σ = P/A o σ B Maximum tensile strength σ % 0.2% proof stress σ EL Elastic limit Work hardening coefficient failure necking σ PL Proportional
19 σ = P/A o σ B Maximum tensile strength σ 0. 0.% 0.% proof stress σ EL Elastic limit Work hardening coefficient failure necking σ PL Proportional limit ε p = 0.% ε e = σ 0. /E plastic strain ε = ε e
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 informationPowerPoint プレゼンテーション
183 04 022 J-REIT J-REIT Debt Equity 90 Equity Equity Equity Equity Equity Equity J-REIT 20019 28 2600 28000 J-REIT The National Council of Real Estate Investment Fiduciaries NOI BMV + 0.5* CI
More informationPart () () Γ Part ,
Contents a 6 6 6 6 6 6 6 7 7. 8.. 8.. 8.3. 8 Part. 9. 9.. 9.. 3. 3.. 3.. 3 4. 5 4.. 5 4.. 9 4.3. 3 Part. 6 5. () 6 5.. () 7 5.. 9 5.3. Γ 3 6. 3 6.. 3 6.. 3 6.3. 33 Part 3. 34 7. 34 7.. 34 7.. 34 8. 35
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 informationCVaR
CVaR 20 4 24 3 24 1 31 ,.,.,. Markowitz,., (Value-at-Risk, VaR) (Conditional Value-at-Risk, CVaR). VaR, CVaR VaR. CVaR, CVaR. CVaR,,.,.,,,.,,. 1 5 2 VaR CVaR 6 2.1................................................
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 information80024622 1996 Science for Open and Environmental Systems 80024622 KIKUCHI, Atsushi Optimal Asset Allocation with Real Estate The institutional investors such as life insurance companies, trust banks, and
More informationThe Physics of Atmospheres CAPTER :
The Physics of Atmospheres CAPTER 4 1 4 2 41 : 2 42 14 43 17 44 25 45 27 46 3 47 31 48 32 49 34 41 35 411 36 maintex 23/11/28 The Physics of Atmospheres CAPTER 4 2 4 41 : 2 1 σ 2 (21) (22) k I = I exp(
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 information( ) ,
II 2007 4 0. 0 1 0 2 ( ) 0 3 1 2 3 4, - 5 6 7 1 1 1 1 1) 2) 3) 4) ( ) () H 2.79 10 10 He 2.72 10 9 C 1.01 10 7 N 3.13 10 6 O 2.38 10 7 Ne 3.44 10 6 Mg 1.076 10 6 Si 1 10 6 S 5.15 10 5 Ar 1.01 10 5 Fe 9.00
More information3/4/8:9 { } { } β β β α β α β β
α β : α β β α β α, [ ] [ ] V, [ ] α α β [ ] β 3/4/8:9 3/4/8:9 { } { } β β β α β α β β [] β [] β β β β α ( ( ( ( ( ( [ ] [ ] [ β ] [ α β β ] [ α ( β β ] [ α] [ ( β β ] [] α [ β β ] ( / α α [ β β ] [ ] 3
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 informationMicrosoft Word - 11問題表紙(選択).docx
A B A.70g/cm 3 B.74g/cm 3 B C 70at% %A C B at% 80at% %B 350 C γ δ y=00 x-y ρ l S ρ C p k C p ρ C p T ρ l t l S S ξ S t = ( k T ) ξ ( ) S = ( k T) ( ) t y ξ S ξ / t S v T T / t = v T / y 00 x v S dy dx
More information財政赤字の経済分析:中長期的視点からの考察
1998 1999 1998 1999 10 10 1999 30 (1982, 1996) (1997) (1977) (1990) (1996) (1997) (1996) Ihori, Doi, and Kondo (1999) (1982) (1984) (1987) (1993) (1997) (1998) CAPM 1980 (time inconsistency) Persson, Persson
More information, 1.,,,.,., (Lin, 1955).,.,.,.,. f, 2,. main.tex 2011/08/13( )
81 4 2 4.1, 1.,,,.,., (Lin, 1955).,.,.,.,. f, 2,. 82 4.2. ζ t + V (ζ + βy) = 0 (4.2.1), V = 0 (4.2.2). (4.2.1), (3.3.66) R 1 Φ / Z, Γ., F 1 ( 3.2 ). 7,., ( )., (4.2.1) 500 hpa., 500 hpa (4.2.1) 1949,.,
More information01-._..
Journal of the Faculty of Management and Information Systems, Prefectural University of Hiroshima 2014 No.6 pp.43 56 43 The risk measure for resilience in the inventory control system Nobuyuki UENO, Yu
More information現代物理化学 1-1(4)16.ppt
(pdf) pdf pdf http://www1.doshisha.ac.jp/~bukka/lecture/index.html http://www.doshisha.ac.jp/ Duet -1-1-1 2-a. 1-1-2 EU E = K E + P E + U ΔE K E = 0P E ΔE = ΔU U U = εn ΔU ΔU = Q + W, du = d 'Q + d 'W
More information医系の統計入門第 2 版 サンプルページ この本の定価 判型などは, 以下の URL からご覧いただけます. このサンプルページの内容は, 第 2 版 1 刷発行時のものです.
医系の統計入門第 2 版 サンプルページ この本の定価 判型などは, 以下の URL からご覧いただけます. http://www.morikita.co.jp/books/mid/009192 このサンプルページの内容は, 第 2 版 1 刷発行時のものです. i 2 t 1. 2. 3 2 3. 6 4. 7 5. n 2 ν 6. 2 7. 2003 ii 2 2013 10 iii 1987
More informationuntitled
Horioka Nakagawa and Oshima u ( c ) t+ 1 E β (1 + r ) 1 = t i+ 1 u ( c ) t 0 β c t y t uc ( t ) E () t r t c E β t ct γ ( + r ) 1 0 t+ 1 1 = t+ 1 ξ ct + β ct γ c t + 1 1+ r ) E β t + 1 t ct (1
More informationNo δs δs = r + δr r = δr (3) δs δs = r r = δr + u(r + δr, t) u(r, t) (4) δr = (δx, δy, δz) u i (r + δr, t) u i (r, t) = u i x j δx j (5) δs 2
No.2 1 2 2 δs δs = r + δr r = δr (3) δs δs = r r = δr + u(r + δr, t) u(r, t) (4) δr = (δx, δy, δz) u i (r + δr, t) u i (r, t) = u i δx j (5) δs 2 = δx i δx i + 2 u i δx i δx j = δs 2 + 2s ij δx i δx j
More informationH 0 H = H 0 + V (t), V (t) = gµ B S α qb e e iωt i t Ψ(t) = [H 0 + V (t)]ψ(t) Φ(t) Ψ(t) = e ih0t Φ(t) H 0 e ih0t Φ(t) + ie ih0t t Φ(t) = [
3 3. 3.. H H = H + V (t), V (t) = gµ B α B e e iωt i t Ψ(t) = [H + V (t)]ψ(t) Φ(t) Ψ(t) = e iht Φ(t) H e iht Φ(t) + ie iht t Φ(t) = [H + V (t)]e iht Φ(t) Φ(t) i t Φ(t) = V H(t)Φ(t), V H (t) = e iht V (t)e
More information研修コーナー
l l l l l l l l l l l α α β l µ l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l
More informationBIS CDO CDO CDO CDO Cifuentes and O Connor[1] Finger[6] Li[8] Duffie and Garleânu[4] CDO Merton[9] CDO 1 CDO CDO CDS CDO three jump model Longstaff an
CDO 2010 5 18 CDO(Collateralized Debt Obligation) Duffie and Garleânu[4] CDO CDS(Credit Default Swap) Duffie and Garleânu[4] 4 CDO CDS CDO CDS CDO 2007 CDO CDO CDS 1 1.1 2007 2008 9 15 ( ) CDO CDO 80 E-mail:taiji.ohka@gmail.com
More information変 位 変位とは 物体中のある点が変形後に 別の点に異動したときの位置の変化で あり ベクトル量である 変位には 物体の変形の他に剛体運動 剛体変位 が含まれている 剛体変位 P(x, y, z) 平行移動と回転 P! (x + u, y + v, z + w) Q(x + d x, y + dy,
変 位 変位とは 物体中のある点が変形後に 別の点に異動したときの位置の変化で あり ベクトル量である 変位には 物体の変形の他に剛体運動 剛体変位 が含まれている 剛体変位 P(x, y, z) 平行移動と回転 P! (x + u, y + v, z + w) Q(x + d x, y + dy, z + dz) Q! (x + d x + u + du, y + dy + v + dv, z +
More information(Compton Scattering) Beaming 1 exp [i (k x ωt)] k λ k = 2π/λ ω = 2πν k = ω/c k x ωt ( ω ) k α c, k k x ωt η αβ k α x β diag( + ++) x β = (ct, x) O O x
Compton Scattering Beaming exp [i k x ωt] k λ k π/λ ω πν k ω/c k x ωt ω k α c, k k x ωt η αβ k α x β diag + ++ x β ct, x O O x O O v k α k α β, γ k γ k βk, k γ k + βk k γ k k, k γ k + βk 3 k k 4 k 3 k
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 informationuntitled
Cross [1973]French [1980] Rogalsk [1984]Arel [1990]Arel [1987]Rozeff and Knney [1974]seasonaltycalendar structure [2004] 12 Half-Year Effect [2004] [1983] [1990]ChanHamao and Lakonshok [1991] Fama and
More informationuntitled
2 book conference 1990 2003 14 Repeated Cross-Section Data 1 M1,M2 M1 Sekine(1998) Repeated Cross-Section Data 1 1. (1989), Yoshida and Rasche(1990), Rasche(1990), 19921997, Fujiki and Mulligan(1996),
More informationTOP URL 1
TOP URL http://amonphys.web.fc.com/ 1 19 3 19.1................... 3 19.............................. 4 19.3............................... 6 19.4.............................. 8 19.5.............................
More information(1.2) T D = 0 T = D = 30 kn 1.2 (1.4) 2F W = 0 F = W/2 = 300 kn/2 = 150 kn 1.3 (1.9) R = W 1 + W 2 = = 1100 N. (1.9) W 2 b W 1 a = 0
1 1 1.1 1.) T D = T = D = kn 1. 1.4) F W = F = W/ = kn/ = 15 kn 1. 1.9) R = W 1 + W = 6 + 5 = 11 N. 1.9) W b W 1 a = a = W /W 1 )b = 5/6) = 5 cm 1.4 AB AC P 1, P x, y x, y y x 1.4.) P sin 6 + P 1 sin 45
More information³ÎΨÏÀ
2017 12 12 Makoto Nakashima 2017 12 12 1 / 22 2.1. C, D π- C, D. A 1, A 2 C A 1 A 2 C A 3, A 4 D A 1 A 2 D Makoto Nakashima 2017 12 12 2 / 22 . (,, L p - ). Makoto Nakashima 2017 12 12 3 / 22 . (,, L p
More informationchap9.dvi
9 AR (i) (ii) MA (iii) (iv) (v) 9.1 2 1 AR 1 9.1.1 S S y j = (α i + β i j) D ij + η j, η j = ρ S η j S + ε j (j =1,,T) (1) i=1 {ε j } i.i.d(,σ 2 ) η j (j ) D ij j i S 1 S =1 D ij =1 S>1 S =4 (1) y j =
More informationV(x) m e V 0 cos x π x π V(x) = x < π, x > π V 0 (i) x = 0 (V(x) V 0 (1 x 2 /2)) n n d 2 f dξ 2ξ d f 2 dξ + 2n f = 0 H n (ξ) (ii) H
199 1 1 199 1 1. Vx) m e V cos x π x π Vx) = x < π, x > π V i) x = Vx) V 1 x /)) n n d f dξ ξ d f dξ + n f = H n ξ) ii) H n ξ) = 1) n expξ ) dn dξ n exp ξ )) H n ξ)h m ξ) exp ξ )dξ = π n n!δ n,m x = Vx)
More information() ( ) ( ) (1996) (1997) (1997) EaR (Earning at Risk) VaR ( ) ( ) Memmel (214) () 2 (214) 2
1 (Basel Committee on Banking Supervision, BCBS) (BCBS(24), BCBS(215) ) *1 ( ) ( (1997) (213a,b) ) 2 *1 (214) 1 () ( ) ( ) (1996) (1997) (1997) EaR (Earning at Risk) VaR 2 1 1 ( ) ( ) Memmel (214) () 2
More informationuntitled
1 3 23 4 ... 1 2... 3 3... 6 4... 10 4.1... 10 4.2... 14 4.2.1... 14 4.2.2... 16 4.2.3... 17 4.2.4 WASEDA / REINS... 19 4.3... 22 5... 25 5.1... 25 5.2... 26 5.3 S&P/... 29 5.4... 32 5.5... 32 5.6... 33
More information,. Black-Scholes u t t, x c u 0 t, x x u t t, x c u t, x x u t t, x + σ x u t, x + rx ut, x rux, t 0 x x,,.,. Step 3, 7,,, Step 6., Step 4,. Step 5,,.
9 α ν β Ξ ξ Γ γ o δ Π π ε ρ ζ Σ σ η τ Θ θ Υ υ ι Φ φ κ χ Λ λ Ψ ψ µ Ω ω Def, Prop, Th, Lem, Note, Remark, Ex,, Proof, R, N, Q, C [a, b {x R : a x b} : a, b {x R : a < x < b} : [a, b {x R : a x < b} : a,
More informationマクロ経済スライド下における積立金運用でのリスク
2004 2005 ALM 2030 2050 50 JEL Classification: H55, G11 Key words: 2 H16 007 102-0073 4-1-7 FAX: 03-5211-1082, E-mail: kitamura@nli-research.co.jp E-mail: nakasima@nli-resaech.co.jp E-mail: usuki@nli-research.co.jp
More informationchap10.dvi
. q {y j } I( ( L y j =Δy j = u j = C l ε j l = C(L ε j, {ε j } i.i.d.(,i q ( l= y O p ( {u j } q {C l } A l C l
More informationuntitled
18 1 2,000,000 2,000,000 2007 2 2 2008 3 31 (1) 6 JCOSSAR 2007pp.57-642007.6. LCC (1) (2) 2 10mm 1020 14 12 10 8 6 4 40,50,60 2 0 1998 27.5 1995 1960 40 1) 2) 3) LCC LCC LCC 1 1) Vol.42No.5pp.29-322004.5.
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 informationx E E E e i ω = t + ikx 0 k λ λ 2π k 2π/λ k ω/v v n v c/n k = nω c c ω/2π λ k 2πn/λ 2π/(λ/n) κ n n κ N n iκ k = Nω c iωt + inωx c iωt + i( n+ iκ ) ωx
x E E E e i ω t + ikx k λ λ π k π/λ k ω/v v n v c/n k nω c c ω/π λ k πn/λ π/(λ/n) κ n n κ N n iκ k Nω c iωt + inωx c iωt + i( n+ iκ ) ωx c κω x c iω ( t nx c) E E e E e E e e κ e ωκx/c e iω(t nx/c) I I
More informationshuron.dvi
01M3065 1 4 1.1........................... 4 1.2........................ 5 1.3........................ 6 2 8 2.1.......................... 8 2.2....................... 9 3 13 3.1.............................
More informationTOP URL 1
TOP URL http://amonphys.web.fc.com/ 3.............................. 3.............................. 4.3 4................... 5.4........................ 6.5........................ 8.6...........................7
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 information[ ] 0.1 lim x 0 e 3x 1 x IC ( 11) ( s114901) 0.2 (1) y = e 2x (x 2 + 1) (2) y = x/(x 2 + 1) 0.3 dx (1) 1 4x 2 (2) e x sin 2xdx (3) sin 2 xdx ( 11) ( s
[ ]. lim e 3 IC ) s49). y = e + ) ) y = / + ).3 d 4 ) e sin d 3) sin d ) s49) s493).4 z = y z z y s494).5 + y = 4 =.6 s495) dy = 3e ) d dy d = y s496).7 lim ) lim e s49).8 y = e sin ) y = sin e 3) y =
More informationp = mv p x > h/4π λ = h p m v Ψ 2 Ψ
II p = mv p x > h/4π λ = h p m v Ψ 2 Ψ Ψ Ψ 2 0 x P'(x) m d 2 x = mω 2 x = kx = F(x) dt 2 x = cos(ωt + φ) mω 2 = k ω = m k v = dx = -ωsin(ωt + φ) dt = d 2 x dt 2 0 y v θ P(x,y) θ = ωt + φ ν = ω [Hz] 2π
More informationnm (T = K, p = kP a (1atm( )), 1bar = 10 5 P a = atm) 1 ( ) m / m
.1 1nm (T = 73.15K, p = 101.35kP a (1atm( )), 1bar = 10 5 P a = 0.9863atm) 1 ( ).413968 10 3 m 3 1 37. 1/3 3.34.414 10 3 m 3 6.0 10 3 = 3.7 (109 ) 3 (nm) 3 10 6 = 3.7 10 1 (nm) 3 = (3.34nm) 3 ( P = nrt,
More informationm dv = mg + kv2 dt m dv dt = mg k v v m dv dt = mg + kv2 α = mg k v = α 1 e rt 1 + e rt m dv dt = mg + kv2 dv mg + kv 2 = dt m dv α 2 + v 2 = k m dt d
m v = mg + kv m v = mg k v v m v = mg + kv α = mg k v = α e rt + e rt m v = mg + kv v mg + kv = m v α + v = k m v (v α (v + α = k m ˆ ( v α ˆ αk v = m v + α ln v α v + α = αk m t + C v α v + α = e αk m
More informationQMII_10.dvi
65 1 1.1 1.1.1 1.1 H H () = E (), (1.1) H ν () = E ν () ν (). (1.) () () = δ, (1.3) μ () ν () = δ(μ ν). (1.4) E E ν () E () H 1.1: H α(t) = c (t) () + dνc ν (t) ν (), (1.5) H () () + dν ν () ν () = 1 (1.6)
More information05Mar2001_tune.dvi
2001 3 5 COD 1 1.1 u d2 u + ku =0 (1) dt2 u = a exp(pt) (2) p = ± k (3) k>0k = ω 2 exp(±iωt) (4) k
More information1: 3.3 1/8000 1/ m m/s v = 2kT/m = 2RT/M k R 8.31 J/(K mole) M 18 g 1 5 a v t πa 2 vt kg (
1905 1 1.1 0.05 mm 1 µm 2 1 1 2004 21 2004 7 21 2005 web 2 [1, 2] 1 1: 3.3 1/8000 1/30 3 10 10 m 3 500 m/s 4 1 10 19 5 6 7 1.2 3 4 v = 2kT/m = 2RT/M k R 8.31 J/(K mole) M 18 g 1 5 a v t πa 2 vt 6 6 10
More information6.1 (P (P (P (P (P (P (, P (, P.
(011 30 7 0 ( ( 3 ( 010 1 (P.3 1 1.1 (P.4.................. 1 1. (P.4............... 1 (P.15.1 (P.16................. (P.0............3 (P.18 3.4 (P.3............... 4 3 (P.9 4 3.1 (P.30........... 4 3.
More informationII ( ) (7/31) II ( [ (3.4)] Navier Stokes [ (6/29)] Navier Stokes 3 [ (6/19)] Re
II 29 7 29-7-27 ( ) (7/31) II (http://www.damp.tottori-u.ac.jp/~ooshida/edu/fluid/) [ (3.4)] Navier Stokes [ (6/29)] Navier Stokes 3 [ (6/19)] Reynolds [ (4.6), (45.8)] [ p.186] Navier Stokes I Euler Navier
More information9. 05 L x P(x) P(0) P(x) u(x) u(x) (0 < = x < = L) P(x) E(x) A(x) P(L) f ( d EA du ) = 0 (9.) dx dx u(0) = 0 (9.2) E(L)A(L) du (L) = f (9.3) dx (9.) P
9 (Finite Element Method; FEM) 9. 9. P(0) P(x) u(x) (a) P(L) f P(0) P(x) (b) 9. P(L) 9. 05 L x P(x) P(0) P(x) u(x) u(x) (0 < = x < = L) P(x) E(x) A(x) P(L) f ( d EA du ) = 0 (9.) dx dx u(0) = 0 (9.2) E(L)A(L)
More information( ) Loewner SLE 13 February
( ) Loewner SLE 3 February 00 G. F. Lawler, Conformally Invariant Processes in the Plane, (American Mathematical Society, 005)., Summer School 009 (009 8 7-9 ) . d- (BES d ) d B t = (Bt, B t,, Bd t ) (d
More information5 5.1 E 1, E 2 N 1, N 2 E tot N tot E tot = E 1 + E 2, N tot = N 1 + N 2 S 1 (E 1, N 1 ), S 2 (E 2, N 2 ) E 1, E 2 S tot = S 1 + S 2 2 S 1 E 1 = S 2 E
5 5.1 E 1, E 2 N 1, N 2 E tot N tot E tot = E 1 + E 2, N tot = N 1 + N 2 S 1 (E 1, N 1 ), S 2 (E 2, N 2 ) E 1, E 2 S tot = S 1 + S 2 2 S 1 E 1 = S 2 E 2, S 1 N 1 = S 2 N 2 2 (chemical potential) µ S N
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 information5 1.2, 2, d a V a = M (1.2.1), M, a,,,,, Ω, V a V, V a = V + Ω r. (1.2.2), r i 1, i 2, i 3, i 1, i 2, i 3, A 2, A = 3 A n i n = n=1 da = 3 = n=1 3 n=1
4 1 1.1 ( ) 5 1.2, 2, d a V a = M (1.2.1), M, a,,,,, Ω, V a V, V a = V + Ω r. (1.2.2), r i 1, i 2, i 3, i 1, i 2, i 3, A 2, A = 3 A n i n = n=1 da = 3 = n=1 3 n=1 da n i n da n i n + 3 A ni n n=1 3 n=1
More informationaragciv54mac.dvi
2013 3 25 ( 4 ) Modigliani-Miller(1963) 1 1 1 IRR ( ) NPV IRR NPV IRR NPV IRR 100 EBIT 20 20% τ (1 τ) 20% CC tax = S V ρ S + B V (1 τ)ρ B (1) S B V ρ S CAPM 1 ρ B *1 (1) MM *2 ( ) MM MM ( ) MM (1 τ) 20%
More informationuntitled
- k k k = y. k = ky. y du dx = ε ux ( ) ux ( ) = ax+ b x u() = ; u( ) = AE u() = b= u () = a= ; a= d x du ε x = = = dx dx N = σ da = E ε da = EA ε A x A x x - σ x σ x = Eε x N = EAε x = EA = N = EA k =
More information研究シリーズ第40号
165 PEN WPI CPI WAGE IIP Feige and Pearce 166 167 168 169 Vector Autoregression n (z) z z p p p zt = φ1zt 1 + φ2zt 2 + + φ pzt p + t Cov( 0 ε t, ε t j )= Σ for for j 0 j = 0 Cov( ε t, zt j ) = 0 j = >
More information8 300 mm 2.50 m/s L/s ( ) 1.13 kg/m MPa 240 C 5.00mm 120 kpa ( ) kg/s c p = 1.02kJ/kgK, R = 287J/kgK kPa, 17.0 C 118 C 870m 3 R = 287J
26 1 22 10 1 2 3 4 5 6 30.0 cm 1.59 kg 110kPa, 42.1 C, 18.0m/s 107kPa c p =1.02kJ/kgK 278J/kgK 30.0 C, 250kPa (c p = 1.02kJ/kgK, R = 287J/kgK) 18.0 C m/s 16.9 C 320kPa 270 m/s C c p = 1.02kJ/kgK, R = 292J/kgK
More informationMicrosoft Word - 章末問題
1906 R n m 1 = =1 1 R R= 8h ICP s p s HeNeArXe 1 ns 1 1 1 1 1 17 NaCl 1.3 nm 10nm 3s CuAuAg NaCl CaF - - HeNeAr 1.7(b) 2 2 2d = a + a = 2a d = 2a 2 1 1 N = 8 + 6 = 4 8 2 4 4 2a 3 4 π N πr 3 3 4 ρ = = =
More information(diversity) 2
Final Version (July 27, 2012) Abstract JEL code R21 ; R31 ; R33 Key words ; ; ; ; ; ; 1? Georges Enderle Neil Crosby David Geltner PhD,CRE,FRICS 1 (diversity) 2 2 (2012.7.18) (1) (2) (3) (4) (5) (6) (7)
More information金融システムレポート(2008年3月号)
inancial ystem eport 1 Box 1 5 1 1 1 1 3 17 5 7 Box 9 Box 3 3 Box 3 Box 5 36 Box 6 38 Box 7 1 1 1 3 3 5 Box 8 58 Box 9 6 6 1 6 66 Box 1 7 7 1 3 Box 1 7 7 8 1 1 3 Box Box 1 B1-1 B1-1 origination distribution
More informationn (1.6) i j=1 1 n a ij x j = b i (1.7) (1.7) (1.4) (1.5) (1.4) (1.7) u, v, w ε x, ε y, ε x, γ yz, γ zx, γ xy (1.8) ε x = u x ε y = v y ε z = w z γ yz
1 2 (a 1, a 2, a n ) (b 1, b 2, b n ) A (1.1) A = a 1 b 1 + a 2 b 2 + + a n b n (1.1) n A = a i b i (1.2) i=1 n i 1 n i=1 a i b i n i=1 A = a i b i (1.3) (1.3) (1.3) (1.1) (ummation convention) a 11 x
More information通信容量制約を考慮したフィードバック制御 - 電子情報通信学会 情報理論研究会(IT) 若手研究者のための講演会
IT 1 2 1 2 27 11 24 15:20 16:05 ( ) 27 11 24 1 / 49 1 1940 Witsenhausen 2 3 ( ) 27 11 24 2 / 49 1940 2 gun director Warren Weaver, NDRC (National Defence Research Committee) Final report D-2 project #2,
More information2 R U, U Hausdorff, R. R. S R = (S, A) (closed), (open). (complete projective smooth algebraic curve) (cf. 2). 1., ( ).,. countable ( 2 ) ,,.,,
15, pp.1-13 1 1.1,. 1.1. C ( ) f = u + iv, (, u, v f ). 1 1. f f x = i f x u x = v y, u y = v x.., u, v u = v = 0 (, f = 2 f x + 2 f )., 2 y2 u = 0. u, u. 1,. 1.2. S, A S. (i) A φ S U φ C. (ii) φ A U φ
More information微分積分 サンプルページ この本の定価 判型などは, 以下の URL からご覧いただけます. このサンプルページの内容は, 初版 1 刷発行時のものです.
微分積分 サンプルページ この本の定価 判型などは, 以下の URL からご覧いただけます. ttp://www.morikita.co.jp/books/mid/00571 このサンプルページの内容は, 初版 1 刷発行時のものです. i ii 014 10 iii [note] 1 3 iv 4 5 3 6 4 x 0 sin x x 1 5 6 z = f(x, y) 1 y = f(x)
More informationn ξ n,i, i = 1,, n S n ξ n,i n 0 R 1,.. σ 1 σ i .10.14.15 0 1 0 1 1 3.14 3.18 3.19 3.14 3.14,. ii 1 1 1.1..................................... 1 1............................... 3 1.3.........................
More informationhttp://www.ike-dyn.ritsumei.ac.jp/ hyoo/wave.html 1 1, 5 3 1.1 1..................................... 3 1.2 5.1................................... 4 1.3.......................... 5 1.4 5.2, 5.3....................
More informationスプレッド・オプション評価公式を用いた裁定取引の可能性―電力市場のケース― 藤原 浩一,新関 三希代
403 81 1 Black and Scholes 1973 Email:kfujiwar@mail.doshisha.ac.jp 82 404 58 3 1 2 Deng, Johnson and Sogomonian 1999 Margrabe 1978 2 Deng, Johnson and Sogomonian 1999 Margrabe 1978 Black and Scholes 1973
More information211 kotaro@math.titech.ac.jp 1 R *1 n n R n *2 R n = {(x 1,..., x n ) x 1,..., x n R}. R R 2 R 3 R n R n R n D D R n *3 ) (x 1,..., x n ) f(x 1,..., x n ) f D *4 n 2 n = 1 ( ) 1 f D R n f : D R 1.1. (x,
More information36巻目次.indd
36 12 論 文 知的財産評価と企業価値 岩城康史 ( 関西学院大学大学院 ) 岡田克彦 ( 関西学院大学 ) 要 1970 1977 2009 33 旨 キーワード 1 はじめに 400 120 1 2013 9 6 1,000 Griliches et al. 1986 1 2014 6 22 2 36 12 Trajtenberg 1990 Weighted Patent Count, WPC
More informationtextream ( )
27 EM151010 2016 1 18 textream ( ) 1 4 2 6 3 8 4 10 5 12 5.1.......................................... 12 5.2 Comtemporary regression................................... 13 5.3........................................
More informationK E N Z U 2012 7 16 HP M. 1 1 4 1.1 3.......................... 4 1.2................................... 4 1.2.1..................................... 4 1.2.2.................................... 5................................
More informationKorteweg-de Vries
Korteweg-de Vries 2011 03 29 ,.,.,.,, Korteweg-de Vries,. 1 1 3 1.1 K-dV........................ 3 1.2.............................. 4 2 K-dV 5 2.1............................. 5 2.2..............................
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 informationJuly 28, H H 0 H int = H H 0 H int = H int (x)d 3 x Schrödinger Picture Ψ(t) S =e iht Ψ H O S Heisenberg Picture Ψ H O H (t) =e iht O S e i
July 8, 4. H H H int H H H int H int (x)d 3 x Schrödinger Picture Ψ(t) S e iht Ψ H O S Heisenberg Picture Ψ H O H (t) e iht O S e iht Interaction Picture Ψ(t) D e iht Ψ(t) S O D (t) e iht O S e ih t (Dirac
More informationK E N Z U 01 7 16 HP M. 1 1 4 1.1 3.......................... 4 1.................................... 4 1..1..................................... 4 1...................................... 5................................
More information202 2 9 Vol. 9 yasuhisa.toyosawa@mizuho-cb.co.jp 3 3 Altman968 Z Kaplan and Urwitz 979 Merton974 Support Vector Machine SVM 20 20 2 SVM i s i x b si t = b x i i r i R * R r (R,R, L,R ), R < R < L < R
More information1 I 1.1 ± e = = - = C C MKSA [m], [Kg] [s] [A] 1C 1A 1 MKSA 1C 1C +q q +q q 1
1 I 1.1 ± e = = - =1.602 10 19 C C MKA [m], [Kg] [s] [A] 1C 1A 1 MKA 1C 1C +q q +q q 1 1.1 r 1,2 q 1, q 2 r 12 2 q 1, q 2 2 F 12 = k q 1q 2 r 12 2 (1.1) k 2 k 2 ( r 1 r 2 ) ( r 2 r 1 ) q 1 q 2 (q 1 q 2
More information( ) Note (e ) (µ ) (τ ) ( (ν e,e ) e- (ν µ, µ ) µ- (ν τ,τ ) τ- ) ( ) ( ) (SU(2) ) (W +,Z 0,W ) * 1) 3 * 2) [ ] [ ] [ ] ν e ν µ ν τ e
( ) Note 3 19 12 13 8 8.1 (e ) (µ ) (τ ) ( (ν e,e ) e- (ν µ, µ ) µ- (ν τ,τ ) τ- ) ( ) ( ) (SU(2) ) (W +,Z 0,W ) * 1) 3 * 2) [ ] [ ] [ ] ν e ν µ ν τ e µ τ, e R, µ R, τ R (1a) L ( ) ) * 3) W Z 1/2 ( - )
More information2 G(k) e ikx = (ik) n x n n! n=0 (k ) ( ) X n = ( i) n n k n G(k) k=0 F (k) ln G(k) = ln e ikx n κ n F (k) = F (k) (ik) n n= n! κ n κ n = ( i) n n k n
. X {x, x 2, x 3,... x n } X X {, 2, 3, 4, 5, 6} X x i P i. 0 P i 2. n P i = 3. P (i ω) = i ω P i P 3 {x, x 2, x 3,... x n } ω P i = 6 X f(x) f(x) X n n f(x i )P i n x n i P i X n 2 G(k) e ikx = (ik) n
More information02.„o“φiflì„㙃fic†j
X-12-ARIMA Band-PassDECOMP HP X-12-ARIMADECOMP HPBeveridge and Nelson DECOMP X-12-ARIMA Band-PassHodrick and PrescottHP DECOMPBeveridge and Nelson M CD X ARIMA DECOMP HP Band-PassDECOMP Kiyotaki and Moore
More information