, 1), 2) (Markov-Switching Vector Autoregression, MSVAR), 3) 3, ,, , TOPIX, , explosive. 2,.,,,.,, 1

Size: px
Start display at page:

Download ", 1), 2) (Markov-Switching Vector Autoregression, MSVAR), 3) 3, ,, , TOPIX, , explosive. 2,.,,,.,, 1"

Transcription

1

2 , 1), 2) (Markov-Switching Vector Autoregression, MSVAR), 3) 3, ,, , TOPIX, , explosive. 2,.,,,.,, 1986 Q Q2,,. :, explosive, recursiveadf, MSVAR, 4

3 1 1.1,, , 1), 2) (Markov-Switching Vector Autoregression, MSVAR), 3) ,., 1985.,.,,,,.,, ).,,.,, ( 1 ). 90,,,.,.,,,,.,,., 1987, , 2),.,, 2000,,,, 3., Phillips et al.(2012), AR(1). AR(1),.. x t = µ x + δx t 1 + ε t,x (1.1) δ > 1,, explosivear(1)., 0 < δ < 1, AR(1), δ = 1,.,,,.,, explosive. 1) ), 2

4 1.2, (2009) Phillips et al.(2012), recursiveadf, 225 TOPIX : 225( ), TOPIX( ), ( /m 2 ). 1.2,, FRB view BIS view 2 3). FRB view.,,.,,.,,., BIS View,..,,, 3) (2008) 3

5 1.2.,,.,.,., 1), 2), 3).,,,.,,MSVAR,.,,.,,.,,.. 目的 手段 バブル期間の推定 単位根検定 バブル構造変化の推定 MSVAR バブル期間のみ株価に影響を与える変数の存在 インパルス応答関数 2:.,,,,.,.. 2,,, MSVAR. 3, 4

6 recursive ADF. 4, MSVAR,,. 5,., 6. 2, MSVAR.,. (2007),,,,,. (2000),,,, 4. (2000),,,,.,,,., 5,., (2012, 2013),, 2,,,.,, (2008) (Dynamic General Equilibrium, DGE),.,,., ,. MSVAR, Hamilton(1989)., Krolzig(1998) Ox MSVAR.,. Fujiwara(2004) MSVAR,,,,., Inoue and Okimoto(2007) MSVAR,

7 3, recursiveadf. recursiveadf,,,, MSVAR. 3.1, (2009) Phillips et al.(2012),., t D t, t P t, t E t., R( ). P t = R E t(p t+1 + D t+1 ) (3.1) Campbell and Shiller(1989),. p t = p f t + b t (3.2) p f t = κ γ 1 ρ + (1 ρ) ρ i E t p t+i (3.3) i=0 b t = lim i ρ i E t p t+i (3.4) E t (b t+1 ) = 1 ρ b t = (1 + exp(d p)b t ) (3.5) κ = log(ρ) (1 ρ) log( 1 1) (3.6) ρ, p t = log(p t ), d t = log(d t ), γ = log(1 + R)ρ = 1/(1 + exp(d p)), d p = E[log(D t /P t )]. p f t,,. b t,,. b t = 1 ρ b t 1 + ε b,t (1 + g)b t 1 + ε b,t, E t 1 (ε b,t ) = 0 (3.7) g = 1 ρ 1 = exp(d p) > 0,., ε b,t, b t.,(2) b t 0., (3.2), b t = 0,, p t = p f t = µ + (1 ρ) ρ i E t (d t+1+i ), 0 < ρ < 1 (3.8) i=0 6

8 3.2 ADF, d t p t., d t p t. d t p t = κ γ 1 ρ ρ i E t ( d t+1+i ) (3.9), d t I(0), p t d t (1, 1)., = 1 L, L., b t 0,, (3.7) b t explosive, (3.2), p t explosive., b t p t., d t I(1) I(0), p t explosive,., p t d t.,, p t explosive 4).,, explosive. i=0 3.2 ADF,, Augumented Dickey-Fuller(ADF). t x t,. x t = µ x + δx t + J ϕ j x t j + ε x,t (3.10) j=1, J. I(1), δ = 1, I(0), δ < 1, explosivear, δ > 1. δ t ADF. ADF = t δ=1 = ˆδ 1 ˆσ δ (3.11). ˆσ δ, ˆδ. H 0, δ 1, H 1, δ > 1 5). 3.3 sup ADF, ADF, ADF Evans(1991).,, sup ADF. sup ADF. sup ADF = sup ADF [nr] (3.12) r [r 0,1] 4) d t I(1),. (2009) TOPIX, I(1). 5), δ = 1, δ < 1,. 7

9 3.4 recursiveadf, [ ], ADF [nr], 1 [nr] ADF., r 0., n = 500, r 0 = 0.1, sup ADF [nr] = max{adf 50, ADF 51, ADF 52,..., ADF 500 } (3.13) r [r 0,1] [nr 0 ] = 50, ADF 50 1, ADF,. ADF,,.,, (explosive),,, I(1)., sup ADF,,., ADF, sup ADF,. 3.4 recursiveadf, recursiveadf. n 0 n 1 ADF ADF (n 0, n 1 )., 1 n 0 < n 1 n. n. inf ADF (r) = sup ADF (r) = min ADF (1, k) (3.14) k [nr],...,n max ADF (1, k) (3.15) k [nr],...,n rangeadf (r) = sup ADF (r) inf ADF (r) (3.16) inf ADF, sup ADF., rangeadf, sup ADF inf ADF,. (2009),, sup ADF, ( )., TOPIX( ), ,TOPIX, 2,. (2009) Phillips et al(2012), recursiveadf, k [nr] + 1 k ADF rollingadf.,,, recursiveadf 6). 6) Phillips et al(2012), recursiveadf,,. 8

10 4 MSVAR, MSVAR 7). recursiceadf,, MSVAR,, K MSVAR(p). y t = Φ (1) 1 (s t)y t 1 + Φ (2) 2 (s t)y t Φ (p) p (s t )y t p + ε t (4.1) s t = j j = 1,, K (4.2) y t,, s t 1 K. s t, s t s t., i j p ij, P (s t = j s t 1 = i, s t 2 = k,...) = P (s t = j s t 1 = i) = p ij., E(ε t ε t) = Ω(s t )., K P. p 11 p 21 p M1 p 12 p 22 p M2 P = p 1M p 2M p MM M p ij = 1, i, j (1, M) (4.3) P (j, i), p ij. (2, 1), 1 2., 3 3 MSVAR(2). j=1 y 1,t y 2,t = + ϕ (1) 11 ϕ (1) 12 ϕ (1) 13 ϕ (1) 21 ϕ (1) 22 ϕ (1) 23 (s t ) y 3,t ϕ (1) 31 ϕ (1) 32 ϕ (1) 33 ϕ (2) 11 ϕ (2) 12 ϕ (2) 13 ϕ (2) 21 ϕ (2) 22 ϕ (2) 23 (s t ) y 1,t 2 y 2,t 2 ϕ (2) 31 ϕ (2) 32 ϕ (2) y 3,t 2 33 p 11 p 21 p 31 s t = [1, 2, 3] P = p 12 p 22 p 32 p 13 p 23 p 33 y 1,t 1 y 2,t 1 (4.4) y 3,t 1 + ε 1 ε 2 (4.5) ε 3 3 p ij = 1 (4.6), 1. j=1 7) VAR (2010) 9

11 4.2,. P = p p 11 p 22 0 (4.7) 0 1 p 22 p 33, , p 11, p 22, p 33, P.,,, MSVAR. 4.2,.,. ξ k ij(h) = E ty t+h ε jt st = =s t+h =k (4.8), k y t, 1 (h k )h. MSVAR,,.,,.,. 4.3 MSVAR., EM (Gibbs sampler), 8)., R MSBVAR., Patrick(2015), MSVAR., MSBVAR,, Inoue and Okimoto(2007)., MSVAR. θ = [θ 1, θ 2, θ 3, θ 4] (4.9) θ 1 = [s 1, s 2,, s T ] (4.10) θ 2 = [p 11, p 12, p 21, p 22,, p MM ] (4.11) θ 3 = [vech(ω(1)), vech(ω(2)),, vech(ω(k)) ] (4.12) θ 4 = [β(1), β(2),, β(n) ] (4.13) β(j) = [vec(φ (1) 1 (j)), vec(φ (2) 2 (j)),, vec(φ (p) p (j)) ] (4.14) 8), (2007) 10

12 4.3 vech( ), vec( ) vech = ( ) (4.15) ( ) vec = (4.16) 5 6 6, p(θ ŷ T ) 9), θ (0), j = 0 N. 1) θ (j+1) 1 p(θ 1 θ (j) 2, θ(j) 3, θ(j) 4, ŷ T ) ( ), Baum-Hamilton-Lee-Kim(BHLK) filter and smoother., forward-filter-backward-sample. 2) θ (j+1) 2 p(θ 2 θ (j) 1, θ(j) 3, θ(j) 4, ŷ T ) ( ) Dirichlet. 3),,. 4) θ (j+1) 3 p(θ 3 θ (j) 1, θ(j) 2, θ(j) 4, ŷ T ) ( ). 5) θ (j+1) 4 p(θ 4 θ (j) 1, θ(j) 2, θ(j) 3, ŷ T ) ( ). 6) j + 1 = N.,. MSVAR, EM,,,. MSVAR,,,,., EM 9) ŷ T = {y p+1, y p+2,, y t }. 11

13 .,, (MCMC). MCMC,,.,,,.. 5,, ADF MSVAR. ADF,. MSVAR, ADF, ADF, 225 TOPIX., (CPI), ( 3 ). r 0 = 0.1, ADF : 225 TOPIX 225, TOPIX ADF

14 5.1 5%., , TOPIX, , ,, , 1,, AR(1) 1, explosivear(1) 10). 3, recursiveadf. supadf,, explosive /4 1990/ /5 1990/ : 225 TOPIX recursive ADF 225, TOPIX. ( ) ( ) : AR(1) (x t = µ x + δx t 1 + ε x,t ) δ ( 225, TOPIX) 10) AR(1), 2. 13

15 5.1 supadf (r) infadf (r) rangeadf (r) (r 0 = 1/10) supadf (r) infadf (r) rangeadf (r) (r 0 = 1/10) : recursiveadf ( 225, TOPIX),. sup ADF, 225 TOPIX explosive. 225 explosive, TOPIX explosive, explosive AR(1), δ 1.,.1, 225 TOPIX., , TOPIX ,, , TOPIX , 225 TOPIX. 225, 225, TOPIX., TOPIX,, TOPIX, 225., 225 TOPIX 11), TOPIX 225, TOPIX ). 2,TOPIX, (2009). (2009) explosive, , 2., (2009) ( ),. 3, (2000 IT 2007 ).,, ,, 11) 10 AIC 2 VAR(6)., (2010). 12), 225 TOPIX. 14

16 5.2 MSVAR, explosive,.,,,. 5.2 MSVAR, 4 MSVAR MSVAR,. MSVAR, (2010). (2010),, 1985, 86, 87,.,,.,, 225,,,, (CPI) 5.,, 2 13).,,,., 2 5 MSVAR (1)., 1980Q1 2014Q4.,,,,. 5, Nikkei-E-I-Oil-CPI., MSVAR, burn-in 1000, E CPI Nikkei Oil I Time Time 5:. 13) 3, 2. 15

17 5.2 MSVAR MSVAR 14). 6, 2., Q1 Q Q2 Q3., Q1 Q2, (=Q2) (=Q2),., 2008 Q2 Q3,., (=Q3), 1., , , 6994., 1, 225 explosive,,.,, 1,,., 7., , (I Nikkei).,,,.,. 2, 1 2, (Nikkei Nikkei, I I).,,, 2. 3, (Nikkei I, Nikkei CPI).,,. 4, 1, (Oil I)., 1,.,. 1,.,,,,. 14) MSVAR.B 16

18 5.2 MSVAR state1 state : (x, y ). 7: 1 ( 95% ). 17

19 5.2 MSVAR 8: 2 ( 95% ). Nikkei Nikkei E I Shock to E I Oil CPI Oil CPI Response in 9: 1,2 ( 95% ).,. 18

20 5.3 (, ).,.,.,.,. 1,, , 225.,, ). 2, IT,,., 225.,.,, 16)., 5,,.,,,, ,.,, ,. 15) MSVAR,. 16) 1990,,,. 19

21 5.3 y t = β 0 + (β 1 + β 2 D 1 (t))rate t d + ε t (5.1) y t = β 0 + (β 1 + β 3 D 2 (t))rate t d + ε t (5.2) y t = β 0 + (β 1 + β 2 D 1 (t) + β 3 D 2 (t))rate t d + ε t (5.3) y t = β 0 + (β 1 + β 2 D 1 (t))rate t d + β 4 ST t + β 5 EX t + β 6 MB t + ε t (5.4) { 0 (t S k ) D 1 (t) = t + 1 t 0 (t S k ), D 2(t) = t t = 1,., 175 (5.5) { {1986Q1,, 1990Q2} ( ) S k = (5.6) {1986Q1,, 1990Q1} (TOPIX ) y t 225 or TOPIX( ) Rate t ST t TOPIX or 225( ) EX t MB t D 1 (t) D 2 (t), 4., 225 TOPIX 2., (5.1), ( 1), (5.2), ( 2), (5.3), 1 2 ( 3), (5, 4), (5.1), ( 225, TOPIX ),, ( 4). D 1 (t), S k 1, 2, 3, 1, 0.,, 225, 1986 Q Q2, TOPIX, 1986 Q Q1., D 2 (t), 1, 2, 3, 1., 1970 Q Q4.,, β 2,. 1,,, 2,., 3 4, β 2,., 5. I) (d = 0), II) 1 (d = 1), III) 1, IV) 1, V) D j (t) = k, j = 1, 2 k k

22 5.3, I), II)1, III, IV), V), (4 ) (5 ) (2 ) (2 ) , I II, III, IV, V. Nikkei225 TOPIX / I n 1 I N n 2 I N n 3 I N n 4 I N n 1 I T n 2 I T n 3 I T n 4 I T n t 1 I N t 2 I N t 3 I N t 4 I N t 1 I T t 2 I T t 3 I T t 4 I T t II n 1 II N n 2 II N n 3 II N n 4 II N n 1 II T n 2 II T n 3 II T n 4 II T n t 1 II N t 2 II N t 3 II N t 4 II N t 1 II T t 2 II T t 3 II T t 4 II T t III n 1 III N n 2 III N n 3 III N n 4 III N n 1 III T n 2 III T n 3 III T n 4 III T n t 1 III N n 2 III N n 3 III N n 4 III N n 1 III T n 2 III T n 3 III T n 4 III T n IV n 1 IV N n 2 IV N n 3 IV N n 4 IV N n 1 IV T n 2 IV T n 3 IV T n 4 IV T n t 1 IV N t 2 IV N t 3 IV N t 4 IV N t 1 IV T t 2 IV T t 3 IV T t 4 IV T t V n 1 V N n 2 V N n 3 V N n 4 V N n 1 V T n 2 V T n 3 V T n 4 V T n t 1 V N t 2 V N t 3 V N t 4 V N t 1 V T t 2 V T t 3 V T t 4 V T t 3: (n, t TOPIX.),,,,.,,.,,,.,, 17). 17), 21

23 5.3 Excange.Rate Time.Dummy O.N.Rate Bubble.Dummy.T TOPIX Bubble.Dummy.N Nikkei Monetary.Base Time Time 10: , β 2 β 3 10%, 4., I II, II., III V 1. 4, TOPIX 1 (= ), k 2., β 3 β 2 TOPIX, 1. Nikkei225 TOPIX I n t II n t III n t IV n t V n t 4: ( 10% β 2, II β 3 ) 22

24 5.3 II n IV t 5., TOPIX 4,,.,., (, 1986 Q Q2 ),., 4,,,., 6 V n., 5 t., k k 2,., R 2, 5 6, k 2.. (1986 Q Q2), ( 225, TOPIX).,., k k 2, 18).,.,. 1, TOPIX,., (5.0% 3.0%) 19).,, ,, 1986 Q Q2, ,,,. 2,,.,.,,,.,, 18), k 3, k 2 t R 2. 19) % 4.5%, 3 10, 4.5% 4.0%, 4 21, 4.0% 3.5%, 11 1, 3.5% 3.0%,, 2 23, 3.0% 2.5%. 23

25 5.3. Nikkei225 TOPIX β ( ) (1.741) (1.237 ) (1.875 ) ( 1.108) (1.853 ) (1.478 ) (1.963 ) (1.621) β ( ) ( 0.516) ( 0.657) ( 0.975) (0.874) ( 1.108) ( 0.979) ( 1.226) ( 1.271) β ( ) ( ) ( ) ( ) ( ) ( ) (0.620) β ( ) ( 0.128) (0.828) ( 0.013) (0.733) β ( ) ( ) ( ) β ( ) ( 0.079) ( 0.700) β ( ) (2.322 ) ( 1.930) R : II n IV t ( t,,,,, 0.1%, 1%, 5%, 10% ) Nikkei225 TOPIX β ( ) (1.822) (1.237 ) (1.948 ) ( 1.038) (1.901 ) (1.478 ) (2.002 ) (1.571) β ( ) ( 0.596) ( 0.657) ( 0.995) (0.869) ( 1.193) ( 0.979) ( 1.235) ( 1.311) β ( ) ( ) ( ) ( ) ( ) ( ) (0.806) β ( ) ( 0.128) (0.799) ( 0.013) (0.692) β ( ) ( ) ( ) β ( ) ( 0.076) ( 0.689) β ( ) (2.330 ) ( ) R : V n ( t,,,,, 0.1%, 1%, 5%, 10% ) 24

26 6, 3, , TOPIX , , explosive., MSVAR,,,.,,,,.,, 1986 Q Q2,., 2. 1,..,,. 2, ( ).,,,.,.,., ,,., , , 2010,,., 20).,.,,,.,.,. 1,.,. (2000), ,,.,,,. 2, MSVAR.,, 20) , 200, ,

27 .,.,,,., 21),,.,, MSVAR 22). [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] Campbell, J. Y., and Shiller R., The Dividend-Price Ratio and Expectation of Future Dividend and Discount Factors, Review of Financial Studies 1, 1989, [14] Evans, George, W., Pitfalls in Testing for Explosive Bubbles in Asset Prices, Economic Review 81, 1991, pp [15] Fujiwara, Ippei, Evaluationg Monetary Policy When Nominal Interest Rates are Almost Zero, Research and Statistics Department, Bank of Japan, 2004, pp ) (2000) 22),. 26

28 [16] Hamilton, James, A New Approach to the Economic Analysis of Nonstationary Time Series and the Buiness Cycle, Econometrica 57(2), 1989, pp [17] Inoue, Tomoo, Okimoto, Tatsuyoshi, Were There Structural Breaks in the Effect of Japanese Monetary Policy? Re-evaluating Policy Effects of the Lost Decade, Faculty of Economics and IG555, Yokohama National University, 2007, pp [18] Krolzig, Hans, M., Econometric Modelling of Markov-Switching Vector Autoregressions using MSVAR for Ox, Institute of Economics and Statistics and Nuffield College, Oxford, 1998, pp [19] Patrick, Brandt, Package MSBVAR, CRAN, 2015, pp.1-92 [20] Phillips, Peter, C. B., Wu, Yangru and Yu, Jun, Explosive Behavior in the 1990s Nasdaq : When Did Exuberance Escalate Asset Values?, Couwles Foundation Paper NO.1349, 2012, pp

29 .A 225, TOPIX, NEEDS (CPI) = =100 IMF 85 6, 7:.B MSVAR B.1 MSVAR Naive SR Time-Series SE p p p p : MSVAR 28

30 B.2 MSVAR B.2 MSVAR Naive SR Time-Series SE Nikkei Nikkei E I Oil CPI E Nikkei E I Oil CPI I Nikkei E I Oil CPI Oil Nikkei E I Oil CPI CPI Nikkei E I Oil CPI : 1 MSVAR 29

31 B.2 MSVAR Naive SR Time-Series SE Nikkei Nikkei E I Oil CPI E Nikkei E I Oil CPI I Nikkei E I Oil CPI Oil Nikkei E I Oil CPI CPI Nikkei E I Oil CPI : 2 MSVAR 30

1 Nelson-Siegel Nelson and Siegel(1987) 3 Nelson-Siegel 3 Nelson-Siegel 2 3 Nelson-Siegel 2 Nelson-Siegel Litterman and Scheinkman(199

1 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 information

財政赤字の経済分析:中長期的視点からの考察

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

03.Œk’ì

03.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 information

LA-VAR Toda- Yamamoto(1995) VAR (Lag Augmented vector autoregressive model LA-VAR ) 2 2 Nordhaus(1975) 3 1 (D2)

LA-VAR Toda- Yamamoto(1995) VAR (Lag Augmented vector autoregressive model LA-VAR ) 2 2 Nordhaus(1975) 3 1 (D2) LA-VAR 1 1 1973 4 2000 4 Toda- Yamamoto(1995) VAR (Lag Augmented vector autoregressive model LA-VAR ) 2 2 Nordhaus(1975) 3 1 (D2) E-mail [email protected] 2 Toda, Hiro Y. and Yamamoto,T.(1995) 3

More information

082_rev2_utf8.pdf

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 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.   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 information

カルマンフィルターによるベータ推定( )

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: [email protected]., 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 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)

「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 information

n 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 information

fiúŁÄ”s‘ê‡ÌŁª”U…−…X…N…v…„…~…A…•‡Ì ”s‘ê™´›ß…−…^†[…fiŠ‚ª›Âfl’«

fiúŁÄ”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 information

untitled

untitled 2007 2 * (i) (ii) 2006 7 1999 2 2000 8 1 (2003) Oda and Ueda (2005) 2005 Kimura and Small(2006) Iwamura, Shiratsuka and Watanabe (2006) (2006) 3 (i) (ii) (iii) 2 2 3 4 2.1 (2003) (2005) 1) (i) (ii) (i)

More information

seminar0220a.dvi

seminar0220a.dvi 1 Hi-Stat 2 16 2 20 16:30-18:00 2 2 217 1 COE 4 COE RA E-MAIL: [email protected] 2004 2 25 S-PLUS S-PLUS S-PLUS S-code 2 [8] [8] [8] 1 2 ARFIMA(p, d, q) FI(d) φ(l)(1 L) d x t = θ(l)ε t ({ε t }

More information

デフレの定義(最新版).PDF

デフレの定義(最新版).PDF DP/01-1 Director General for Economic Assessment and Policy Analysis CABINET OFFICE E-mail : [email protected] 1 2 3 i (ii) 4 5 Deflation defined as at least two consecutive years of price decreases.

More information

( ) Loewner SLE 13 February

( ) 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 information

,,,17,,, ( ),, E Q [S T F t ] < S t, t [, T ],,,,,,,,

,,,17,,, ( ),, E Q [S T F t ] < S t, t [, T ],,,,,,,, 14 5 1 ,,,17,,,194 1 4 ( ),, E Q [S T F t ] < S t, t [, T ],,,,,,,, 1 4 1.1........................................ 4 5.1........................................ 5.........................................

More information

³ÎΨÏÀ

³ÎΨÏÀ 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 information

n 2 + π2 6 x [10 n x] x = lim n 10 n n 10 k x 1.1. a 1, a 2,, a n, (a n ) n=1 {a n } n=1 1.2 ( ). {a n } n=1 Q ε > 0 N N m, n N a m

n 2 + π2 6 x [10 n x] x = lim n 10 n n 10 k x 1.1. a 1, a 2,, a n, (a n ) n=1 {a n } n=1 1.2 ( ). {a n } n=1 Q ε > 0 N N m, n N a m 1 1 1 + 1 4 + + 1 n 2 + π2 6 x [10 n x] x = lim n 10 n n 10 k x 1.1. a 1, a 2,, a n, (a n ) n=1 {a n } n=1 1.2 ( ). {a n } n=1 Q ε > 0 N N m, n N a m a n < ε 1 1. ε = 10 1 N m, n N a m a n < ε = 10 1 N

More information

高齢化とマクロ投資比率―国際パネルデータを用いた分析―

高齢化とマクロ投資比率―国際パネルデータを用いた分析― 196 2017 * ** ** ** ** 160 2 2 JEL Classification Codes E21, E22, J11 Keywords * ESRI 28 ESRI 29 3 17 ESRI ** 115 196 Population Aging and Domestic Investment An Analysis Using International Panel Data

More information

山形大学紀要

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 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.   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 information

‚åŁÎ“·„´Šš‡ðŠp‡¢‡½‹âfi`fiI…A…‰…S…−…Y…•‡ÌMarkovŸA“½fiI›ð’Í

‚åŁÎ“·„´Šš‡ðŠp‡¢‡½‹âfi`fiI…A…‰…S…−…Y…•‡ÌMarkovŸA“½fiI›ð’Í Markov 2009 10 2 Markov 2009 10 2 1 / 25 1 (GA) 2 GA 3 4 Markov 2009 10 2 2 / 25 (GA) (GA) L ( 1) I := {0, 1} L f : I (0, ) M( 2) S := I M GA (GA) f (i) i I Markov 2009 10 2 3 / 25 (GA) ρ(i, j), i, j I

More information

わが国のレポ市場について―理論的整理と実証分析―

わが国のレポ市場について―理論的整理と実証分析― GCGC SC GCSC SC SC E-mail: [email protected] E-mail: [email protected] GC general collateralscspecial collateral Griffiths and Winters GCFF Jordan and JordanDuffie matched book GC GC SC DuffieKrishnamurthy

More information

IMES DISCUSSION PAPER SERIES Discussion Paper No. 99-J- 9 -J-19 INSTITUTE FOR MONETARY AND ECONOMIC STUDIES BANK OF JAPAN

IMES DISCUSSION PAPER SERIES Discussion Paper No. 99-J- 9 -J-19 INSTITUTE FOR MONETARY AND ECONOMIC STUDIES BANK OF JAPAN IMES DISCUSSION PAPER SERIES Discussion Paper No. 99-J- 9 -J-19 INSTITUTE FOR MONETARY AND ECONOMIC STUDIES BANK OF JAPAN 100-8630 03 IMES Discussion Paper Series 99-J- 9 -J-19 1999 6 * * [1999] *(E-mail:

More information

2 1 κ c(t) = (x(t), y(t)) ( ) det(c (t), c x (t)) = det (t) x (t) y (t) y = x (t)y (t) x (t)y (t), (t) c (t) = (x (t)) 2 + (y (t)) 2. c (t) =

2 1 κ c(t) = (x(t), y(t)) ( ) det(c (t), c x (t)) = det (t) x (t) y (t) y = x (t)y (t) x (t)y (t), (t) c (t) = (x (t)) 2 + (y (t)) 2. c (t) = 1 1 1.1 I R 1.1.1 c : I R 2 (i) c C (ii) t I c (t) (0, 0) c (t) c(i) c c(t) 1.1.2 (1) (2) (3) (1) r > 0 c : R R 2 : t (r cos t, r sin t) (2) C f : I R c : I R 2 : t (t, f(t)) (3) y = x c : R R 2 : t (t,

More information

nsg02-13/ky045059301600033210

nsg02-13/ky045059301600033210 φ φ φ φ κ κ α α μ μ α α μ χ et al Neurosci. Res. Trpv J Physiol μ μ α α α β in vivo β β β β β β β β in vitro β γ μ δ μδ δ δ α θ α θ α In Biomechanics at Micro- and Nanoscale Levels, Volume I W W v W

More information

() Remrk I = [0, ] [x i, x i ]. (x : ) f(x) = 0 (x : ) ξ i, (f) = f(ξ i )(x i x i ) = (x i x i ) = ξ i, (f) = f(ξ i )(x i x i ) = 0 (f) 0.

() Remrk I = [0, ] [x i, x i ]. (x : ) f(x) = 0 (x : ) ξ i, (f) = f(ξ i )(x i x i ) = (x i x i ) = ξ i, (f) = f(ξ i )(x i x i ) = 0 (f) 0. () 6 f(x) [, b] 6. Riemnn [, b] f(x) S f(x) [, b] (Riemnn) = x 0 < x < x < < x n = b. I = [, b] = {x,, x n } mx(x i x i ) =. i [x i, x i ] ξ i n (f) = f(ξ i )(x i x i ) i=. (ξ i ) (f) 0( ), ξ i, S, ε >

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,,.

,. 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

I, II 1, A = A 4 : 6 = max{ A, } A A 10 10%

I, II 1, A = A 4 : 6 = max{ A, } A A 10 10% 1 2006.4.17. A 3-312 tel: 092-726-4774, e-mail: [email protected], http://www.math.kyushu-u.ac.jp/ hara/lectures/lectures-j.html Office hours: B A I ɛ-δ ɛ-δ 1. 2. A 1. 1. 2. 3. 4. 5. 2. ɛ-δ 1. ɛ-n

More information

all.dvi

all.dvi 72 9 Hooke,,,. Hooke. 9.1 Hooke 1 Hooke. 1, 1 Hooke. σ, ε, Young. σ ε (9.1), Young. τ γ G τ Gγ (9.2) X 1, X 2. Poisson, Poisson ν. ν ε 22 (9.) ε 11 F F X 2 X 1 9.1: Poisson 9.1. Hooke 7 Young Poisson G

More information

Microsoft Word - 表紙.docx

Microsoft Word - 表紙.docx 黒住英司 [ 著 ] サピエンティア 計量経済学 訂正および練習問題解答 (206/2/2 版 ) 訂正 練習問題解答 3 .69, 3.8 4 (X i X)U i i i (X i μ x )U i ( X μx ) U i. i E [ ] (X i μ x )U i i E[(X i μ x )]E[U i ]0. i V [ ] (X i μ x )U i i 2 i j E [(X i

More information

y = 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 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 information

IMES DISCUSSION PAPER SERIES Discussion Paper No. 99-J-17 INSTITUTE FOR MONETARY AND ECONOMIC STUDIES BANK OF JAPAN 100-8630 203 IMES Discussion Paper Series 99-J-17 1999 6 * JEL classification E52 E58

More information

<4D F736F F D B B83578B6594BB2D834A836F815B82D082C88C60202E646F63>

<4D F736F F D B B83578B6594BB2D834A836F815B82D082C88C60202E646F63> 単純適応制御 SAC 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/091961 このサンプルページの内容は, 初版 1 刷発行当時のものです. 1 2 3 4 5 9 10 12 14 15 A B F 6 8 11 13 E 7 C D URL http://www.morikita.co.jp/support

More information

第一次大戦後の日本における国債流通市場の制度改革

第一次大戦後の日本における国債流通市場の制度改革 IMES DISCUSSION PAPER SERIES Discussion Paper No. 2009-J-23 INSTITUTE FOR MONETARY AND ECONOMIC STUDIES BANK OF JAPAN 103-8660 2-1-1 http://www.imes.boj.or.jp IMES Discussion Paper Series 2009-J-23 2009

More information

ii 3.,. 4. F. (), ,,. 8.,. 1. (75%) (25%) =7 20, =7 21 (. ). 1.,, (). 3.,. 1. ().,.,.,.,.,. () (12 )., (), 0. 2., 1., 0,.

ii 3.,. 4. F. (), ,,. 8.,. 1. (75%) (25%) =7 20, =7 21 (. ). 1.,, (). 3.,. 1. ().,.,.,.,.,. () (12 )., (), 0. 2., 1., 0,. 24(2012) (1 C106) 4 11 (2 C206) 4 12 http://www.math.is.tohoku.ac.jp/~obata,.,,,.. 1. 2. 3. 4. 5. 6. 7.,,. 1., 2007 (). 2. P. G. Hoel, 1995. 3... 1... 2.,,. ii 3.,. 4. F. (),.. 5... 6.. 7.,,. 8.,. 1. (75%)

More information

-February FRB BIS IMF BIS Spillover Spillovers BIS IMF EMEs

-February FRB BIS IMF BIS Spillover Spillovers BIS IMF EMEs BIS FRB BIS BIS FRB QE QE -February FRB BIS IMF BIS Spillover Spillovers BIS IMF EMEs GDP GDP........ GDP BIS, 83rd Annual Report, June 2013, p.14 Ibid., p.14. -February 1 1 2009 2013 2) 2009 2013 2) -7.6-4.0

More information

1 (1997) (1997) 1974:Q3 1994:Q3 (i) (ii) ( ) ( ) 1 (iii) ( ( 1999 ) ( ) ( ) 1 ( ) ( 1995,pp ) 1

1 (1997) (1997) 1974:Q3 1994:Q3 (i) (ii) ( ) ( ) 1 (iii) ( ( 1999 ) ( ) ( ) 1 ( ) ( 1995,pp ) 1 1 (1997) (1997) 1974:Q3 1994:Q3 (i) (ii) ( ) ( ) 1 (iii) ( ( 1999 ) ( ) ( ) 1 ( ) ( 1995,pp.218 223 ) 1 2 ) (i) (ii) / (iii) ( ) (i ii) 1 2 1 ( ) 3 ( ) 2, 3 Dunning(1979) ( ) 1 2 ( ) ( ) ( ) (,p.218) (

More information

Part () () Γ Part ,

Part () () 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

4.9 Hausman Test Time Fixed Effects Model vs Time Random Effects Model Two-way Fixed Effects Model

4.9 Hausman Test Time Fixed Effects Model vs Time Random Effects Model Two-way Fixed Effects Model 1 EViews 5 2007 7 11 2010 5 17 1 ( ) 3 1.1........................................... 4 1.2................................... 9 2 11 3 14 3.1 Pooled OLS.............................................. 14

More information

006 11 8 0 3 1 5 1.1..................... 5 1......................... 6 1.3.................... 6 1.4.................. 8 1.5................... 8 1.6................... 10 1.6.1......................

More information

オーストラリア研究紀要 36号(P)☆/3.橋本

オーストラリア研究紀要 36号(P)☆/3.橋本 36 p.9 202010 Tourism Demand and the per capita GDP : Evidence from Australia Keiji Hashimoto Otemon Gakuin University Abstract Using Australian quarterly data1981: 2 2009: 4some time-series econometrics

More information

π, R { 2, 0, 3} , ( R),. R, [ 1, 1] = {x R 1 x 1} 1 0 1, [ 1, 1],, 1 0 1,, ( 1, 1) = {x R 1 < x < 1} [ 1, 1] 1 1, ( 1, 1), 1, 1, R A 1

π, R { 2, 0, 3} , ( R),. R, [ 1, 1] = {x R 1 x 1} 1 0 1, [ 1, 1],, 1 0 1,, ( 1, 1) = {x R 1 < x < 1} [ 1, 1] 1 1, ( 1, 1), 1, 1, R A 1 sup inf (ε-δ 4) 2018 1 9 ε-δ,,,, sup inf,,,,,, 1 1 2 3 3 4 4 6 5 7 6 10 6.1............................................. 11 6.2............................... 13 1 R R 5 4 3 2 1 0 1 2 3 4 5 π( R) 2 1 0

More information

TOP URL 1

TOP URL   1 TOP URL http://amonphys.web.fc.com/ 3.............................. 3.............................. 4.3 4................... 5.4........................ 6.5........................ 8.6...........................7

More information

solutionJIS.dvi

solutionJIS.dvi May 0, 006 6 [email protected] /9/005 (7 0/5/006 1 1.1 (a) (b) (c) c + c + + c = nc (x 1 x)+(x x)+ +(x n x) =(x 1 + x + + x n ) nx = nx nx =0 c(x 1 x)+c(x x)+ + c(x n x) =c (x i x) =0 y i (x

More information

V(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

V(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

II III II 1 III ( ) [2] [3] [1] 1 1:

II 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 information

(2004 ) 2 (A) (B) (C) 3 (1987) (1988) Shimono and Tachibanaki(1985) (2008) , % 2 (1999) (2005) 3 (2005) (2006) (2008)

(2004 ) 2 (A) (B) (C) 3 (1987) (1988) Shimono and Tachibanaki(1985) (2008) , % 2 (1999) (2005) 3 (2005) (2006) (2008) ,, 23 4 30 (i) (ii) (i) (ii) Negishi (1960) 2010 (2010) ( ) ( ) (2010) E-mail:[email protected] E-mail:[email protected] E-mail:[email protected] 1 1 16 (2004 ) 2 (A) (B) (C) 3 (1987)

More information

autocorrelataion cross-autocorrelataion Lo/MacKinlay [1988, 1990] (A)

autocorrelataion cross-autocorrelataion Lo/MacKinlay [1988, 1990] (A) Discussion Paper Series A No.425 2002 2 186-8603 [email protected] 14 1 24 autocorrelataion cross-autocorrelataion Lo/MacKinlay [1988, 1990] 1990 12 13 (A) 12370027 13 1 1980 Lo/MacKinlay [1988]

More information

2 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

2 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 information

自由集会時系列part2web.key

自由集会時系列part2web.key spurious correlation spurious regression xt=xt-1+n(0,σ^2) yt=yt-1+n(0,σ^2) n=20 type1error(5%)=0.4703 no trend 0 1000 2000 3000 4000 p for r xt=xt-1+n(0,σ^2) random walk random walk variable -5 0 5 variable

More information

(2 X Poisso P (λ ϕ X (t = E[e itx ] = k= itk λk e k! e λ = (e it λ k e λ = e eitλ e λ = e λ(eit 1. k! k= 6.7 X N(, 1 ϕ X (t = e 1 2 t2 : Cauchy ϕ X (t

(2 X Poisso P (λ ϕ X (t = E[e itx ] = k= itk λk e k! e λ = (e it λ k e λ = e eitλ e λ = e λ(eit 1. k! k= 6.7 X N(, 1 ϕ X (t = e 1 2 t2 : Cauchy ϕ X (t 6 6.1 6.1 (1 Z ( X = e Z, Y = Im Z ( Z = X + iy, i = 1 (2 Z E[ e Z ] < E[ Im Z ] < Z E[Z] = E[e Z] + ie[im Z] 6.2 Z E[Z] E[ Z ] : E[ Z ] < e Z Z, Im Z Z E[Z] α = E[Z], Z = Z Z 1 {Z } E[Z] = α = α [ α ]

More information

N cos s s cos ψ e e e e 3 3 e e 3 e 3 e

N cos s s cos ψ e e e e 3 3 e e 3 e 3 e 3 3 5 5 5 3 3 7 5 33 5 33 9 5 8 > e > f U f U u u > u ue u e u ue u ue u e u e u u e u u e u N cos s s cos ψ e e e e 3 3 e e 3 e 3 e 3 > A A > A E A f A A f A [ ] f A A e > > A e[ ] > f A E A < < f ; >

More information

2 2 MATHEMATICS.PDF 200-2-0 3 2 (p n ), ( ) 7 3 4 6 5 20 6 GL 2 (Z) SL 2 (Z) 27 7 29 8 SL 2 (Z) 35 9 2 40 0 2 46 48 2 2 5 3 2 2 58 4 2 6 5 2 65 6 2 67 7 2 69 2 , a 0 + a + a 2 +... b b 2 b 3 () + b n a

More information