中国封闭式基金折价问题研究

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1 panel GARCH EGARCH 3 EGARCH - -

2 LIQUIDITY 5.. MANAGERIAL PERFORMANCE THEORY 5..3 INVESTOR SENTIMENT THEORY RATIONAL EXPECTATION THEORY 6..4 MARKET FRICTIONS

3 EGARCH EGARCH 44 7 EGARCH 45 8 EGARCH

4 . closed-end fund NAV pricei NAVi D i = NAV i. LeeShleifer Thaler99-4 -

5 3... liquidiy proxy for liquidiy Daar.. managerial performance heory sock selecion marke iming Chay Trzcinka

6 ..3 invesor senimen heory raional expecaion heory DeLongShleiferSummers Waldmann99 noise rader LeeShleifer Thaler99 DSSW, Chandar Paro closed-end counry funds Kyle985 O Hara marke fricions raional explanaion - 6 -

7 .3 panel GARCHEGARCH 3 EGARCH - 7 -

8 . 3 3 ) % 6 5 ) % %5.97% 3) Skewness ) 3 9 lepokuric (playkuric)

9 n D D = α + β + ε. D D D = a + ) D ( β + ε. ε β = D DickeyFuller β = ) ) D + ε = D 3) 57 D.. D i Pi NAV i = NAV i W i n i = NAV i NAV Pi i = m i i m i, DI = n i = W i NAVi i Di i mi Wi i n DI D i, - 9 -

10

11 % A

12 (invesor senimen) DeLongShleiferSummers Waldmann(99), LeeShleifer Thaler(99)DSSW :, % - -

13 .3. Likelihood Raio *.* *.9*.9* * 3.86*.97* 9.4* * * 8.3* * 9.53* 8.3* * 6.63*.53*.* 4.* * * 5.55* * 6.66* * * 5%

14 3 3. privae informaion noise ( v N p,σ (, σ ) N ~ v ) - 4 -

15 O Hara(997) Single-Aucion Model agen v x x + p p x + p = P x + π = ( v p) x P ( ) ( x + ) = E[ v x + ] ( ) X o X (O) v E [ π ( X () o, P) v~ = v] > E[ π ( X ( o), P) v~ = v Kyle Kyle985 () v = β ( v p ) = α + βv x = X 3. α = βp ( x + ) = p + λ( + ) P = P x 3. β = Σ σ λ = Σ σ ] - 5 -

16 3. laws of condiional disribuions of normally disribued random variables ~v ϑ ~ γ ( ~ N, σ ) ( ϑ ~ N ~,Σ ) E() v p ( ) = x + α + βp ~ γ x + ~ = E Σ Σ Σ Σ βσ = = Σ Σ βσ σ + β Σ λ = βσ σ + β Σ P( x + ) = E = p = p ( ) ( ) ( vγ ) = + Σ Σ ( γ ) βσ + σ + β Σ + λγ ( γ α βp ) 3.3 ~ π = E = ( v ~ d ~ π = dβ σ β = ( ) λ = Σ σ {[ v P( x + ) x] v = v~ } = E{ [ v ( P )] x v v~ + λγ = } β x P ) x σ + β / x( σ + β ( σ + β ) β ) ~ π ) x x σ β x = ( σ + β x x σ β = 3.3 Σ E ~,Σ ( ) () ( ) ϑ = ϑ,ϑ N Σ () ( ) () ( ) ( ) ( ϑ ϑ ) = + Σ Σ ( ϑ ) - 6 -

17 ( x + ) = p + λ( x + ) P ( v) = ( v ) x = X β θ x + = v p + = β ( ) p θ β + p = v + β 3.4 β Z θ β + p = v + Z v ( σ ) Z, ( v, ) Z v ~ N β σ N ~ Z v Bayes Learning Rule p p = p Σ + Z ( β σ ) Σ + β σ ( Σ + β σ ) Σ = β pσ + σ Z p = = β + σ + σ ( p + Z ) = ( p + θ p ) θ = x + λ = Σ p = p = p σ x + + β σ + Σ ( x + ) = p + λ( x + ) x = β ( v p ) - 7 -

18 x = Σ σ ( v p ) 3.5 x π = E [( v P ] ( ) ) x = Σ σ 3.6 σ 3.3 λ x p x densiy p convoluion σ / σ p ( ) + = p + v p 3.7 σ Σ Σ p p v - 8 -

19 Σ = Σ β + σ β Σ = Σ σ σ + σ = Σ 3.8 v 5% p v v p ( ) N p,σ N ( p Σ ) 3.7 v, p [ p v] ( p ) E = v v v 3.4 θ β + p = v + β β T θ + T p = v + T = T = β T = T 3. T Srong Law of Large numbers E T T = T θ T T T = p = v T [ p v] E[ p v] = lim p v = T T = - 9 -

20 v - -

21 Yi =a+agmi+ajyri+a3fhi+a4jzdi+a5idxi+a6vi+a7kmxsi+i (4.) Y. ε i GM 3, ) JYR ) FH 3) JZD % - -

22 % % % 4. 4 JZD = I d + I d I d i i 4. I i d i 4) IDX 5) V 6) KMXS 4.. 5: Variable Number Parial Model 4. Sep Enered Label Vars In R-Square R-Square C(p) F Value Pr > F GM GM <. JYR JYR <. 3 FH FH <. 4 JZD JZD IDX IDX V V

23 4. Parameer Sandard Variable Label DF Esimae Error Value Pr > GM GM <. JYR JYR <. FH FH <. JZD JZD Y = GM.4378log( JYR) +.36FH. 3384JZD i i i i ) ) 3),,,,,, 3-3 -

24 4. 4) (999 ) Parameer Sandard Variable Label DF Esimae Error Value Pr > Inercep Inercep <. IDX IDX GM GM FH FH JZD JZD

25 Parameer Sandard Variable Label DF Esimae Error Value Pr > GM GM <. FH FH <. JYR JYR <. JZD JZD P. A,,,,, A 4.3 EGARCH 985 Kyle - 5 -

26 Kyle,985 4 BreadhWidh (Deph) 3 Resiliency 4 (Immediacy) 3,

27 GARCH ---EGARCH EGARCH Nelson(99)GARCH, Black Chrisie (Leverage Effec) h ε i Nelson99 GARCH EGARCH EGARCH h ε. i R = i= γ R i + ε log( h ) = var( ε ) = a + r z = ε / i h q i= a log( h i ) + λ p i i= z - + θ p i i= z EGARCHM R γ + h Z 4.4 = c + R + βv + β V + β 3V3-7 -

28 3 = a + ( a j ln h j + λ j Z j + θ jz j ) + ϖ V + ϖ V + ϖ 3V + ξ j= ln h 3 R ln P ln P lnt4.5 P V V V 3 T h Z EGARCHM 68 ) R R-.3.5 ) V ) (V3) 4 4) θ RES/SQR[GARCH]()RES/SQR[GARCH]() RES/SQR[GARCH](3) θ

29 - 5) 6 89 T V dv P d ln EGARCH-M Z h Z V h h dv P d ln ϖ β β + = + = 4.6 h Z ) ln ( β = dv P d E ) ( 4 ) ln ( h E dv P d D ϖ = 4.7 ) ( 4 h E ϖ n i i i dv P S d ) ln( = V S i i i V V χ = i χ i i i n i i i i n i i i i n i i n i i i dv P d P S dv P d P S dv dp S dv P S d ln ln χ = = = = = = =

30 χ i S i i = n i= S P i P i d dv n i= S i P i / n i= S i P i d ln P n i = χ i i= dvi d ln n S P d ln P = 4.9 i i n n i= i E( ) E( χ i ) E( ) = χ i β i dv i= dvi i= n d ln Si Pi n n i= 4 d ln Pi 4 D( = χ i D = χ i wi E( hi ) dv dv 4 i= i i= - 3 -

31 5-3 -

32 Sharpe Jensen Treynor - 3 -

33 - 33 -

34

35 . Alan K. Severn, 998, Closed-end funds and senimen risk, Review of Financial Economics 7, No Bollerslev T., 986, Generalized auoregressive condiional heeroskedasiciy, Journal of Economerics 3, Brauer, Gregory, 998, A closed-end fund shares abnormal reurns and he informaion conen of discouns and premiums, Journal of Finance 43, Charles M.C. L, Anderei S. and Richard H.T, 99, Invesor senimen and he closed-end fund puzzle, Journal of Finance, Vol XLVI. 5. Chay J.B. and Charles A.T., 999, Managerial performance and cross-secional pricing of closed-end fund, Journal of financial economics 5, Chen Nai-fu, Raymond K., Meron H and Miller, 993, Are he discouns on closed-end funds a senimen index?, Journal of Finance 48, De Long, J.B., Shleifer A., Summers L. H., and Waldmann R. J., 99, Noise rader risk in financial markes, Journal of Poliical Economy 98, Dilip K.P.,, Measuring performance of he inernaional closed-end fund, Journal of Banking and Finance 5, John E. Richard, James B. Wiggins,, The informaion conen of closed-end counry fund discouns, Financial Services Review 9, Kyle, A.S., Coninuous aucions and insider rading, 985, Economerica 53, pp Lee, Charles MC., Shleifer A. and Thaler R. H., 999, Invesor senimen and he closed-end fund puzzle, Journal of Finance 46, Maddala G.S., 998, Inroducion o economerics, nd Ediion. 3. Chandar N. and Paro D. C., Why do closed-end counry funds rade a enormous premiums during currency crises?, Pacific-Basin Finance Journal 8, Neal R. and Whealey S.M., 998, Adverse selecion and bid-ask spreads: evidence from closed-end Fund, Journal of Financial Marke,

36 5. O Hara and Maureem, Marke microsrucure heory, 997, Blackwell Publishers Inc. 6. Olienyk J.P., Schwebach R.G. and Zumwal J. K., 999, WEBS, SPDRS, and Counry Funds: an analysis of inernaional coinegraion, Journal of Mulinaional Financial Managemen 9, Daar V.,, Impac of liquidiy on premia/discouns in closed-end funds, The Quarerly Review of Economics and Finance 4,

37 % -.53% -8.67% -.8% -7.83% -6.7% -9.36% -4.8% 3.35%-4.79%-.9%-.6%-.75%.89% 53.5% 8.9% -.65%-5.3% -9.59% -.% -8.73% -.% -9.% -4.93%.76%-5.56%-.96% -8.96%-3.%.87% 46.6% 5.77% % 9.%.5% 8.33% 8.9%.8% 6.% 8.7% 5.9% 6.7% 7.43% 8.5% 5.77% 5.78% 7.76% 8.89% -4.39%-5.53%-4.9%-4.79%-.44%-.86%-.9%-36.3% 3.46%-5.5%-3.6%-8.3%-3.5% -6.7% 9.87% 6.85% 6.4% 3.% 6.8% 4.76% 9.77% 4.35% -.9% 7.4% 6.66%.%.46% 6.39% -5.% 8.3% 9.7% 47.3% 5.53% 8.53% 5.7% 39.55% 5.% 36.% 9.% 43.7% 3.9% 6.6% 5.7% 44.7% 8.5% 34.39% 6.4% 4.38% % -.5% -.6% -.3% -.3% -6.5% 6.49% 6.43% -4.55%-3.9%-3.93% 8.7% 9.57% 8.64%.3% -9.9% -.9%-3.68%-.6%-.84%-.7% -7.33% 6.3% -4.8%-3.57%-4.79% 6.6% 8.66% 7.33%.% % 8.93% 9.4% 8.75% 8.3% 49.37% 5.97% 3.4% 7.% 5.34% 6.6% 6.93% 5.7% 5.79% 6.7% -3.74%-5.4%-5.86%-3.79%-4.77%-6.38%-3.% -.7% -5.93%-3.3%-3.99% 9.66%.6%.% 4.5% 3.97% 3.55% 8.5% 8.45% 5.85% 473.% 54.%.99% 7.%.37%.84% 35.99% 3.6%.86% 38.5% 55.7% 48.96% 44.% 4.4% 4.6% 499.6% 77.4% 3.7% 33.5% 5.69% 6.83% 6.33% 3.%.85% 34.% Level T * -6.39* -5.86* -7.5* -6.55* -.9* -5.43* -6.57* -7.44* -5.58* * -5.84* -3.88* T Level T * -4.7* -5.95* -6.9* -7.43* * -5.3* -5.34* -6.* -5.34* * -6.5* T

38 3 D5 D5 D53 D55 D56 D58 D56D57 D59 D5D55 D58 D4688 D4689 D469 D469 D469 D4693 D4695 D4698 D4699 D47 D5 D5.746 D D D D D D D D D D D D D D D D D D D D

39 4 98/6/3 98/9/3 98//3 99/3/3 99/6/3 99/9/3 99//3 /3/3 /6/3 /9/3 //3 /3/3 /6/3 /9/ *

40

41 5 The REG Procedure Model: MODEL Dependen Variable: Y Y Forward Selecion: Sep Variable GM Enered: R-Square =.337 and C(p) = Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr > F Model <. Error Correced Toal Parameer Sandard Variable Esimae Error Type II SS F Value Pr > F Inercep E E-6.. GM <. Bounds on condiion number:, Forward Selecion: Sep Variable JYR Enered: R-Square =.454 and C(p) = Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr > F Model <. Error Correced Toal Parameer Sandard Variable Esimae Error Type II SS F Value Pr > F Inercep 5.967E E-7.. GM <. JYR <. Bounds on condiion number:.4,

42 Forward Selecion: Sep 3 Variable FH Enered: R-Square =.487 and C(p) =.365 Model <. Error Correced Toal Parameer Sandard Variable Esimae Error Type II SS F Value Pr > F Inercep GM <. JYR <. FH <. Bounds on condiion number:.79,.95 Forward Selecion: Sep 4 Variable JZD Enered: R-Square =.4949 and C(p) = 5.79 Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr > F Model <. Error Correced Toal Parameer Sandard Variable Esimae Error Type II SS F Value Pr > F Inercep GM <. JYR <. FH <. JZD Bounds on condiion number:.437,

43 Forward Selecion: Sep 5 Variable IDX Enered: R-Square =.4985 and C(p) = Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr > F Model <. Error Correced Toal Parameer Sandard Variable Esimae Error Type II SS F Value Pr > F Inercep IDX GM <. JYR <. FH <. JZD Bounds on condiion number:.996, 3.86 Forward Selecion: Sep 6 Variable V Enered: R-Square =.53 and C(p) = 6.4 Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr > F Model <. Error Correced Toal Parameer Sandard Variable Esimae Error Type II SS F Value Pr > F Inercep IDX GM <. JYR <. FH <. JZD V Bounds on condiion number:.77, 5.46 No oher variable me he.5 significance level for enry ino he model

44 6 6 EGARCH c (6) P P P P P P P P P P R(-) V V V C RES /SQR[GARCH]() RES/SQR[GARCH]() RES /SQR[GARCH](). RES/SQR[GARCH](). RES /SQR[GARCH](3). RES/SQR[GARCH](3). EGARCH() EGARCH() EGARCH(3)..7. T V V V

45 7 EGARCH () P P P P P P P P P P R(-) V V V C RES /SQR[GARCH]() RES/SQR[GARCH]() RES /SQR[GARCH]() RES/SQR[GARCH]() RES /SQR[GARCH](3) RES/SQR[GARCH](3) EGARCH() EGARCH() EGARCH(3) T V V V

46 8 EGARCH () P P P P P P P P P P R(-) V V V C RES /SQR[GARCH]() RES/SQR[GARCH]() RES /SQR[GARCH]() RES/SQR[GARCH]() RES /SQR[GARCH](3).7 RES/SQR[GARCH](3) -.7 EGARCH() EGARCH() EGARCH(3).5 T V V V

47 9 Bea 53 4,85, % ,968, % ,95, % ,99, %.74E ,957, % ,869, % ,533, % ,63, % ,899, % ,547, % bea 4 7,9, % ,8, % ,79, % ,53, % ,633,6.4 3.% ,76, % ,4,. 3.5% ,366, % ,95, % ,9, % Bea 68 35,936, ,7, ,49, ,65, ,946, ,47, ,584, ,95, ,3, ,6,

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