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: . ., . ., . . . , .

Si = ln

Y (ti ) Y (ti -1 )

(1)

Y(ti) ­ ti, Y(ti-1) ­ ti-1 [1] : - (NYSE), (AMEX), (NASDAQ) 1994 1995: 4·107 .
vi = < ( Si (t )- < Si (t ) >) 2 >

(2)

g

P( g ) ~ g

-

(3)

P(g) , ( , g). Si 3.1±0.03, = 2.84±0.12, g . Si 15- [1]. S&P500 t [2]. . (i) 1200000 1 13 1984-1996 , (ii) - 8686 35- 1962-1996 , (iii) - 852 71- 1926-1996. (1) t, t - 1 . , S&P500 t < 4 ~1560 3. -


t = 16 , [2] . ­ NIKKEI 225 Hang Seng [2]. 2 . (iv) 3560 NIKKEI 14- 1984-1997 , (v) - 4649 Hang Seng 18- 1980-1997 . t = 4 , () . t = 3.05 NIKKEI = 3.03 Hang-Seng. [3,4]. , - [3], 1 . . S&P500 4 [4]. (2) ~ t-0.3 [4]: . . . , 1- 2007 ., 7 , S&P500 [3,4]. .1:
autocorr
Data: Data1_B Model: ExpDecay1 Equation: y = y0 + A1*exp(-(x-x0)/t1) W eighting: y No weighting Chi^2/DoF = 0.00018 R^2 = 0.96505 y0 x0 A1 t1 0 ±0 0 ±0 0.19331 13.91821

RTS autocorrelation coefficient

0,1

0,01

±0.01002 ±1.95868

correlation time about 7 min

1E-3 0 20 40 60 80 100

steps N

. 1. . ­ , ­ . X , ­ . .


. 2007 . , . , [5]. , ( ) [6]. . ( 1 ) x ~ x-4, S&P500, NIKKEI Hang Seng [2]. .2 :
1

RTS return cumulative distributions

0,1

0,01

0,1

1

10

normalized return

.2. , . ­ , ­ , ­ 15. , ­ , - , ­ , - , ­ , .

, [2]. «» x-3.42 1- x-2.17 . , , (. [1] SP500 [3]) ­ . .2, . ~ x-4, - [1] , -


NYSE. . .3 ( , , .. 1-., 15-., ..):
neg1m pos1m neg15m pos15m neg60m pos60m neg1d pos1d

1

Sberbank return cumulatuve distributions

0,1

0,01

1E-3

1E-4 0,1 1 10

normalized return

.3. , . ­ , ­ , ­ 15-. , ­ , - , ­ , - , ­ . x-2.14, - x-3.14.

. «» x-2.14 15-. x-3.14 1-. , .4. ( ). , ( ) . , .5.
B (t ) ~ t
-

= 0.27±0.03, [4] S&P500. , .


1

GMKN return cumulative distributions

0,1

neg1m pos1m neg15m pos15m neg60m pos60m

0,01

1E-3

1E-4

1E-5 0,1 1 10

normalized return

.4. , . ­ , ­ , ­ 15-. , ­ , - , ­ . x-2.16, - x-3.33.
dayvolatilitycorr
1
Data: Data1_volatilitycorr Model: Allometric1 Equation: y = a*x^b Weighting: y No weighting Chi^2/DoF = 0.00075 R^2 = 0.93536 a b 0.35403 -0.26651 ±0.02018 ±0.03033

volatility correlation coefficient

0,1

1

10

100

days

.5. .

. 1) (, [1,2]). [6].


2) . , S&P500. , -, . 3) , S&P500 [4].
1. Gopikrishnan P., Meyer M., Amaral L.A.N., and Stanley H.E. Inverse cubic law for the distribution of stock price variations// Eur. Phys. J. B. 1998. V.3. P.139-140. 2. Gopikrishnan P., Plerou V., Amaral L.A.N., Meyer M., and Stanley H.E. Scaling of the distribution of fluctuations of financial market indices // Phys. Rev. E. 1999. V.60, No.5. P.5305-5315. 3. Mantegna R.N., Stanley H.E. An Introduction to Econophysics. Correlation and Complexity in Finance. NY: Cambrige University Press, 2000. 156 p. 4. Y.Liu, P.Gopikrishnan, P.Cizeau, M.Meyer, C-K.Peng, and H. Eugene Stanley. Statistical properties of the volatility of price fluctuations// Phys.Rev.E. 1999. V.60. P.1390-1400. 5. Fama E.F. Efficient Capital Markets: A Review of Theory and Empirical Work // J.Finance. 1970. V.25. P.383-417. 6. .., .. . .- .: , 2007. 280 .

RETURN OF RUSSIAN STOCK MARKET ASSETS: CORRELATIONS AND DISTRIBUTIONS Vidov P. V., Zhukov I. A., Romanovsky M. Y.

Autocorrelations and return distributions of stocks and stock indexes of Russian stock market were investigated experimentally. It was shown that autocorrelations and return distributions of Russian assets are similar to corresponding values of international assets