( ) 2017 3 ( 205 ), 210023,, ( MF-DFA), ( MM-DCCA),, Hurst, ;, ; ; ; ; F224. 9 F830. 9 A 1672-6049 2017 03-0077-08 2003 1 2 Fang 1 GARCH 1. 1 1998 2004 Chuliá 2 GARCH 3 Copula-GARCH 4 SVAR GARCH-M 5 DVAR Wald LR 6 2017-02-16 12YJAZH020 ZWFXT14001 KYLX16_1337 1993 1963 77
, 7 DCC-MVGARCH GARCH 90 Peters 8 9 10 11 12 500 Sukpitak 13 DFA Ma 14 DCCA Zhou 15 MF-DCCA Shi 16 MF-DCCA MM-DCCA 17-19 MM-DCCA MM-DCCA Shi 16 MF-DCCA 15 x i y i i = 1 2 N. N X i = i k = 1 x k - x Y i = i y k - y i = 1 2 N k = 1 x = 1 x k y = 1 y k. N N k = 1 N N k = 1 X i Y i s N s = int N /s N s X i Y i 2N s 2N s v v = 1 2 2N s X i Y i v P v i T v i ν = 1 2 N s F 2 s ν = 1 s s X ν - 1 s + i - P ν i Y ν - 1 s + i - T ν i i = 1 ν = N s + 1 N s + 2 2N s 78
JOURNAL OF NANJING UNIVERSITY OF FINANCE AND ECONOMICS F 2 s ν = 1 s s X N - ν - 1 s + i - P ν i Y N - ν - 1 s + i - T ν i. i = 1 q F q s = F 0 s = exp 2N s 1 { 2N s [ F 2 s ν ] q / 2 } 1 /q ν = 1 2N s 1 { 4N s ln [ F 2 s ν ] } ν = 1 q 0 q = 0 q q = 2 DCCA 20 q F q s s log-log x i y i F q s ~ s H xy q H xy q MF-DCCA x i y i MF-DCCA MF-DFA 21 H xy q Hurst H q s q F q s H xy q s Hurst H xy q s q s H xy q H xy q s Hurst s = a + b 2 s a b MF-DCCA H xy q s q H xy q s q H xy q s q H xy q s < 0. 5 H xy q s = 0. 5 H xy q s > 0. 5 H xy q s ( ) 1990 12 1234 9518 30 2003 2 24 2016 10 17 3315 http / /money. 163. com / 1 x t g t = log x t - log x t - 1 79
, 1 R t = g t - g t /σ g t σ g t 2 2 2 0 0. 1677 0. 0631 0. 3032 1 ( ) Kantelhard 21 2002 MF-DFA MF-DFA 3 q - 5-4 4 5 q q Hurst H q 3 Hurst MF-DFA 3-5 q 5 Hurst H q q q H q Hurst H q 80
JOURNAL OF NANJING UNIVERSITY OF FINANCE AND ECONOMICS 3 Hurst Hurst H q 0. 5 Hurst 0. 5 Hurst 0. 5 4 F q ( s) 4 F q s q - 5 5 F q s MM-DCCA H xy q s 10 50 s 120 600 H xy q 5 MM- DCCA Hurst H xy q s 5 Hurst q s 0. 5 5 a H 5 b c 2014 7 7 2015 8 8 60% 80% 80% 81
, 5 MM-DCCA Hurst Hurst 5 b Hurst 5 c 5 a b c Hurst MM-DCCA MF-DFA Hurst H q q 82
JOURNAL OF NANJING UNIVERSITY OF FINANCE AND ECONOMICS MM-DCCA Hurst 1 FANG V LIN E LEE V. Volatility linkages and spillovers in stock and bond markets some international evidence J. Journal of international finance and economics 2007 7 1 1-10. 2 CHULI H TORR H. The economic value of volatility transmission between the stock and bond markets J. Journal of futures markets 2008 28 11 1066-1094. 3. D. 2012. 4. D. 2013. 5. J. 2008 4 9-13. 6. J. 2005 2 122-124. 7. D. 2014. 8 PETERS E E. Fractal market analysis applying chaos theory to investment and economics M. John Wiley & Sons 1994. 9 MA W J HU C K AMRITKAR R E. Stochastic dynamical model for stock-stock correlations J. Physical review E 2004 70 2 026101. 10. J. 2015 34 5 878-889. 11. J. 2016 1 57-63. 12. J. 2009 4 166-169. 13 SUKPITAK J HENGPUNYA V. Efficiency of thai stock markets detrended fluctuation analysis J. Physica A statistical mechanics and its applications 2016 458 204-209. 14 MA P LI D LI S. Efficiency and cross-correlation in equity market during global financial crisis evidence from China J. Physica A statistical mechanics and its applications 2016 444 163-176. 15 ZHOU W X. Multifractal detrended cross-correlation analysis for two non-stationary signals J. Physical review E 2008 77 66-211. 16 SHI W SHANG P WANG J et al. Multiscale multifractal detrended cross-correlation analysis of financial time series J. Physica A statistical mechanics and its applications 2014 403 35-44. 17 KIM H YIM K KIM S et al. Nonlinear properties of the Korea fund market J. Journal of the korean physical society 2015 67 12 2039-2044. 18 CAO G XU W. Nonlinear structure analysis of carbon and energy markets with MFDCCA based on maximum overlap wavelet transform J. Physica A statistical mechanics and its applications 2016 444 505-523. 19 SHI K LIU C Q AI N S. Monofractal and multifractal approaches in investigating temporal variation of air pollution indexes J. Fractals 2009 17 4 513-521. 20 PODOBNIK B STANLEY H E. Detrended cross-correlation analysis a new method for analyzing two nonstationary time series J. Physical review letters 2008 100 8 84-102. 83
, 21 KANTELHARDT J W ZSCHIEGNER S A KOSCIELNY-BUNDE E et al. Multifractal detrended fluctuation analysis of nonstationary time series J. Physica A statistical mechanics and its applications 2002 316 1 87-114. Multiscale multifractal analysis on the cross-correlation of the China securities markets WANG Tongtong WANG Hongyong School of Applied Mathematics Nanjing University of Finance and Economics Nanjing 210023 China Abstract The stock market the bond market and the fund market are three important components of financial markets and the relationship among these three markets has been paid expansive attention by the investors and the managers. In this paper we choose the return series of Shanghai Composite Index Bond Index and Fund Index as the research objects and use MF-DFA method to confirm the existence of multifractal features in the fluctuation of the three markets. Meanwhile we utilize the MM- DCCA method at multiple scales and generate the Hurst surface to better visualize the interaction between markets respectively. The empirical results show that the cross-correlation among three markets presents various fractal characteristics at different scales. Besides the correlation between the stock market and the fund market is stronger than that of the other two groups and the correlation between the stock market and the bond market is unstable. Key words stock market bond market fund market cross-correlation multiscale analysis 檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪 ( 40 ) An analysis of the factors influencing the capital utilization rate of China's up-scale industry LI Chunji School of Economics Nanjing University of Finance and Economics Nanjing 210023 China Abstract This paper builds a theory model to analyze the factors influencing industrial capital utilization rate based on the calculation of China's up-scale industry from 2001 to 2014. The conclusion of the theoretical model analysis is empirically tested. The theory analysis and the empirical test show the following results the output capital ratio and the rate of product sales of industry have a significant positive effect on the industry capital utilization rate the industry debt-interest rate has a significant negative effect the depreciation rate of capital also has certain negative effect but not significant the economic growth rate has a significant positive effect and the bank benchmark interest rate has a significant negative effect individually the national capital ratio and the loan size of industry have a significant positive effect but their interaction has a significant negative effect. Based on these results we suggest that the enterprises should pay efforts to expand their product sales invigorate the stock assets increase the productivity of capital for resolving excess of capacity and raising capital utilization rate and the government should provide fiscal and financial policy support and reduce the enterprise debt burden in the prevention and control of economic overheating. Key words capital utilization rate excess of capacity output capital ratio product sales rate 84