6,406 research outputs found
Dynamic evolution of cross-correlations in the Chinese stock market
We study the dynamic evolution of cross-correlations in the Chinese stock
market mainly based on the random matrix theory (RMT). The correlation matrices
constructed from the return series of 367 A-share stocks traded on the Shanghai
Stock Exchange from January 4, 1999 to December 30, 2011 are calculated over a
moving window with a size of 400 days. The evolutions of the statistical
properties of the correlation coefficients, eigenvalues, and eigenvectors of
the correlation matrices are carefully analyzed. We find that the stock
correlations are significantly increased in the periods of two market crashes
in 2001 and 2008, during which only five eigenvalues significantly deviate from
the random correlation matrix, and the systemic risk is higher in these
volatile periods than calm periods. By investigating the significant
contributors of the deviating eigenvectors in different moving windows, we
observe a dynamic evolution behavior in business sectors such as IT,
electronics, and real estate, which lead the rise (drop) before (after) the
crashes
Scaling in the distribution of intertrade durations of Chinese stocks
The distribution of intertrade durations, defined as the waiting times
between two consecutive transactions, is investigated based upon the limit
order book data of 23 liquid Chinese stocks listed on the Shenzhen Stock
Exchange in the whole year 2003. A scaling pattern is observed in the
distributions of intertrade durations, where the empirical density functions of
the normalized intertrade durations of all 23 stocks collapse onto a single
curve. The scaling pattern is also observed in the intertrade duration
distributions for filled and partially filled trades and in the conditional
distributions. The ensemble distributions for all stocks are modeled by the
Weibull and the Tsallis -exponential distributions. Maximum likelihood
estimation shows that the Weibull distribution outperforms the -exponential
for not-too-large intertrade durations which account for more than 98.5% of the
data. Alternatively, nonlinear least-squares estimation selects the
-exponential as a better model, in which the optimization is conducted on
the distance between empirical and theoretical values of the logarithmic
probability densities. The distribution of intertrade durations is Weibull
followed by a power-law tail with an asymptotic tail exponent close to 3.Comment: 16 elsart pages including 3 eps figure
Intraday pattern in bid-ask spreads and its power-law relaxation for Chinese A-share stocks
We use high-frequency data of 1364 Chinese A-share stocks traded on the
Shanghai Stock Exchange and Shenzhen Stock Exchange to investigate the intraday
patterns in the bid-ask spreads. The daily periodicity in the spread time
series is confirmed by Lomb analysis and the intraday bid-ask spreads are found
to exhibit -shaped pattern with idiosyncratic fine structure. The intraday
spread of individual stocks relaxes as a power law within the first hour of the
continuous double auction from 9:30AM to 10:30AM with exponents
for the Shanghai market and
for the Shenzhen market. The power-law
relaxation exponent of individual stocks is roughly normally
distributed. There is evidence showing that the accumulation of information
widening the spread is an endogenous process.Comment: 12 Elsart pages including 7 eps figure
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