Abstract: The interdependences among stock market indices
were studied for a long while by academics in the entire world. The
current financial crisis opened the door to a wide range of opinions
concerning the understanding and measurement of the connections
considered to provide the controversial phenomenon of market
integration. Using data on the log-returns of 17 stock market indices
that include most of the CEE markets, from 2005 until 2009, our
paper studies the problem of these dependences using a new
methodological tool that takes into account both the volatility
clustering effect and the stochastic properties of these linkages
through a Dynamic Conditional System of Simultaneous Equations.
We find that the crisis is well captured by our model as it provides
evidence for the high volatility – high dependence effect.
Abstract: The aim of the article is extending and developing
econometrics and network structure based methods which are able to
distinguish price manipulation in Tehran stock exchange. The
principal goal of the present study is to offer model for
approximating price manipulation in Tehran stock exchange. In order
to do so by applying separation method a sample consisting of 397
companies accepted at Tehran stock exchange were selected and
information related to their price and volume of trades during years
2001 until 2009 were collected and then through performing runs
test, skewness test and duration correlative test the selected
companies were divided into 2 sets of manipulated and non
manipulated companies. In the next stage by investigating
cumulative return process and volume of trades in manipulated
companies, the date of starting price manipulation was specified and
in this way the logit model, artificial neural network, multiple
discriminant analysis and by using information related to size of
company, clarity of information, ratio of P/E and liquidity of stock
one year prior price manipulation; a model for forecasting price
manipulation of stocks of companies present in Tehran stock
exchange were designed. At the end the power of forecasting models
were studied by using data of test set. Whereas the power of
forecasting logit model for test set was 92.1%, for artificial neural
network was 94.1% and multi audit analysis model was 90.2%;
therefore all of the 3 aforesaid models has high power to forecast
price manipulation and there is no considerable difference among
forecasting power of these 3 models.