57 research outputs found
Modeling the Epps effect of cross correlations in asset prices
We review the decomposition method of stock return cross-correlations,
presented previously for studying the dependence of the correlation coefficient
on the resolution of data (Epps effect). Through a toy model of random
walk/Brownian motion and memoryless renewal process (i.e. Poisson point
process) of observation times we show that in case of analytical treatability,
by decomposing the correlations we get the exact result for the frequency
dependence. We also demonstrate that our approach produces reasonable fitting
of the dependence of correlations on the data resolution in case of empirical
data. Our results indicate that the Epps phenomenon is a product of the finite
time decay of lagged correlations of high resolution data, which does not scale
with activity. The characteristic time is due to a human time scale, the time
needed to react to news.Comment: to appear in the Proceedings of SPIE Fluctuations and Noise 200
The Epps effect revisited
We analyse the dependence of stock return cross-correlations on the sampling
frequency of the data known as the Epps effect: For high resolution data the
cross-correlations are significantly smaller than their asymptotic value as
observed on daily data. The former description implies that changing trading
frequency should alter the characteristic time of the phenomenon. This is not
true for the empirical data: The Epps curves do not scale with market activity.
The latter result indicates that the time scale of the phenomenon is connected
to the reaction time of market participants (this we denote as human time
scale), independent of market activity. In this paper we give a new description
of the Epps effect through the decomposition of cross-correlations. After
testing our method on a model of generated random walk price changes we justify
our analytical results by fitting the Epps curves of real world data.Comment: 23 pages, 10 figures, 2 tables; added references, added figures and
statistical details, extended overview on literatur
Segmentation algorithm for non-stationary compound Poisson processes
We introduce an algorithm for the segmentation of a class of regime switching
processes. The segmentation algorithm is a non parametric statistical method
able to identify the regimes (patches) of the time series. The process is
composed of consecutive patches of variable length, each patch being described
by a stationary compound Poisson process, i.e. a Poisson process where each
count is associated to a fluctuating signal. The parameters of the process are
different in each patch and therefore the time series is non stationary. Our
method is a generalization of the algorithm introduced by Bernaola-Galvan, et
al., Phys. Rev. Lett., 87, 168105 (2001). We show that the new algorithm
outperforms the original one for regime switching compound Poisson processes.
As an application we use the algorithm to segment the time series of the
inventory of market members of the London Stock Exchange and we observe that
our method finds almost three times more patches than the original one.Comment: 11 pages, 11 figure
The value of information in a multi-agent market model
We present an experimental and simulated model of a multi-agent stock market
driven by a double auction order matching mechanism. Studying the effect of
cumulative information on the performance of traders, we find a non monotonic
relationship of net returns of traders as a function of information levels,
both in the experiments and in the simulations. Particularly, averagely
informed traders perform worse than the non informed and only traders with high
levels of information (insiders) are able to beat the market. The simulations
and the experiments reproduce many stylized facts of stock markets, such as
fast decay of autocorrelation of returns, volatility clustering and fat-tailed
distribution of returns. These results have an important message for everyday
life. They can give a possible explanation why, on average, professional fund
managers perform worse than the market index.Comment: 11 pages, 5 figures, published in EPJ
The value of information in a multi-agent market model
We present an experimental and simulated model of a multi-agent stock market driven by a double auction order matching mechanism. Studying the effect of cumulative information on the performance of traders, we find a non monotonic relationship of net returns of traders as a function of information levels, both in the experiments and in the simulations. Particularly, averagely informed traders perform worse than the non informed and only traders with high levels of information (insiders) are able to beat the market. The simulations and the experiments reproduce many stylized facts of stock markets, such as fast decay of autocorrelation of returns, volatility clustering and fat-tailed distribution of returns. These results have an important message for everyday life. They can give a possible explanation why, on average, professional fund managers perform worse than the market index.Economics; econophysics; financial markets; business and management; information theory and communication theory
How does the market react to your order flow?
We present an empirical study of the intertwined behaviour of members in a
financial market. Exploiting a database where the broker that initiates an
order book event can be identified, we decompose the correlation and response
functions into contributions coming from different market participants and
study how their behaviour is interconnected. We find evidence that (1) brokers
are very heterogeneous in liquidity provision -- some are consistently
liquidity providers while others are consistently liquidity takers. (2) The
behaviour of brokers is strongly conditioned on the actions of {\it other}
brokers. In contrast brokers are only weakly influenced by the impact of their
own previous orders. (3) The total impact of market orders is the result of a
subtle compensation between the same broker pushing the price in one direction
and the liquidity provision of other brokers pushing it in the opposite
direction. These results enforce the picture of market dynamics being the
result of the competition between heterogeneous participants interacting to
form a complicated market ecology.Comment: 22 pages, 5+9 figure
Why is order flow so persistent?
Order flow in equity markets is remarkably persistent in the sense that order
signs (to buy or sell) are positively autocorrelated out to time lags of tens
of thousands of orders, corresponding to many days. Two possible explanations
are herding, corresponding to positive correlation in the behavior of different
investors, or order splitting, corresponding to positive autocorrelation in the
behavior of single investors. We investigate this using order flow data from
the London Stock Exchange for which we have membership identifiers. By
formulating models for herding and order splitting, as well as models for
brokerage choice, we are able to overcome the distortion introduced by
brokerage. On timescales of less than a few hours the persistence of order flow
is overwhelmingly due to splitting rather than herding. We also study the
properties of brokerage order flow and show that it is remarkably consistent
both cross-sectionally and longitudinally.Comment: 42 pages, 15 figure
- …