2 research outputs found
An empirical behavioural order-driven model with price limit rules
We develop an empirical behavioural order-driven (EBOD) model, which consists
of an order placement process and an order cancellation process. Price limit
rules are introduced in the definition of relative price. The order placement
process is determined by several empirical regularities: the long memory in
order directions, the long memory in relative prices, the asymmetric
distribution of relative prices, and the nonlinear dependence of the average
order size and its standard deviation on the relative price. Order cancellation
follows a Poisson process with the arrival rate determined from real data and
the cancelled order is determined according to the empirical distributions of
relative price level and relative position at the same price level. All these
ingredients of the model are derived based on the empirical microscopic
regularities in the order flows of stocks on the Shenzhen Stock Exchange. The
model is able to produce the main stylized facts in real markets. Computational
experiments uncover that asymmetric setting of price limits will cause the
stock price diverging exponentially when the up price limit is higher than the
down price limit and vanishing vice versus. We also find that asymmetric price
limits have influences on stylized facts. Our EBOD model provides a suitable
computational experiment platform for academics, market participants and policy
makers.Comment: 19 pages, 8 figures and 7 table
Multifractal analysis of financial markets
Multifractality is ubiquitously observed in complex natural and socioeconomic
systems. Multifractal analysis provides powerful tools to understand the
complex nonlinear nature of time series in diverse fields. Inspired by its
striking analogy with hydrodynamic turbulence, from which the idea of
multifractality originated, multifractal analysis of financial markets has
bloomed, forming one of the main directions of econophysics. We review the
multifractal analysis methods and multifractal models adopted in or invented
for financial time series and their subtle properties, which are applicable to
time series in other disciplines. We survey the cumulating evidence for the
presence of multifractality in financial time series in different markets and
at different time periods and discuss the sources of multifractality. The
usefulness of multifractal analysis in quantifying market inefficiency, in
supporting risk management and in developing other applications is presented.
We finally discuss open problems and further directions of multifractal
analysis.Comment: A review paper contains 145 page