708,532 research outputs found
Volatility return intervals analysis of the Japanese market
We investigate scaling and memory effects in return intervals between price
volatilities above a certain threshold for the Japanese stock market using
daily and intraday data sets. We find that the distribution of return intervals
can be approximated by a scaling function that depends only on the ratio
between the return interval and its mean . We also find memory
effects such that a large (or small) return interval follows a large (or small)
interval by investigating the conditional distribution and mean return
interval. The results are similar to previous studies of other markets and
indicate that similar statistical features appear in different financial
markets. We also compare our results between the period before and after the
big crash at the end of 1989. We find that scaling and memory effects of the
return intervals show similar features although the statistical properties of
the returns are different.Comment: 11 page
Financial factor influence on scaling and memory of trading volume in stock market
We study the daily trading volume volatility of 17,197 stocks in the U.S.
stock markets during the period 1989--2008 and analyze the time return
intervals between volume volatilities above a given threshold q. For
different thresholds q, the probability density function P_q(\tau) scales with
mean interval as P_q(\tau)=^{-1}f(\tau/) and the tails of
the scaling function can be well approximated by a power-law f(x)~x^{-\gamma}.
We also study the relation between the form of the distribution function
P_q(\tau) and several financial factors: stock lifetime, market capitalization,
volume, and trading value. We find a systematic tendency of P_q(\tau)
associated with these factors, suggesting a multi-scaling feature in the volume
return intervals. We analyze the conditional probability P_q(\tau|\tau_0) for
following a certain interval \tau_0, and find that P_q(\tau|\tau_0)
depends on \tau_0 such that immediately following a short/long return interval
a second short/long return interval tends to occur. We also find indications
that there is a long-term correlation in the daily volume volatility. We
compare our results to those found earlier for price volatility.Comment: 17 pages, 6 figure
Return interval distribution of extreme events and long term memory
The distribution of recurrence times or return intervals between extreme
events is important to characterize and understand the behavior of physical
systems and phenomena in many disciplines. It is well known that many physical
processes in nature and society display long range correlations. Hence, in the
last few years, considerable research effort has been directed towards studying
the distribution of return intervals for long range correlated time series.
Based on numerical simulations, it was shown that the return interval
distributions are of stretched exponential type. In this paper, we obtain an
analytical expression for the distribution of return intervals in long range
correlated time series which holds good when the average return intervals are
large. We show that the distribution is actually a product of power law and a
stretched exponential form. We also discuss the regimes of validity and perform
detailed studies on how the return interval distribution depends on the
threshold used to define extreme events.Comment: 8 pages, 6 figure
Scaling and memory of intraday volatility return intervals in stock market
We study the return interval between price volatilities that are above
a certain threshold for 31 intraday datasets, including the Standard &
Poor's 500 index and the 30 stocks that form the Dow Jones Industrial index.
For different threshold , the probability density function
scales with the mean interval as
, similar to that found in daily
volatilities. Since the intraday records have significantly more data points
compared to the daily records, we could probe for much higher thresholds
and still obtain good statistics. We find that the scaling function is
consistent for all 31 intraday datasets in various time resolutions, and the
function is well approximated by the stretched exponential, , with and , which indicates the
existence of correlations. We analyze the conditional probability distribution
for following a certain interval , and find
depends on , which demonstrates memory in intraday
return intervals. Also, we find that the mean conditional interval
increases with , consistent with the memory found for
. Moreover, we find that return interval records have long
term correlations with correlation exponents similar to that of volatility
records.Comment: 19 pages, 8 figure
Finite-Size Effects on Return Interval Distributions for Weakest-Link-Scaling Systems
The Weibull distribution is a commonly used model for the strength of brittle
materials and earthquake return intervals. Deviations from Weibull scaling,
however, have been observed in earthquake return intervals and in the fracture
strength of quasi-brittle materials. We investigate weakest-link scaling in
finite-size systems and deviations of empirical return interval distributions
from the Weibull distribution function. We use the ansatz that the survival
probability function of a system with complex interactions among its units can
be expressed as the product of the survival probability functions for an
ensemble of representative volume elements (RVEs). We show that if the system
comprises a finite number of RVEs, it obeys the -Weibull distribution.
We conduct statistical analysis of experimental data and simulations that show
good agreement with the -Weibull distribution. We show the following:
(1) The weakest-link theory for finite-size systems involves the
-Weibull distribution. (2) The power-law decline of the
-Weibull upper tail can explain deviations from the Weibull scaling
observed in return interval data. (3) The hazard rate function of the
-Weibull distribution decreases linearly after a waiting time , where is the Weibull modulus and is the system size
in terms of representative volume elements. (4) The -Weibull provides
competitive fits to the return interval distributions of seismic data and of
avalanches in a fiber bundle model. In conclusion, using theoretical and
statistical analysis of real and simulated data, we show that the
-Weibull distribution is a useful model for extreme-event return
intervals in finite-size systems.Comment: 33 pages, 11 figure
Short-term returns and the predictability of Finnish stock returns
The predictability of Finnish stock returns is studied using the framework of Ferson and Harvey (1993). We use a conditional asset pricing model where risk premia and risk sensitivities are conditioned on a range of financial information variables. In particular, we study the effect of the return interval on the predictability of short-term stock returns. Using daily, weekly, and monthly Finnish size and industry-sorted portfolio returns, we find that the predictability of returns increases with the length of return interval, but so does the power of the conditional pricing model to explain the predictability. Consistent with earlier results, we report that the time variation in risk premium accounts for most of the predictability. However, the results show also there is a sizable positive interaction between beta and risk premium which seems to increase for smaller companies.asset pricing; predictability; return interval; time aggregation
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