59 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
Limitations of portfolio diversification through fat tails of the return Distributions: Some empirical evidence
This study investigates the level of risk due to fat tails of the return distribution and the changes of tail fatness (TF) through portfolio diversification. TF is not eliminated through portfolio diversification, and, interestingly, the positive tail has declining fatness until a certain level is reached, while the negative tail has rising fatness. This indicates that fat tails are highly relevant to common factors on systematic risk and that the relevance of common factors is higher for the negative tail compared to the positive tail. In the portfolio diversification effect, the declining fatness of the positive tail further reduces risk, but the rising fatness of the negative tail does not contribute to this effect. The asymmetry between the fatness of the positive and negative tails in the return distribution corresponds to the asymmetry of the trade-off relationship between loss avoidance and profit sacrifice that is expected as a consequence of portfolio diversification. Investors use portfolio diversification to reduce their risk of suffering high losses, but following this strategy means sacrificing high-profit potential. Our study provides empirical confirmation for the practical limitation of portfolio diversification and explains why investors with diversified portfolios suffer high losses from market crashes. An examination of the Northeast Asian stock markets of China, Japan, Korea, and Taiwan show identical results
Statistical properties of absolute log-returns and a stochastic model of stock markets with heterogeneous agents
This paper is intended as an investigation of the statistical properties of
{\it absolute log-returns}, defined as the absolute value of the logarithmic
price change, for the Nikkei 225 index in the 28-year period from January 4,
1975 to December 30, 2002. We divided the time series of the Nikkei 225 index
into two periods, an inflationary period and a deflationary period. We have
previously [18] found that the distribution of absolute log-returns can be
approximated by the power-law distribution in the inflationary period, while
the distribution of absolute log-returns is well described by the exponential
distribution in the deflationary period.\par To further explore these empirical
findings, we have introduced a model of stock markets which was proposed in
[19,20]. In this model, the stock market is composed of two groups of traders:
{\it the fundamentalists}, who believe that the asset price will return to the
fundamental price, and {\it the interacting traders}, who can be noise traders.
We show through numerical simulation of the model that when the number of
interacting traders is greater than the number of fundamentalists, the
power-law distribution of absolute log-returns is generated by the interacting
traders' herd behavior, and, inversely, when the number of fundamentalists is
greater than the number of interacting traders, the exponential distribution of
absolute log-returns is generated.Comment: 12 pages, 5 figure
Financial Time Series Analysis of SV Model by Hybrid Monte Carlo
We apply the hybrid Monte Carlo (HMC) algorithm to the financial time sires
analysis of the stochastic volatility (SV) model for the first time. The HMC
algorithm is used for the Markov chain Monte Carlo (MCMC) update of volatility
variables of the SV model in the Bayesian inference. We compute parameters of
the SV model from the artificial financial data and compare the results from
the HMC algorithm with those from the Metropolis algorithm. We find that the
HMC decorrelates the volatility variables faster than the Metropolis algorithm.
We also make an empirical analysis based on the Yen/Dollar exchange rates.Comment: 8 pages, 3 figures, to be published in LNC
An Adaptive Markov Chain Monte Carlo Method for GARCH Model
We propose a method to construct a proposal density for the
Metropolis-Hastings algorithm in Markov Chain Monte Carlo (MCMC) simulations of
the GARCH model. The proposal density is constructed adaptively by using the
data sampled by the MCMC metho d itself. It turns out that autocorrelations
between the data generated with our adaptive proposal density are greatly
reduced. Thus it is concluded that the adaptive construction method is very
efficient and works well for the MCMC simulations of the GARCH model.Comment: 11 pages, 6 figure
Statistical mixing and aggregation in Feller diffusion
We consider Feller mean-reverting square-root diffusion, which has been
applied to model a wide variety of processes with linearly state-dependent
diffusion, such as stochastic volatility and interest rates in finance, and
neuronal and populations dynamics in natural sciences. We focus on the
statistical mixing (or superstatistical) process in which the parameter related
to the mean value can fluctuate - a plausible mechanism for the emergence of
heavy-tailed distributions. We obtain analytical results for the associated
probability density function (both stationary and time dependent), its
correlation structure and aggregation properties. Our results are applied to
explain the statistics of stock traded volume at different aggregation scales.Comment: 16 pages, 3 figures. To be published in Journal of Statistical
Mechanics: Theory and Experimen
Volatility clustering and scaling for financial time series due to attractor bubbling
A microscopic model of financial markets is considered, consisting of many
interacting agents (spins) with global coupling and discrete-time thermal bath
dynamics, similar to random Ising systems. The interactions between agents
change randomly in time. In the thermodynamic limit the obtained time series of
price returns show chaotic bursts resulting from the emergence of attractor
bubbling or on-off intermittency, resembling the empirical financial time
series with volatility clustering. For a proper choice of the model parameters
the probability distributions of returns exhibit power-law tails with scaling
exponents close to the empirical ones.Comment: For related publications see http://www.helbing.or
Information flow between stock indices
Using transfer entropy, we observed the strength and direction of information
flow between stock indices. We uncovered that the biggest source of information
flow is America. In contrast, the Asia/Pacific region the biggest is receives
the most information. According to the minimum spanning tree, the GSPC is
located at the focal point of the information source for world stock markets
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