For both stock and currency markets, we study the return intervals tau between the daily volatilities of the price changes that are above a certain threshold q. We find that the distribution function Pq(tau) scales with the mean return interval tau as Pq(tau)=tau(-1)f(tau/tau). The scaling function fx is similar in form for all seven stocks and for all seven currency databases analyzed, and fx ...

This study tests the presence of the day of the week effect on stock market volatility by using the S&P 500 market index during the period of January 1973 and October 1997. The findings show that the day of the week effect is present in both volatility and return equations. While the highest and lowest returns are observed on Wednesday and Monday, the highest and the lowest volatility are obser...

Business Intelligence (BI) systems support decision-making and information-sharing within increasingly complex organizational environments. As investment in these systems is steadily increasing in a wide range of industries, it is important to understand the effect they have on companies. We claim that BI systems impact companies in two important ways: 1) they stabilize the decision-making proc...

This paper examines the volatility dynamics of the greater China stock markets (Shanghai Aand Bshares, Shenzhen Aand B-shares, Taiwan, and Hong Kong) by employing a multivariate (tetravariate) framework that incorporates the features of asymmetries, persistence, and time-varying correlations, which are typically observed in stock markets of developed economies. Specifically, we introduce two ne...

This paper aims to determine the impact of dividend policy on stock price volatility by taking firms listed on Tehran stock exchange.  A sample of 68 listed companies from Tehran stock exchange is examined for a period from 2001 to 2012.  The estimation is based on cross-sectional ordinary least square regression analysis to find the relationship between share price volatility and dividend poli...

This is a project on modeling time-varying volatility of S&P 500 weely return for the years 1990 to 2012 using Bayesian methods. First, MCMC on the log-stochastic volatility (SV) model is implemented with simulation results analyzed. Second, I generalize the SV model to encompass regime-switching properties with the markov switching log-stochastic volatility (MSSV) model, under which, high-vola...

We document a positive relation between the volatility of liquidity and expected returns. Our measure of liquidity is based on Amihud (2002) and its volatility is measured using daily data. We show that the volatility of liquidity effect is different from previously documented liquidity risks: the covariance of stock returns with aggregate liquidity, the covariance of stock liquidity with aggre...

We perform return interval analysis of 1-min realized volatility defined by the sum of absolute high-frequency intraday returns for the Shanghai Stock Exchange Composite Index (SSEC) and 22 constituent stocks of SSEC. The scaling behavior and memory effect of the return intervals between successive realized volatilities above a certain threshold q are carefully investigated. In comparison with ...

In this article, I explicitly solve dynamic portfolio choice problems, up to the solution of an ordinary differential equation (ODE), when the asset returns are quadratic and the agent has a constant relative risk aversion (CRRA) coefficient. My solution includes as special cases many existing explicit solutions of dynamic portfolio choice problems. I also present three applications that are no...

Complex systems attract many researchers from various scientific fields. Financial markets are one of these widely studied complex systems. Statistical physics, which was originally developed to study large systems, provides novel ideas and powerful methods to analyze financial markets. The study of financial fluctuations characterizes market behavior, and helps to better understand the underly...