This paper revisits event-study methodology based on regression estimation of abnormal returns. The paper reviews the traditional event study and gives a more detailed discussion of the regression based approach with quantitative event variables. The paper discusses also briefly the the dummy variable regression which is a special case of the quantitative case. Use of GARCH to predict event per...

We simulate a series of daily returns from intraday price movements initiated by microstructure elements. Significant evidence is found that daily returns and daily return volatility exhibit first order autocorrelation, but trading volume and daily return volatility are not correlated, while intraday volatility is. We also consider GARCH effects in daily return series and show that estimates us...

This paper provides a new empirical guidance for modeling a skewed and fat-tailed error distribution underlying the traditional GARCH models for equity returns based on empirical findings on Realized Volatility (RV), constructed from the summation of higher-frequency squared (demeaned) returns. Based on an 80-year sample of U.S. daily stock market returns, I find that the distribution of monthl...

Related commodity markets have two characteristics: (i) they may follow similar volatility processes; and (ii) such markets are frequently represented by a market aggregate or index. Indices are used to represent the performance and time series properties of a group of markets. An important issue regarding the time series properties of an index is how it reflects the time series properties of i...

This paper considers the pricing of options when there are jumps in the pricing kernel and correlated jumps in asset prices and volatilities. We extend theory developed by Nelson (1990) and Duan (1997) by considering limiting models for our resulting approximating GARCH-Jump process. Limiting cases of our processes consist of models where both asset price and local volatility follow jump diffus...

This paper investigates the asymmetry in volatility of returns for the Iranian stock market using the daily closing values of the Tehran exchange price index (TEPIX) covering the period from March 25, 2001 to July 25, 2012, with a total of 2743 observations. To this end, two sets of tests have been employed: the first set is based on the residuals derived from a symmetric GARCH (1,1) model. The...

1. Methods and application Several studies in empirical finance literature have highlighted the importance of allowing for skewness, tail-fatness, non normality of returns for asset allocation and pricing models. Moreover, the dependence between returns, that can impact portfolio decisions, often exhibits nonlinear structures and asymmetric extremal behavior that the usual correlation coefficie...

Daily financial returns (and daily stock returns, in particular) are commonly modeled as GARCH(1,1) processes. Here we test this specification using new model evaluation technology developed in Ashley and Patterson (2006), which examines the ability of the estimated model to reproduce features of particular interest: various aspects of nonlinear serial dependence, in the present instance. Using...

We show that, for three common SARV models, fitting a minimum mean square linear filter is equivalent to fitting a GARCH model. This suggests that GARCH models may be useful for filtering, forecasting, and parameter estimation in stochastic volatility settings. To investigate, we use simulations to evaluate how the three SARV models and their associated GARCH filters perform under controlled co...

A key problem in financial econometrics is the modeling, estimation and forecasting of conditional return volatility and correlation. Having accurate forecasting models for conditional volatility and correlation is important for accurate derivatives pricing, risk management and asset allocation decisions. It is well known that conditional volatility and correlation are highly predictable. An in...