Conditional Heteroskedasticity in Count Data Regression: Self-Feeding Activity in Fish

The paper introduces a new approach to incorporating time dependent overdispersion for Poisson related regression models. To handle the added flexibility in conditional heteroskedasticity in time series count data some wellknown estimators are adapted and a GMM type estimator is suggested. The estimators are applied to a time series of self-feeding activity in Arctic charr. There is strong support for both a dynamic conditional mean function and a dynamic model for the overdispersion.Poisson; Overdispersion; ARCH; Estimation; Self-Feeding; Arctic Charr

Temporal Aggregation of the Returns of a Stock Index Series

The effects of temporal aggregation on asymmetry properties and the kurtosis of returns based on the NYSE composite index are studied. There is less asymmetry in responses to shocks for weekly and monthly frequencies than for the daily frequency. Kurtosis is not smaller for the lower frequencies.symmetric moving average; QGARCH; estimation; kurtosis; Pearson IV; NYSE

Conditional Heteroskedasticity in some Common Count Data Models for Financial Time Series Data

Conditional heteroskedasticity properties are derived for some common count data regression and time series models. New extensions are suggested and discussed.Conditional variance; time series; finance; traded stocks; Poisson.

Forecasting the Size Distribution of Financial Plants in Swedish Municipalities

The paper studies the forecasting of a future size distribution of plants. As a model we use an open Markov chain model for macro data. Estimation is by reparametrization instead of by inequality restrictions using single equation least squares. The estimator is studied in a small Monte Carlo experiment for short time series lengths and macro data. Well-known mobility indices and a new idea of using a truncated transition probability matrix are discussed and also studied in the Monte Carlo experiment. For the financial plants (1984-1993) we find evidence of mobility of a downsizing nature. In a one-step-ahead forecast evaluation we find some overprediction.Open Markov chain; Mobility index; Reparametrization; Least squares estimation; Plant size; Municipality

Discretized Time and Conditional Duration Modelling for Stock Transaction Data

The paper considers conditional duration models in which durations are in continuous time but measured in grouped or discretized form. This feature of recorded durations in combination with a frequently traded stock is expected to negatively influence the performance of conventional estimators. A few estimators that account for the discreteness are discussed and compared in a Monte Carlo experiment. An EM-algorithm accounting for the discrete data performs better than those which do not. Empirical results are reported for trading durations in Ericsson B at Stockholmsbörsen for a three-week period of July 2002. The incorporation of level variables for past trading is rejected in favour of change variables. This enables an interpretation in terms of news effects. No evidence of asymmetric responses to news about prices and spreads is found.Grouped data; Maximum likelihood; EM-algorithm; Estimation; Finance; News

Influence of News in Moscow and New York on Returns and Risks on Baltic State Stock Indices

The impact of news of the Moscow and New York stock market exchanges on the returns and volatilities of the Baltic state stock market indices is studied using daily return data for the period of 2000-2005. A nonlinear time series model that accounts for asymmetries in the conditional mean and variance functions is used for the em- pirical work. News from New York have stronger effect on returns in Tallinn, than news from Moscow. High risk shocks in New York have a strong impact on volatility in Tallinn, whereas volatility of Vilnius is more influenced by high risk shocks from Moscow. Riga seems to be autonomous to news arriving from abroad.Estonia; Latvia; Lithuania; Time series; Estimation; Finance

Effects of Explanatory Variables in Count Data Moving Average Models

This note gives dynamic effects of discrete and continuous explanatory variables for count data or integer-valued moving average models. An illustration based on a model for the number of transactions in a stock is included.INMA model; Marginal effect; Intra-day; Financial data

Forecasting based on Very Small Samples and Additional Non-Sample Information

Generalized method of moments estimation and forecasting is introduced for very small samples when additional non-sample information is available. Small simulation experiments are conducted for the linear model with errors-in-variables and for a Poisson regression model. Two empirical illustrations are included. One is based on Ukrainian imports and the other on private schools in a Swedish county.Generalized method of moments; additional information; forecasting; Ukrainian imports; private schools

An Alternative Conditional Asymmetry Specification for Stock Returns

The paper advances the log-generalized gamma distribution as a suitable generator of conditional skewness. Based on the NYSE composite daily returns an asMA-asQGARCH model along with skewness dynamics is estimated. The results indicate a skewness that varies between sizeable negative skewness and almost symmetry. The conditional variance and skewness measures are negatively correlated.Time series, finance, nonlinearity, skewness, gamma, estimation, NYSE

Integer-Valued Moving Average Modelling of the Number of Transactions in Stocks

The integer-valued moving average model is advanced to model the number of transactions in intra-day data of stocks. The conditional mean and variance properties are discussed and model extensions to include, e.g., explanatory variables are offered. Least squares and generalized method of moment estimators are presented. In a small Monte Carlo study the least squares estimator comes out as the best choice. Empirically we find support for the use of long-lag moving average models in a Swedish stock series. News about prices are found to exert a symmetric and positive effect on the number of transactions.Count data; Intra-day; High frequency; Time series; Estimation; Finance.