The Integer-valued Moving Average Model (INMA) 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 explanatory variables are offered. Least squares and generalized method of moment estimators are presented. In a small Monte Carlo study a feasible 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. There is evidence of asymmetric effects of news about prices on the number of transactions.

The devastation resulting from the recent global financial and Eurozone crises is immense. Most researchers commonly believe that the global financial crisis originated in the United States, and spread immediately to global financial hubs where it eventually became the Eurozone crisis. Several studies have been conducted on financial market contagion during both global and Eurozone crises; however, the issue of whether equity market contagion spreads from the United States to the world equity markets during these crises has not been addressed yet. Using US dollar-denominated MSCI daily indices from fifty-five equity markets for the period 2003-2013, we have found evidence of contagion in developed and emerging markets during the global and Eurozone crises. We show that contagion spread from the United States to the world markets during both crises. Our regression results identify that the bank risk transfer between the United States and other countries is the key transmission channel for crosscountry correlations. This study has an important policy implication for portfolio diversification between the United States and other countries during these crises.

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A model to account for the long-memory property in a count data framework is proposed and applied to high-frequency stock transactions data. By combining features of the INARMA and ARFIMA models, an Integer-valued Auto Regressive Fractionally Integrated Moving Average (INARFIMA) model is proposed. The unconditional and conditional first-and second-order moments are given. The CLS, FGLS and GMM estimators are discussed. In its empirical application to two stock series for AstraZeneca and Ericsson B, we find that both series have a fractional integration property.

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The current paper studies equity markets for the contagion of squared index returns as a proxy for stock market volatility, which has not been studied earlier. The study examines squared stock index returns of equity in 35 markets, including the US, UK, Euro Zone and BRICS (Brazil, Russia, India, China and South Africa) countries, as a proxy for the measurement of volatility. Results from the conditional heteroskedasticity long memory model show the evidence of long memory in the squared stock returns of all 35 stock indices studied. Empirical findings show the evidence of contagion during the global financial crisis (GFC) and Euro Zone crisis (EZC). The intensity of contagion varies depending on its sources. This implies that the effects of shocks are not symmetric and may have led to some structural changes. The effect of contagion is also studied by decomposing the level series into explained and unexplained behaviors.

The effect of Swedish regional investment grants during 1990-1999 on firm performance, in terms of returns on equity and number of employees, were studied using a propensity-score matching-method to control for sample selection. Firms that received grants did not perform better in terms of returns on equity when compared to matched firms in the control group. In most years, recipient firms also did not hire more employees. The results thus cast doubt on the use of regional investment grants as a general policy instrument to improve firm performance

This paper incorporates conditional heteroscedasticity properties in the long memory model and applies the model on squared returns of BRICS (Brazil, Russia, India, China, and South Africa), UK and USA equity markets to capture the volatility of stock return. The conditional first-and second-order moments are provided.The CLS, FGLS and QML are discussed and 2SQML estimator is proposed. The simulation study suggests that the proposed 2SQML estimator performs better than the other three estimators. Both in simulation and empirical studies, we find that the proposed model FIMACH outperforms FIGARCH in terms of eliminating serial correlations.