Abstract This publication presents the results of the comparative analysis of economic growth in the United States and Poland using Harver Analytics. It takes into account factors such as GDP, industrial output, consumption expenditure, investment, exports and consumption expenditure of the government. The aim of the publication is presentation of differences between economic growth in Poland and USA.

Abstract This research, under Engle-Granger Co-integration framework, examines the hedging efficiency of Indian rubber future markets during the period 2004-2017. The essence of this study is to seek evidence for the effects of global financial crisis of 2008 on the efficiency of rubber futures in hedging  price risks of spot rubber in India. The study proved the hedging efficiency of rubber futures during both pre and post recession periods. However, increased price volatility of Indian rubber after recession heightened risk exposure to market participants that eventually lead to unexpected changes in the hedging efficiency of rubber futures.  The research concludes with a suggestion that writing of rubber futures in India allows traders to hedge risk exposures in spot market along with the potentials of arbitrage gains. 

Abstract While there has been a significant amount of research covering the causes of merger waves, few papers have rank ordered merger waves based on the causes nor sought to determine which rationale leads to higher bidder payouts.  This paper seeks to fill this gap by examining a cross section of large mergers across most industries occurring over a 17 year period.  I find that merger waves over this period are caused foremost by changing economic and regulatory conditions.  It is the behavioral rationale of mispricing, however, that more often leads to higher bidder payouts or merger premiums among acquirers in merger waves. 

Abstract Recently, the use of dynamic conditional score (DCS) time series models are suggested in the body of literature on time series econometrics. DCS models are robust to extreme observations because those observations are discounted by the score function that updates each dynamic equation. Examples of the DCS models are the quasi-autoregressive (QAR) model and the Beta- t -EGARCH (exponential generalized autoregressive conditional heteroscedasticity) model, which measure the dynamics of location and scale, respectively, of the dependent variable. Both QAR and Beta- t -EGARCH discount extreme observations according to a smooth form of trimming. Classical dynamic location and scale models (for example, the AR and the GARCH models) are sensitive to extreme observations. Thus, the AR and the GARCH modelsmay provide imprecise estimates of location and scale dynamics. In the application presented in this paper, we use data from the Shanghai Stock Exchange A-Share Index and the Shenzhen Stock Exchange A-Share Index for the period of 5th January 1998 to 29th December 2017. For the corresponding stock index return time series, a relatively high number of extreme values are observed during the sample period. We find that the statistical performance of QAR plus Beta- t -EGARCH is superior to that of AR plus t -GARCH, due to the robustness of QAR plus Beta- t -EGARCH to extreme unexpected returns.