Habitat quality and fish populations: impacts of nutrient enrichment on the value of European perch off the east coast of Sweden

This study aims to examine whether the Autoregressive Integrated Moving Average (ARIMA) model is appropriate to be applied in the Indonesia Stock Exchange, especially for the socially resposible investment stocks. For the ARIMA model combines the autoregressive and moving average method, so it is viewed as a useful tool to predict the stock prices. Those methods are frequently used methods to forecast the stock prices. The data used in this study were daily SRI-KEHATI Index during the period of June 8, 2009 to July 17, 2017. The results showed that the daily SRI-KEHATI Index data were not stationary data, thus this data needed to be transformed. The transformation was done by using the first seasonal differencing transformation process. After being transformed, those data became stationary. Furthermore, this study found that ARIMA (3,1,1) was a model, which might be appropriate and fit with the data condition. This method was also relevant to be applied in the Indonesia Stock Exchange in order to forecast the stock prices.

Value-at-risk (VaR) is the most common and widely used risk measure that enterprises, particularly major banking corporations and investment bank firms employ in their risk mitigation processes. The purpose of this study is to investigate the value-at-risk (VaR) estimation models and their predictive performance by applying a series of backtesting methods on BRICS (Brazil, Russia, India, China, South Africa) and US stock market indices. The study employs three different VaR estimation models, namely normal (N), historical (HS), exponential weighted moving average (EMWA) procedures, and eight backtesting models. The empirical analysis is conducted during three different periods: overall period (2006–2021), global financial crisis (GFC) period (2008–2009), and COVID-19 period (2020–2021). The results show that the EMWA model performs better compared to N and HS estimation models for all the six stock market indices during overall and crisis sample periods. The results found that VaR models perform poorly during crisis periods like GFC and COVID-19 compared to the overall sample period. Furthermore, the study result shows that the predictive accuracy of VaR methods is weak during the COVID-19 era when compared to the GFC period.

To analyze and evaluate the investment processes in the regions of Ukraine, it is suggested to use a simulative model that, unlike existing ones, allows to take into account the influence of macroeconomic factors and to predict the future development of the economic system of the regions taking into account their investment potential. The examination of the assessed simulative models of the investment processes in the regions of Ukraine for adequacy is carried out using the determination coefficient and Fisher’s criterion, by which the influence of the most significant economic variables of social and economic development of the regions on the investments formation is determined. Research of the investments impact on the dynamics of economic systems indicators of the regions has shown that 86% of the constructed models are adequate. The presence of statistically significant estimates of model parameters confirms the effectiveness of the proposed approach for conducting research on the analysis and forecasting of the patterns of significant indicators formation of investment activity at the regional level, as well as their impact on indicators of social and economic development.