Elena Pelinescu

The Impact of Human Capital on Economic Growth

Abstract The EUs 2020 Strategy is focused on three area of growth: smart, sustainable and inclusive that couldn’t be achieved without major contribution of skills, knowledge or value of people, common knew as human capital. It is difficult to believe that these goals could be realized without a good education and training system, a large diffusion of knowledge in manufacturing services, a creative industries and a great effort to create a research-intensive economy. Using a panel methodology, the paper tried to reveal the role of human capital as a factor of the growth and to argue that the slow investment in human capital should influence the sustainable development of the countries.

Volatility Analysis of the Romanian Exchange Rate

Abstract Volatility has been traditionally analysed from the perspective of economic cycles and only recently as an autonomous process with a major influence on different macroeconomic approaches. Volatility is associated with risk in the sense that it offers a measure of possible variations of economic variables and the increased volatility of the markets can trigger economic crises as it can be seen in economic history. The paper intends to analyse the volatility of the leu/euro exchange rate taken into account the influence of the volatility of other currencies and other fundamental macroeconomic variables. The analysis is based on specific methods for high frequency time series. The database is comprised of daily time series for the period 05.01.2000-31.08.2013. The application of different ARCH-GARCH models indicate that the volatility of the leu/euro exchange rate follows an ARCH process, that there is a high asymmetry in the evaluation of information regarding the evolution of the exchange rate and that the exchange rate returns are correlated with volatility.

Short-term financial forecasting using ANN adaptive predictors in cascade

Our purpose is to verify the predictive performances of the artificial neural networks (ANNs) under volatile statistics and possibly incomplete information. Daily forecasts of exchange rate using exclusively primary available information for an emergent economy (such as the Romanian one) could be a proper experimental ground with such a goal. The present paper extends the previous authors’ research (Dobrescu et al., 2006; Nastac et al., 2007) on the same issue to improve the accuracy of exchange rate forecasting by using a set of neural predictors in cascade, instead of a single one. The results show that the presented model, despite its proved advantages, could be further improved in order to avoid the translation into residuals of the high serial correlation present in the primary database.

Modelling the High Frequency Exchange Rate in Romania with FIGARCH

Abstract Romanian forex market is an emerging market with periods of high volatility. The Romanian exchange rate was for a long term on a depreciating trend in nominal terms interrupted by short bursts of appreciation. The paper applies a FIGARCH model (Beine at al., 2002) for measuring the volatility of exchange rates in order to see how the forex market actors react to shocks in the exchange rate. We used daily data of Ron-Euro exchange rate between 2007 and 2012. Our results indicate that the exchange rate is highly fractionally integrated, indicating a fast process of mean reversion. The source of the exchange rate persistence is difficult to be explained and may be accounted for by unobserved factors or by the central bank intervention. If there are structural breaks in data, then the exchange rate could falsely indicate persistence in the data. In order to test for the spurious long-memory, we used a Zivot-Andrews Unit Root Test.