Ausrine Lakstutiene

This study comprehensively evaluates the ability to borrow machine learning modeling techniques for SMEs, utilizing a sample of the Baltic States with many variables. The study aims to assess the applicability of access to credit modeling techniques for SMEs. This is the first study in which a large–scale assessment has been carried out in the Baltic States sample, covering five years of credit applications from SMEs to a depository institution. The results showed that Gradient Boosting produces the most accurate results. Gradient Boosting demonstrated better results than the benchmark Logistic Regression as well as other advanced machine learning models, including Random Forests and Multilayer Perceptron models. The method showed the highest accuracy of the overall receiver operating characteristic (ROC) curve and the associated area under the curve (AUC) (ROC–AUC) and Average Precision values, as well as other discriminatory threshold values, compared to alternative methods.

This paper presents a stress testing technique based on a hidden Markov regime switching model and scenario generations. Firstly, we assume that investor’s risk preferences and beliefs are primarily observed in main world indices and then transmitted to other markets such as IInd pillar life-cycle pension funds. This enables both regime (market state) identification and regime-switching detection. Second, we apply stress to the transition matrix by increasing probabilities of moving to a worse market state, i.e., we increase the transition probabilities on the upper-right side of the main diagonal and decrease the diagonal probabilities correspondingly. Third, future scenarios (evolution) of returns of pension funds are generated using stressed transition matrices and basic risk and performance measures of the final wealth are presented. Finally, three different strategies for pension managers are considered and compared to each other. The results show how the best strategy depends on the stress level.

Review report of Financial Repercussions of Carbon Emissions and Depletion of Natural Resources: The Role of Fiscal Policy in Determining Health Expenditures Here are some revised suggestions: * It is recommended to ensure that the abstract mainta...

This paper focuses on the performance of Lithuanian life-cycle second-pillar pension funds. Every such fund first specifies its benchmark and then attempts to follow the benchmark in some way. This is a form of regulation, meaning that every such fund is somehow regulated and controlled by the central bank authorities. The goal of this paper is twofold: (i) to analyse the returns of the pension funds with respect to their benchmarks and (ii) to determine whether less strict regulation leads to a better outperformance of the fund with respect to the benchmark. In order to achieve this, we introduced a new performance measure called the dominance-tracking index, which combines the ideas of almost stochastic dominance relations and tracking errors. While the tracking error and its modifications measure the strength of the regulation, almost stochastic dominance provides information about preferences between the funds and their benchmarks. Therefore, the new index was constructed in such a way as to take into account both approaches. The empirical section of the study then presents the results separately for the considered pension managers and participants’ age groups as usual in the life-cycle pension funds analysis. Finally, by taking into account various periods, we studied the effects of the COVID-19 crisis.

Scientific discussions have emphasized that the main problem with the current deposit insurance system is that the current system does not evaluate the risks that banks assume to calculate the deposit insurance premiums in many countries of the European Union (E.U.). Thus, the prevailing system does not safeguard a sufficient level of stability in the banking system. Scientific studies show that the deposit insurance system should consider not only the risk indicators for individual banks, but it must also consider the systemic risk of banks that affects the stability of the banking system. Hence, the question arises as to whether measurements of systemic risk in a common E.U. risk-based deposit insurance system are a formal necessity or if they are a value-adding process. Expanding the discussion of scientists, this article analyzes how contributions to insurance funds would change the banks of Lithuania following the introduction of the E.U.’s overall risk-based deposit insurance system and after taking into consideration the additional systemic risk. The research results that were obtained provide evidence that the introduction of a risk-based deposit insurance system would redistribute payments to the deposit insurance fund between banks operating in Lithuania, and, thereby, would contribute to a reduction in the negative effects of the deposit insurance system and would improve the stability in the financial system.

Over 98 per cent of all business entities in the EU are SMEs which provide two-thirds of private workplaces and generate more than half of value-added. Moreover, importance of SMEs increases. As prevailing economic conditions worsened situation of SMEs and banks tightened up lending conditions thus aggravating access to funding sources, it was just necessary to develop support for these companies. Therefore, the purpose of this paper was to analyse financial engineering tools for business development in Lithuania established by the EU structural funds (under the program of 2007-2013) and to evaluate its impact on Lithuanian economy as well as on small and medium-sized business. The research evidenced that SMEs have a significant impact on the overall state of Lithuanian economy: they employ the majority of employees (over 70 per cent) and generate a significant part of the value-added (over 50 per cent). The highest obtained correlation coefficients (0.8 and above) were those of the EU structural funds and number of SMEs, GDP and value-added, number of SMEs and GDP, number of SMEs and value-added. This evidences that the EU structural funds have an impact on the number of SMEs, which in turn affects GDP and value-added generated by SMEs. DOI: http://dx.doi.org/10.5755/j01.em.18.3.5230