Econometric Modeling to Measure the Efficiency of Sharpe’s Ratio with Strong Autocorrelation Portfolios

Sharpe’s ratio is the most widely used index for establishing an order of priority for the portfolios to which the investor has access, and the purpose of this investigation is to verify that Sharpe’s ratio allows decisions to be made in investment portfolios considering different financial market conditions. The research is carried out by autoregressive model (AR) of the financial series of returns using Sharpe’s ratio for evaluations looking over the priority of financial assets which the investor can access while observing the effects that can cause autocorrelated series in evaluation measures for financial assets. The results presented in this study confirm the hypothesis proposed in which Sharpe’s ratio allows decisions to be made in the selection of investment portfolios under normal conditions thanks to the definition of a robustness function, whose empirical estimation shows an average 73% explanation of the variance in the degradation of the Spearman coefficient for each of the performance measures; however, given the presence of autocorrelation in the financial series of returns, this similarity is broken.

Economics of soil erosion: case study of Ukraine

Purpose. The main objective of this paper is (і) to determinate the economic loss due to crop productivity loss caused by soil erosion in Ukraine, and (іі) to present the results of the econometric modeling of soil erosion impact on the efficiency crop production at the regional and district level. Methodology / approach. This study uses the following methods: expert assessments and monographic (for the assessment of economic losses due to crop productivity loss from spreading soil erosion); graphical (for building three-dimensional graphs); econometric modeling (to develop a mathematical model of the dependence of the gross crop production and income from sales per 100 hectares from the share of eroded arable land in its total area and production costs in crop industry per 100 hectares); abstract-and-logical (for generalization of the research results). To solve the assigned tasks, linear and quadratic econometric models (production functions) were developed using a dataset (і) from 168 observations (on the example of Ukrainian regions for 2010–2016) and (ii) from 189 observations (on the example of districts of Kharkiv region for 2010–2016). This study was conducted in order to test the hypothesis that the increase in the area of eroded arable land has a negative effect on the gross output of crop production. Results. Our expert assessment of economic losses due to crop productivity loss from spread of soil erosion on agricultural land in Ukraine is 224 mln USD. The obtained results confirm the hypothesis about the negative relationship between gross crop output and the level of land erosion. The obtained data confirm that an increase in the area of eroded arable land by 1 % leads to a decrease in the gross output of crop production by 0.20 % per 100 hectares of agricultural land in total, and in the third group of the studied subjects (the share of eroded arable land in their total area is more than 50 %) – by 0.61 %, respectively. Originality / scientific novelty. For the first time, linear and nonlinear (quadratic) econometric models were developed, which made it possible to carry out quantitative assessment of the impact of the soil erosion and the financial support (production costs in crop industry) per hectare on the formation of the financial results (gross crop output and income) of business entities in Ukrainian agriculture. The provision on the economics of soil erosion was further developed in terms of expert assessment of losses from this type of degradation and confirmation of the effect of the economic law of diminishing returns, which should be taken into account when developing measures for sustainable land management. Practical value / implications. The main results of the study can be used for the development, substantiation and implementation of soil protection measures for the sustainable use of agricultural land and/or to informed decision-making at different levels of management concerning restoration of eroded land.

Econometrics for Modelling Climate Change

Shared features of economic and climate time series imply that tools for empirically modeling nonstationary economic outcomes are also appropriate for studying many aspects of observational climate-change data. Greenhouse gas emissions, such as carbon dioxide, nitrous oxide, and methane, are a major cause of climate change as they cumulate in the atmosphere and reradiate the sun’s energy. As these emissions are currently mainly due to economic activity, economic and climate time series have commonalities, including considerable inertia, stochastic trends, and distributional shifts, and hence the same econometric modeling approaches can be applied to analyze both phenomena. Moreover, both disciplines lack complete knowledge of their respective data-generating processes (DGPs), so model search retaining viable theory but allowing for shifting distributions is important. Reliable modeling of both climate and economic-related time series requires finding an unknown DGP (or close approximation thereto) to represent multivariate evolving processes subject to abrupt shifts. Consequently, to ensure that DGP is nested within a much larger set of candidate determinants, model formulations to search over should comprise all potentially relevant variables, their dynamics, indicators for perturbing outliers, shifts, trend breaks, and nonlinear functions, while retaining well-established theoretical insights. Econometric modeling of climate-change data requires a sufficiently general model selection approach to handle all these aspects. Machine learning with multipath block searches commencing from very general specifications, usually with more candidate explanatory variables than observations, to discover well-specified and undominated models of the nonstationary processes under analysis, offers a rigorous route to analyzing such complex data. To do so requires applying appropriate indicator saturation estimators (ISEs), a class that includes impulse indicators for outliers, step indicators for location shifts, multiplicative indicators for parameter changes, and trend indicators for trend breaks. All ISEs entail more candidate variables than observations, often by a large margin when implementing combinations, yet can detect the impacts of shifts and policy interventions to avoid nonconstant parameters in models, as well as improve forecasts. To characterize nonstationary observational data, one must handle all substantively relevant features jointly: A failure to do so leads to nonconstant and mis-specified models and hence incorrect theory evaluation and policy analyses.

Parallels and Differences in Earnings Management of the Visegrad Four and the Baltics

Earnings management is a legal and widely preferred phenomenon of business finance that financial managers use to maintain and improve the enterprise’s competitiveness. Managers purposely manipulate business earnings to achieve the required status of the enterprise. The consequence of these activities is to provide a positive perspective for the owners, encourage the profitability for the creditor and the investors as well as demonstrate economic strengths to competitors. This article aims to identify parallels and differences in earnings management of enterprises in the Visegrad Four and the Baltics in terms of competitiveness for the nineyear period 2010-2018. The research uses a final sample of 4,543 observations from the EBITs of Slovak, Czech, Hungarian and Polish enterprises as well as 1,633 observations from the EBITs of Latvian, Lithuanian and Estonian enterprises. Time-series methods with all necessary assumptions have been run for the analyzed financial dataset. The results of the econometric modeling of unit roots show significant parallels in these groups of countries. The enterprises from the Visegrad group and the Baltics group use the apparatus of earnings management to be competitive. The obtained results confirm the systematic but legal manipulation from the side of management. A quantitative analysis of homogeneity tests using 1,000,000 Monte Carlo simulations indicates significant time differences of manipulation in these emerging countries. The year 2014 signaled a radical “accelerando” in earnings management for the V4, and the year 2016 is highlighted for the Baltics.

The textile and garment industries in the Russian regions: Spatial and econometric modeling

Subject. The article focuses of the industrial specialization of the Russian regions. Objectives. We test the technique for analyzing the regional industrial specialization with econometric toolkit, referring to the textile and garment industries in Russia. Methods. We conducted the econometric analysis, relying upon spatial panel data on the regional industrial specialization. We used localization coefficients of the metrics, such as revenue from sale of goods, average monthly pay of workers in the given industry, average headcount in the given industry and labor productivity. Results. We discovered that there is a spatial correlation of labor productivity in the textile and garment industries. The localization of those employed in the textile and garment manufacturing has a negative correlation with labor productivity in the regions. We traced a positive correlation of labor productivity in the regions and the localization of workers’ wages. Conclusions. The proven economic analysis technique helps identify and analyze correlations of regional industrial specialization indicators.

Current Development Trends in the Digital Currency Market

Aim. The presented study aims to analyze the development of the digital currency market, investigate trends for expanding the use of its tools, identify the peculiarities of the current stage of digital currency use, and consider the mechanism of introducing central bank digital currencies (CBDCs).Tasks. The authors specify the role and content of the digital currency market and its tools in the modern Russian economy; examine the formation and development of the cryptocurrency market from the perspective of introducing the “digital ruble”; identify regulatory prerequisites that hinder the development of the digital currency market; describe current trends and the mechanism of organizing the introduction of CBDCs.Methods. This article reflects a comprehensive approach to assessing the effectiveness of the use of digital currency market tools based on the use of economic-statistical and general scientific dialectical methods as well as the laws and principles of formal logic. The conducted studies and recommendations are based on statistics provided by CoinMarketCap. In particular, the methodological basis includes econometric modeling tools used to assess the cryptocurrency market in order to identify its characteristic traits and features.Results. Under modern conditions, the digital currency market is considered to be one of the main transformational elements of the digital economy. The authors focus on the prerequisites for the development and implementation of the domestic digital currency as an instrument of the national monetary policy and for ensuring the financial stability of the economy as a whole. This hypothesis is confirmed by the analysis and study of the global economic situation in the international digital currency market as well as the peculiarities of the functioning of its key components.Conclusions. In the modern context, it is important to have a theoretical and practical understanding of the conditions for the functioning of the digital currency market in the national economy and to find a comprehensive solution to issues associated with expanding the use of its tools for the development of the payment system and the formation of a favorable competitive environment.

An analysis of digital factors of bank capital transnationalization

Subject. This article discusses the use of digital technologies in banking. Objectives. The article aims to identify the factors and channels of impact of digital technologies on the implementation of transnational strategies by banks and assess the conditions for the transnationalization of bank capital. Methods. For the study, I used econometric modeling and the methods of systems, logical, structural, and comparative analyses. Results. Based on the systematization of the theory of transnationalization and a comprehensive analysis of factors affecting the activities of transnational banks, the article offers an original model for assessing the impact of digital technologies on the transnationalization of bank capital. Conclusions. To maintain a competitive position in the market, it is necessary to take into account external and internal factors of digital development when developing a strategy for transnationalization of banking activities in the context of digitalization, continuous technological changes, and innovation.

Livelihood Diversification of Forest Dwellers – A Necessity or Choice?

The forest as an ecosystem plays a vital role in protecting the environment and meeting indispensable human needs. Restrictions on forestry activities for safeguarding the environment and seasonality in collection often count for vulnerable livelihood of the forest-fringe dwellers. This paper is an attempt to assess the existing opportunities for livelihood diversification of the forest dwellers residing at the Simlipal National Park region, India, through the Herfindahl–Hirschman Index; and to determine the key factors responsible for the scopes and realization of livelihood diversification through econometric modeling. The analysis is done across blocks with having different forest-zonal geographies and human characteristics to comprehend and ensure sustainable livelihood for a better future. JEL Classification: C80, Q23, Q56, C50, Q01, Q2