Journals Published by Vilnius Tech
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The effects of fınancıal pressure polıcıes on economıc growth: The case of OECD countries
Get PDFIn this study, the effects on economic growth of financial pressure policies applied in OECD countries are examined. For this purpose, the “financial pressure index (FPI)” was calculated by using 10- year data for 2010–2020 from 37 OECD countries and “growth rates” were obtained. The FPI was calculated using (i) loans extended to the pri¬vate sector, (ii) loans extended to the central government, (iii) interest payments and (iv) inflation rate data. In calculating FPI, first of all, the data was standardized. Following the standardization process, the data was weighted using Principal Component Analysis (PCA) to calculate the FPI. After weighting the data, each standardized value was aggregated by multiplying it by its own weighted value, and the final FPI was ultimately calculated. Economic growth rates were calculated as a percentage of GDP. Finally, the analysis was carried out by comparing the calculated FPI with the economic growth rates. According to the results of the analysis, the coefficient of FPI was statistically significant (p < 0.05). In this context, every 1-point increase in FPI reduced GDP by 0.178 points.
First published online 17 September 202
Is the household sector over-indebted in China? – the perspective of economic growth and financial risks
Get PDFIn this paper, we introduce household debt into a general equilibrium model and investigate the sources of changes in household debt through the lens of leverage constraints of the household sector, firms, and banks. Based on this, we analyze the impacts of household debt on economic growth and the financial risks embedded in debt-stimulated economic growth. After fitting our model to the data from China, we find that the increase in household debt in China is conducive to economic growth as it promotes demand growth and reduces financial frictions. In addition, the marginal financial risk induced by the growth of household debt is relatively small, implying that the increase in household debt can some- what promote economic growth without accumulating much endogenous vulnerability in the economy. This contrasts with the reduction of firms’ debt, which leads to drastic negative economic fluctuations in the short term, although it is beneficial to economic growth in the long run given that firms have already been caught in a vicious debt-deflation cycle. Therefore, to ensure the stability of the economy in China, it is plausible to squeeze out firms’ debt through increasing debt in the household sector.
First published online 12 February 202
Fiscal sustainability and economic growth in the light of new economic governance
Get PDFThis research estimates the effects of public debt on economic growth. In addition, it contributes to examining the impact of public debt on investment as a possible channel of impact on economic growth. The empirical analysis is based on a smooth transition panel data regression model. The results show the non-linear relationship between public debt and economic growth for the sample of 12 Euro area countries is markedly statistically significant. The sustainable threshold for this relationship is on average between 93% and 105%. This implies that public debt to gross domestic product ratios above this sustainable threshold would have a negative effect on economic growth. Although insignificant, the results for the sample of 20 Euro area countries could indicate that for the less developed Euro countries the potentially negative effects of high debt can already be seen at a lower level of public debt, which is the case for even more sensible debt reduction policies. Taking into consideration the fiscal rules, the results suggest that one size does not necessarily fit all. Moreover, the trade-off between fiscal consolidation and increased green public investment will be one of the key challenges of this decade given the reinstated European Union fiscal rules.
First published online 16 April 202
Market time-series reversal: evidence from China’s market
Get PDFUpon high-frequency data of China Securities Index 300 (CSI 300) exchange-traded fund and index future contracts, we demonstrate a time-series reversal pattern between the last three-hour returns on current day and those on previous day. Further this reversal is also found in China index future market. This predictability has been illustrated to be both statistically and economically significant, and the significance is stronger on more volatile/higher volume days and non-bearish market state. Extensive regression analysis suggests that the time-series reversal is mainly induced by irrational investor overreaction, not by the lack of liquidity provision. Moreover, the economic value of the reversal pattern is evaluated to yield an outstanding trading performance by executing a market timing strategy.
First published online 02 April 202
Fuzzy multicriteria decision-making for assessing multinational enterprises’ outbound investment destinations on the basis of tax factors
Get PDFTaxes are a crucial consideration in a multinational enterprise’s (MNE)’s choice of overseas market to invest in. Taxes are affected by a variety of factors, and these factors are prioritized vis-à-vis each other in such investment decision-making. The present study thus developed a framework for evaluating the relative importance of tax factors affecting the outbound investment decisions of MNEs, using insights from the literature and expert interviews to establish evaluation criteria. To address the complexities of multicriteria decision-making, this study employed fuzzy linguistic preference relations. The study subsequently applied the fuzzy Vlse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) approach to rank the alternatives proposed by the expert decision-making group. Finally, this study applied this methodology to a case study of candidate overseas markets (Vietnam, Malaysia, and Indonesia) for Taiwanese firms to invest in, providing a practical demonstration of the model. In conclusion, the present study’s comparative analysis of the application of fuzzy VIKOR to foreign direct investment by Taiwanese MNEs offers a systematic approach for both academic and industry practitioners involved in strategic host country selection for multinational investments.
First published online 14 July 202
Agility sustainable supply chain in automobile industry
Get PDFAutomobile industries are facing rapid and unanticipated changes in their business environment. New strategies are needed to remain competitive in the market for those companies. The supply chain plays a crucial role in automobile companies, and improving the supply chain helps them to be successful in the competition. The agile paradigm allows companies to be flexible in the competition, and also sustainable paradigm helps them to popularity among the organizational system. The primary purpose of this study is to combine agile supply chain and sustainable supply chain as one strategy. For this purpose, 73 factors obtained from previous studies and the Fuzzy Delphi Method and Fuzzy Best Worst Method were used to find the best factors and rank them. The results show that 26 elements accepted and after ranking Quality, Supply chain configuration, Customer satisfaction, Suppliers ’green initiatives and Top management vision were the best five factors. In addition, the results confirmed the finding and the new model for an agile sustainable supply chain
Evaluating the factors affecting dairy commodity returns: the case of European dairy markets
Get PDFPurpose – motivated by the price spikes and booms in major commodities markets around the world, this study looks into the factors that affect the variance in returns from dairy futures contracts. The purpose of the study is to determine whether dairy commodity prices – especially in times of economic turmoil – are being driven away from their fundamental value. The study focuses on European dairy futures markets, which are less studied by other authors in their research and are at a nascent stage of development in comparison to other agricultural commodity markets. The study includes various determinants such as energy prices, major stock indices, as well as market related variables such as financial speculation, in order to test whether returns from dairy commodities can be explained solely by macroeconomic factors or if this impact is amplified by trade volume or financial speculation within these markets. Therefore, the paper aims to assess the determinants of dairy futures prices before and after 2020, when the COVID-19 pandemic began and was followed by the war in Ukraine.
Research methodology – the authors analyse dairy commodities traded on the European Energy Exchange (EEX) and employ the generalised autoregressive conditional heteroskedasticity (GARCH) modelling, as well as the Augmented Dickey-Fuller (ADF) and Granger non-causality tests to analyse what drives the returns from dairy commodity futures and the direction of this impact. The study consists of two-time frames: before and after the COVID-19 pandemic. Findings – an important finding from the study is that returns from dairy commodities are mostly explained by macroeconomic variables when analysing the post-2020 timeframe. Dairy commodities also experience asymmetric return volatility, showing that in dairy markets, negative returns are followed by reduced volatility. However, the role of trade volume or financial speculation on dairy commodity returns is found to be mixed or that it has an effect only on butter commodities when analysing 2020 and onward time period. Another important finding is that only returns from skimmed milk futures are significantly affected by seasonality.
Research limitations – the study uses only two dairy commodity types in the research, there is insufficient data on non-commercial positions from EEX.
Practical implications – as dairy commodities markets grow and attract more market activity, regulators should be more careful about geopolitical risks and financial speculation in European dairy futures markets.
Originality/Value – the study examines European dairy futures markets, which are relatively new compared to other commodity markets and have not received as much attention in other research
Structuring and validation of photogrammetric territorial data
Get PDFIn the loving memory of Mariasofia Paparo, this publication focuses on the validation procedures of photogrammetric geographic information and the production and interpretation of complex reports, which are essential for handling the vast amount of generated data. Furthermore, sources of error in the structuring of geographic data and the quality parameters and conformity criteria necessary for the utilization of such data within the national geodatabase have been investigated
Accuracy test of satellite imagery-derived bathymetry in shallow waters using Sentinel-2A multispectral imagery
Get PDFContinuous bathymetry mapping for shallow waters is very important considering that these waters are prone to change. Bathymetry measurements obtained from satellite imagery are an alternative that can be used. This study aimed to evaluate and develop algorithms that can be used to estimate shallow water depth values obtained from satellite imagery. In this study, the depth mapping results were obtained from Surface Reflectance derived from Sentinel-2A image processing. A comparative analysis was performed by comparing measurements obtained with an echosounder and estimated depths estimated with Lyzenga, Stumpf, and modified Stumpf algorithms. In this study, where the depth ranged from 2–6 meters, the Lyzenga algorithm performed the best algorithm with the R2 value of 0.94 and the RMSE 0.23, followed by the modified Stumpf algorithm with an R2 value of 0.93 and RMSE 0.24, and Stumpf algorithm with a R2 value of 0.88 and a RMSE of 0.32. Overall, this study provides an important contribution to comparing Lyzenga and Stumpf algorithms for estimating water depths. This study provides guidance on choosing the correct algorithm for bathymetric mapping using satellite imagery in similar water locations
Cluster analysis of factors influencing the valuation of real estate objects
Get PDFThe current real estate market analysis reveals challenges in valuation methods, procedure adequacy, and evolving technological approaches. Uncertainty arises from using localised methods for valuing individual real estate objects. A significant concern is the reliability and completeness of valuation data. Researchers emphasise market-driven aspects as trends in real estate valuation. Features for valuation are identified through quantitative characteristics, uncovering components and their nature. The research analyses foreign and domestic practices for real estate object valuation. Challenges include understanding methodological and informational support through mathematical methods and addressing factors affecting real estate object valuation. The need for cluster analysis to identify factors affecting real estate object valuation is recognised. To implement cluster analysis of factors affecting real estate valuation, a method is proposed involving the development of classification features, optimal typological grouping, and clustering implementation technology. Six groups of factors were chosen: spatial formation, urban planning provision, environmental impact, investment indicators, infrastructure provision, and limiting characteristics. An agglomerative process calculated the distance matrix between clusters of factors. The MacQueen k-means clustering method determined final clusters, confirming the validity of the proposed factor groups. The clustering of factors affecting real estate valuation was based on obtained distance data. The result identifies a high level of factors influencing real estate object valuation. Nine coincidences justify this in their clustering with four units of factors influencing real estate object valuation