Purpose The purpose of this study is to investigate volatility connectedness between major cryptocurrencies by the virtue of market capitalization. In this context, this paper implements the frequency connectedness approach of Barunik and Krehlik (2018) and to measure short-, medium- and long-term connectedness between realized volatilities of cryptocurrencies. Additionally, this paper analyzes network graphs of directional TO/FROM spillovers before and after the announcement of the COVID-19 pandemic by the World Health Organization. Design/methodology/approach In this study, we examine the volatility connectedness among eight major cryptocurrencies by the virtue of market capitalization by using the frequency connectedness approach over the period July 26, 2017 and October 28, 2020. To this end, this paper computes short-, medium- and long-cycle overall spillover indexes on different frequency bands. All indexes properly capture well-known events such as the 2018 cryptocurrency market crash and COVID-19 pandemic and markedly surge around these incidents. Furthermore, owing to notably increased volatilities after the official announcement of the COVID-19 pandemic, this paper concentrates on network connectedness of volatility spillovers for two distinct periods, July 26, 2017–March 10, 2020 and March 11, 2020–October 28, 2020, respectively. In line with the related studies, major cryptocurrencies stand at the epicenter of the connectedness network and directional volatility spillovers dramatically intensify based on the network analysis. Findings Overall spillover indexes have fluctuated between 54% and 92% in May 2018 and April 2020. The indexes gradually escalated till November 9, 2018 and surpassed their average values (71.92%, 73.66% and 74.23%, respectively). Overall spillover indexes dramatically plummeted till January 2019 and reached their troughs (54.04%, 57.81% and 57.81%, respectively). Etherium catalyst the highest sum of volatility spillovers to other cryptocurrencies (94.2%) and is followed by Litecoin (79.8%) and Bitcoin (76.4%) before the COVID-19 announcement, whereas Litecoin becomes the largest transmitter of total volatility (89.5%) and followed by Bitcoin (89.3%) and Etherium (88.9%). Except for Etherium, the magnitudes of total volatility spillovers from each cryptocurrency notably increase after – COVID-19 announcement period. The medium-cycle network topology of pairwise spillovers indicates that the largest transmitter of total volatility spillover is Litecoin (89.5%) and followed by Bitcoin (89.3%) and Etherium (88.9%) before the COVID-19 announcement. Etherium keeps its leading role of transmitting the highest sum of volatility spillovers (89.4%), followed by Bitcoin (88.9%) and Litecoin (88.2%) after the COVID-19 announcement. The largest transmitter of total volatility spillovers is Etherium (95.7%), followed by Litecoin (81.2%) and Binance Coin (75.5%) for the long-cycle connectedness network in the before-COVID-19 announcement period. These nodes keep their leading roles in propagating volatility spillover in the latter period with the following sum of spillovers (Etherium-89.5%, Bitcoin-88.9% and Litecoin-88.1%, respectively). Research limitations/implications The study can be extended by including more cryptocurrencies and high-frequency data. Originality/value The study is original and contributes to the extant literature threefold. First, this paper identifies connectedness between major cryptocurrencies on different frequency bands by using a novel methodology. Second, this paper estimates volatility connectedness between major cryptocurrencies before and after the announcement of the COVID-19 pandemic and thereby to concentrate on its impact on the cryptocurrency market. Third, this paper plots network graphs of volatility connectedness and herewith picture the intensification of cryptocurrencies due to a major financial distress event.

Individuals or criminal organizations carry out various criminal activities in all countries of the world with illegal methods to gain unfair economic benefits. With theft, bribery, and fraud being in the lead, many types of economic crimes disrupt the economic and social order of the society, damage mutual trust between individuals, and feed the informal economy. This study aims to determine the relationship between economic crimes and socioeconomic factors. Within the scope of the study, panel data of 26 sub-regions in the Statistical Regional Units Classification-II (İİBS-II) of Turkey for the period of 2008-2019 were analyzed with dynamic panel data methods and the socioeconomic determinants of 10 different crimes representing economic crimes in the literature were put forward. Based on the results of the study, it is observed that the gross national product per capita, unemployment, educational status, population density, and net migration, which are among the socioeconomic factors included in the analysis, have statistically significant effects on the economic crime rates.

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Social capital is one of the variables that influences overcoming problems related to welfare and development. Studies show that social capital level is generally in a linear relationship with the development level of countries. The purpose of study is to examine the relationship between the welfare levels of OECD countries and their social capital levels within the framework of the Legatum Prosperity Index. For this, the effect of social capital, which is one of the 12 components of the Legatum Prosperity index, on the welfare level of OECD countries was examined. the country rankings in which social capital is equally weighted with other index variables and the country rankings where social capital is excluded from the index are compared. The results obtained show that social capital is improving for the rankings of the welfare levels of Norway,