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The purpose of this study is to investigate volatility connectedness between major cryptocurrencies by the virtue of market capitalization. In this context, this paper…
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.
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.
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).
The study can be extended by including more cryptocurrencies and high-frequency data.
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.
The prevailing theories of entrepreneurship have typically revolved around the ability of individuals to recognize opportunities and act on them by starting new ventures…
The prevailing theories of entrepreneurship have typically revolved around the ability of individuals to recognize opportunities and act on them by starting new ventures. This has generated a literature asking why entrepreneurial behavior varies across individuals with different characteristics, while implicitly holding the external context in which the individual finds oneself to be constant. Thus, where the opportunities come from, or the source of entrepreneurial opportunities, are also implicitly taken as given. By contrast, we provide a theory identifying at least one source of entrepreneurial opportunity – new knowledge and ideas that are not fully commercialized by the organization actually investing in the creation of that knowledge. The knowledge spillover theory of entrepreneurship holds individual characteristics as given, but lets the context vary. In particular, high knowledge contexts are found to generate more entrepreneurial opportunities, where the entrepreneur serves as a conduit for knowledge spillovers. By contrast, impoverished knowledge contexts are found to generate fewer entrepreneurial opportunities. By serving as a conduit for knowledge spillovers, entrepreneurship is the missing link between investments in new knowledge and economic growth. Thus, the knowledge spillover theory of entrepreneurship provides not just an explanation of why entrepreneurship has become more prevalent as the factor of knowledge has emerged as a crucial source for comparative advantage, but also why entrepreneurship plays a vital role in generating economic growth. Entrepreneurship is an important mechanism permeating the knowledge filter to facilitate the spillover of knowledge, and ultimately generating economic growth.
This chapter examines dynamic connectedness among emerging Asian equity markets as well as explores their linkages vis-à-vis other major global markets. We find that…
This chapter examines dynamic connectedness among emerging Asian equity markets as well as explores their linkages vis-à-vis other major global markets. We find that international equity markets are tightly integrated. Measuring connectedness based on a generalized Vector Autoregressive (VAR) model, more than half of all total forecast error variance in equity return and volatility shocks come from other markets as opposed to country own shocks. When examining the degree of connectedness over time, we find that international stock markets have become increasingly connected, with a gentle upward trend since the Asian financial crisis (AFC) but with a rapid burst during the global financial crisis (GFC). Despite the growing importance of Asian emerging markets in the world economy, we find that their influence on advanced economies are still relatively small, with no significant increase over time. During the past decade, advanced markets have been consistently net transmitters of shocks while emerging Asian markets act as net receivers. Based on the nature of equity shock spillovers, we also find that advanced countries are still tightly connected among themselves while intraregional connectedness within Asia remains strong. By investigating whether uncertainty plays an important role in explaining the degree of stock market connectedness, we find that economic policy uncertainty (EPU) from the US is an important source of financial shock spillover for the majority of international equity markets. In contrast, US financial market uncertainty as proxied by the VIX index drives equity market spillovers only among advanced economies.
At the onset of the Global Financial Crisis in 2007–2008, majority of the analysts and policymakers have anticipated contagion from the markets volatility in the advanced…
At the onset of the Global Financial Crisis in 2007–2008, majority of the analysts and policymakers have anticipated contagion from the markets volatility in the advanced economies (AEs) to the emerging markets (EMs). This chapter examines the volatility spillovers from the AEs’ equity markets (Japan, the United States and Europe) to the four key EMs, the BRIC (Brazil, Russia, India and China).
The period under study, from 2000 through mid-2014, reflects a time of varying regimes in markets volatility, including the periods of dot.com bubble, the Global Financial Crisis and the European Sovereign Debt Crisis, the Great Recession and the start of the Russian-Ukrainian geopolitical crisis. To estimate volatility cross-linkages between the AEs and BRIC markets, we use multivariate GARCH-BEKK model across a number of specifications.
We find that, the developed economies weighted return volatility did have a significant impact on volatility across all four of the BRIC economies returns. However, contrary to the consensus view, there was no evidence of volatility spillover from the individual AEs onto BRIC economies with the exception of a spillover from Europe to Brazil. The implied forward-looking expectations for markets volatility had a strong and significant spillover effect onto Brazil, Russia and China, and a weaker effect on India.
The evidence on volatility spillovers from the AEs markets to EMs puts into question the traditional view of financial and economic systems sustainability in the presence of higher orders of integration of the global monetary and financial systems. Overall, data suggest that we are witnessing less than perfect integration between BRIC economies and AEs markets to-date can offer some volatility hedging opportunities for investors.
Our chapter contributes to the growing literature on volatility spillovers from the AEs to the EMs in a number of ways. Firstly, we provide a formal analysis of the spillovers to the BRIC economies over the periods of recent crises. Secondly, we make new conclusions concerning longer-term spillovers as opposed to higher frequency volatility contagion covered by the previous literature. Thirdly, we consider a new channel for volatility contagion – the trade-weighted AEs volatility measure.
Purpose – This chapter aims at investigating the impact of cross-border knowledge spillovers on technological innovation in the renewable energy sector.…
Purpose – This chapter aims at investigating the impact of cross-border knowledge spillovers on technological innovation in the renewable energy sector.
Methodology/approach – The analysis presented in the chapter assumes that technological knowledge exhibits several tacit elements and requires established connections to flow between countries. A new measure for knowledge spillovers is obtained by weighting international R&D stocks through bilateral trade flows. The country-level patenting activity is modelled through a knowledge production function. The sample includes 18 OECD countries over the 1990–2006 period. Estimates are obtained through panel data techniques.
Findings – Our econometric results show that international knowledge developed by other countries has positive effects on the focal country's innovation in renewable energy technologies. Cross-country linkages, rather than mere geographic proximity, are found to favour cross-country knowledge spillovers.
Impact – The research contributes to the design of energy innovation policies. Public R&D is confirmed to be a relevant input to energy innovation. Coordination between countries in energy R&D activities can be required, particularly when countries maintain mutual linkages.
Originality – This study adds empirical evidence on the effect of cross-country knowledge spillovers and on the channels through which technological knowledge diffuses globally. It contributes to the emerging empirical research on energy innovation.
We examine how the degree of regional financial integration in African stock markets has evolved over the last eleven years. Despite increasing regional economic…
We examine how the degree of regional financial integration in African stock markets has evolved over the last eleven years. Despite increasing regional economic cooperation, the process of stock market integration has been slow. To facilitate growth via developed financial markets but keep financial stability risk at a minimum, further regional integration should be promoted, and mild capital controls on non-African investors may be necessary. A Diebold-Yilmaz spillover analysis is applied to ten African stock markets for the period between August 2004 and January 2015. We examine spillovers among four regions and among individual countries. Regional integration, as measured by total spillovers in Africa, is increasing but remains very low. These spillovers were temporarily heightened during the global financial crisis. Cross-regional spillovers are high between Northern and Southern Africa. Asymmetric capital controls on African and non-African investors must be considered to foster further regional integration and to mitigate financial stability risk. This is one of the few studies to address the construction of the future architecture of regionally integrated stock markets in emerging countries.
This chapter examines intra-industry spillover effects from inward foreign direct investment (FDI) in Swiss manufacturing firms. It suggests that (a) the assessment of…
This chapter examines intra-industry spillover effects from inward foreign direct investment (FDI) in Swiss manufacturing firms. It suggests that (a) the assessment of spillovers calls upon a detailed analysis of these effects according to the mechanisms by which they occur (viz. the increase of competition, demonstration effects, and worker mobility), and (b) spillovers depend on the interaction between their mechanisms and the levels of domestic absorptive capacity. Results are affirmative in that high-technology firms benefit from FDI heightening competition, while mid-technology firms benefit from demonstration effects. And low-technology firms, which are not able to benefit from foreign affiliates via demonstration effects alone, manage to reap the benefit via the recruitment of MNCs labor. In addition, only firms which largely invest in absorbing foreign technology benefit from spillovers.
We investigate the return and volatility spillovers from major UK banks to Financial Times Stock Exchange 100 (FTSE 100) index using Gaussian estimation and continuous…
We investigate the return and volatility spillovers from major UK banks to Financial Times Stock Exchange 100 (FTSE 100) index using Gaussian estimation and continuous time models as well as discrete time multivariate GARCH (MGARCH) modelling approaches. Using daily, weekly and monthly data over the period December 1999–December 2010, which includes the recent 2007–2009 global financial crisis, empirical estimates of uni- and/or bi-directional return and volatility spillovers are provided. The bivariate MGARCH results reveal strong return spillovers from the FTSE to the banks, and no return spillover from the latter to the FTSE. Nevertheless, strong bi-directional volatility transmission is verified. The continuous time analysis provides mixed evidence of feedback effects over the different models.
Capital markets thrive on information, and the information revolution has transformed these markets all over the world. Investors can now keep track of the movements of…
Capital markets thrive on information, and the information revolution has transformed these markets all over the world. Investors can now keep track of the movements of capital markets in real-time and they react to the flow of information from around the world. One of the concerns of stock market investors is whether the markets operate efficiently, independently, and with sound fundamentals. However, real market movements tend to exhibit a link as is evident from recent market movements across the world.
The assessment of interdependence between stock markets is an important aspect of international portfolio management. The aim of this chapter is to examine the shock and volatility spillover between the Standard and Poor’s 500 (S&P500) index from the United States (US) Stock Exchange and the Istanbul Stock Exchange 100 (BIST100) index from the Stock Exchange Istanbul.
S&P500 index, which is the most important index representing US markets, and BIST100 index, which is the index representing the Turkish market, were used as variables in this study. In the analysis, the causality in variance test was applied to determine the volatility spillover between these two markets. Later, multivariate GARCH (MGARCH) models were used to measure the volatility spillover in the markets. VAR(1)-GARCH (1,1)-Diagonal BEKK model was applied to the daily data to determine the shock and volatility spillover in the markets.
As a result of the variance causality test, it was found that there is a bi-directional volatility spillover between S&P500 index and BIST100 index. When the return spillover between the markets is examined, a one-way spillover from the S&P500 index to the BIST100 index emerged. Diagonal BEKK model results show that each market is affected by its own news (unexpected shocks) and volatility. Furthermore, the volatility is persistent for both markets. These findings demonstrate that the US market and the Turkish market interact with each other.
We present an overview of research on spillover effects within firms and introduce a classification of the literature. We divide spillovers into either technological or…
We present an overview of research on spillover effects within firms and introduce a classification of the literature. We divide spillovers into either technological or social in nature. In our classification, a technological spillover is one in which an agent rationally responds to a cue in the workplace that does not rely on the identity or characteristics of a coworker. Social spillovers, on the other hand, may be thought of as arising from the social preferences of an individual or social norms established in the organization.