Returns and volatility spillovers among cryptocurrency portfolios

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ismail Olaleke Fasanya ◽  
Oluwatomisin Oyewole ◽  
Temitope Odudu
Keyword(s):  
Design Methodology ◽  
Asset Returns ◽  
Volatility Spillover ◽  
Content Type ◽  
Vector Autoregressive ◽  
Volatility Spillovers ◽  
Dynamic Relationship ◽  
Major Market ◽  
Daily Data ◽  
Variance Decompositions

PurposeThis paper examines the return and volatility spillovers among major cryptocurrency using daily data from 10/08/2015 to 15/04/2018.Design/methodology/approachThe authors employ the Dielbold and Yilmaz (2012) spillover approach and rolling sample analysis to capture the inherent secular and cyclical movements in the cryptocurrency market.FindingsThe authors show that there is substantial difference between the behaviour of the cryptocurrency portfolios return and volatility spillover indices over time. The authors find evidence of interdependence among cryptocurrency portfolios given the spillover indices. While the return spillover index reveals increased integration among the currency portfolios, the volatility spillover index experiences significant bursts during major market crises. Interestingly, return and volatility spillovers exhibit both trends and bursts respectively.Originality/valueThis study makes a methodological contribution by adopting Dielbold and Yilmaz (2012) approach to quantify the returns and volatility transmissions among cryptocurrencies. To the best of our knowledge, little or no study has adopted the Dielbold and Yilmaz (2012) methodology to investigate this dynamic relationship in the cryptocurrencies market. The Dielbold and Yilmaz (2012) approach provides a simple and intuitive measure of interdependence of asset returns and volatilities by exploiting the generalized vector autoregressive framework, which produces variance decompositions that are unaffected by ordering.

Volatility spillovers among G7, E7 stock markets and cryptocurrencies

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Berna Aydoğan ◽  
Gülin Vardar ◽  
Caner Taçoğlu
Keyword(s):  
Financial Market ◽  
Portfolio Management ◽  
Stock Markets ◽  
Negative Impact ◽  
Spillover Effects ◽  
Volatility Spillover ◽  
Content Type ◽  
Volatility Spillovers ◽  
Dynamic Relationship ◽  
The Stability

PurposeThe existence of long memory and persistent volatility characteristics of cryptocurrencies justifies the investigation of return and volatility/shock spillovers between traditional financial market asset classes and cryptocurrencies. The purpose of this paper is to investigate the dynamic relationship between the cryptocurrencies, namely Bitcoin and Ethereum, and stock market indices of G7 and E7 countries to analyze the return and volatility spillover patterns among these markets by means of multivariate (MGARCH) approach.Design/methodology/approachApplying the newly developed VAR-GARCH-in mean framework with the BEKK representation, the empirical results reveal that there exists an evidence of mean and volatility spillover effects among Bitcoin and Ethereum as the proxies for the cryptocurrencies, and stock markets reviewed.FindingsInterestingly, the direction of the return and volatility spillover effects is unidirectional in most E7 countries, but bidirectional relationship was found in most G7 countries. This can be explained as the presence of a strong return and volatility interaction among G7 stock markets and crypto market.Originality/valueOverall, the results of this study are of particular interest for portfolio management since it provides insights for financial market participants to make better portfolio allocation decisions. It is also increasingly important to understand the volatility transmission mechanism across these markets to provide policymakers and regulatory bodies with guidance to eliminate the negative impact of cryptocurrency's volatility on the stability of financial markets.

Shock and volatility spillovers between oil and emerging seven stock markets

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Muzammil Khurshid ◽  
Berna Kirkulak-Uludag
Keyword(s):  
Stock Returns ◽  
Stock Markets ◽  
Design Methodology ◽  
Spillover Effects ◽  
Volatility Spillover ◽  
Causality Test ◽  
Content Type ◽  
Volatility Spillovers ◽  
Oil Market ◽  
High Level

Purpose This study aims to examine the volatility spillover effects between oil and stock returns in the emerging seven economies. Design/methodology/approach In this study, the Granger causality test and vector autoregression-generalized autoregressive conditional heteroskedasticity approach to analyze the volatility spillover from 1995 to 2019 were used. The findings provide evidence of significant volatility spillover between oil and Brazil, China, India, Indonesia, Mexico, Russia and Turkey (E7) stock markets. Findings All emerging seven stock markets exhibit positive and low constant conditional correlations with oil assets. The magnitude of the correlation changes in respond to the country’s net position in the crude oil market. While a relatively high level of correlation exists between oil and the stock markets of net oil-exporting countries, a relatively low level of correlation exists between oil and the stock markets of net oil-importing countries. Originality/value The findings suggest that oil asset improves the risk-adjusted performance of a well-diversified portfolio of stocks. However, investors should invest a larger portion of their portfolios in E7 stock markets than in oil.

Volatility discovery in cryptocurrency markets

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Thomas Dimpfl ◽  
Dalia Elshiaty
Keyword(s):  
Design Methodology ◽  
Stochastic Volatility Model ◽  
Discrete Version ◽  
Data Series ◽  
Content Type ◽  
Vector Autoregressive ◽  
Volatility Model ◽  
Information Shares ◽  
The Common ◽  
To Come

PurposeCryptocurrency markets are notoriously noisy, but not all markets might behave in the exact same way. Therefore, the aim of this paper is to investigate which one of the cryptocurrency markets contributes the most to the common volatility component inherent in the market.Design/methodology/approachThe paper extracts each of the cryptocurrency's markets' latent volatility using a stochastic volatility model and, subsequently, models their dynamics in a fractionally cointegrated vector autoregressive model. The authors use the refinement of Lien and Shrestha (2009, J. Futures Mark) to come up with unique Hasbrouck (1995, J. Finance) information shares.FindingsThe authors’ findings indicate that Bitfinex is the leading market for Bitcoin and Ripple, while Bitstamp dominates for Ethereum and Litecoin. Based on the dominant market for each cryptocurrency, the authors find that the volatility of Bitcoin explains most of the volatility among the different cryptocurrencies.Research limitations/implicationsThe authors’ findings are limited by the availability of the cryptocurrency data. Apart from Bitcoin, the data series for the other cryptocurrencies are not long enough to ensure the precision of the authors’ estimates.Originality/valueTo date, only price discovery in cryptocurrencies has been studied and identified. This paper extends the current literature into the realm of volatility discovery. In addition, the authors propose a discrete version for the evolution of a markets fundamental volatility, extending the work of Dias et al. (2018).

Deviations from fundamental value and future closed-end country fund returns

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Luis Berggrun ◽  
Emilio Cardona ◽  
Edmundo Lizarzaburu
Keyword(s):  
Control Variable ◽  
Asset Returns ◽  
Weak Form ◽  
Share Price ◽  
Local Market ◽  
Impulse Response Functions ◽  
Content Type ◽  
Vector Autoregressive ◽  
Causality Tests ◽  
Fundamental Value

PurposeThis article examines whether deviations from fundamental value or closed-end country fund's discounts or premiums forecast future share price returns or net asset returns.Design/methodology/approachThe main empirical (econometric) tool is a vector autoregressive (VAR) model. The authors model share price returns and net asset returns as a function of their lagged values, the discounts or premiums, and a control variable for local market returns. The authors also conduct Dickey Fuller and Granger causality tests as well as impulse response functions.FindingsIt was found that deviations from fundamental value do predict share price returns. This predictability is contrary to weak-form market efficiency. Premiums or discounts predict net asset returns but weakly.Originality/valueThe findings point to the idea that the closed-end fund market is somewhat predictable and inefficient (in its weak form) since the market appears to be able to anticipate a fund's future returns using information contained in the premiums (or discounts). In particular, the market has the ability to anticipate future behaviour because growing premiums forecast declining share price returns for one or two periods ahead.

scholarly journals An analysis of CEE equity market integration and their volatility spillover effects

2019 ◽  
Vol 29 (1) ◽  
pp. 23-40 ◽  
Author(s):  
Ngo Thai Hung
Keyword(s):  
Market Integration ◽  
Design Methodology ◽  
Spillover Effects ◽  
Equity Market ◽  
Multivariate Garch ◽  
The Czech Republic ◽  
Content Type ◽  
Volatility Spillovers ◽  
Conditional Correlations ◽  
Practical Implications

Purpose The purpose of this paper is to examine the conditional correlations and spillovers of volatilities across CEE markets, namely, Hungary, Poland, the Czech Republic, Romania and Croatia, in the post-2007 financial crisis period. Design/methodology/approach The authors use five-dimensional GARCH-BEKK alongside with the CCC and DCC models. Findings The estimation results of the three models generally demonstrate that the correlations between these markets are particularly significant. Also, own-volatility spillovers are generally lower than cross-volatility spillovers for all markets. Practical implications These results recommend that investors should take caution when investing in the CEE equity markets as well as diversifying their portfolios so as to minimize risk. Originality/value Unlike the previous studies in this field, this paper is the first study using multivariate GARCH-BEKK alongside with CCC and DCC models. The study makes an outstanding contribution to the existing literature on spillover effects and conditional correlations in the CEE financial stock markets.

Cryptocurrency connectedness nexus the COVID-19 pandemic: evidence from time-frequency domains

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Onur Polat ◽  
Eylül Kabakçı Günay
Keyword(s):  
Financial Distress ◽  
World Health ◽  
Volatility Spillover ◽  
Content Type ◽  
Frequency Bands ◽  
Market Capitalization ◽  
Time Frequency ◽  
Long Cycle ◽  
Volatility Spillovers ◽  
Before And After

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.

The connectedness between Sukuk and conventional bond markets and the implications for investors

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Aristeidis Samitas ◽  
Spyros Papathanasiou ◽  
Drosos Koutsokostas
Keyword(s):  
Asset Allocation ◽  
Spillover Effects ◽  
Bond Markets ◽  
Data Set ◽  
Content Type ◽  
Vector Autoregressive ◽  
Volatility Spillovers ◽  
Exogenous Shocks ◽  
Time Varying Parameter ◽  
Optimal Hedge

Purpose The purpose of this paper is to examine the connectedness across a variety of Sukuk and conventional bond indices and the implications for optimal asset allocation for the period January 1, 2010–April 30, 2020. Design/methodology/approach The data set consists of five major Sukuk (Dow Jones Sukuk, Thompson Reuters BPA Malaysia Sukuk, Indonesia Government Sukuk, S&P MENA Sukuk and Tadawul Sukuk and Bonds Index) and five conventional bond indexes, one for developed (USA) and four for emerging markets (Malaysia, Indonesia, Africa and Qatar). This study investigates the connectedness and volatility spillover effects across the aforementioned indices, by following the Diebold and Yilmaz (2012) approach, based on the time-varying parameter vector autoregressive (TVP-VAR) model. In addition, this paper provides optimal hedge ratios and portfolio weights for investors. Findings The empirical results show that Sukuk and conventional bond markets are highly integrated and that total connectedness exhibits sensitivity to exogenous shocks. The Dow Jones and the Malaysian Sukuk indices are the primary shock transmitters to other markets. However, the weak volatility spillovers between the Dow Jones and conventional bonds suggest that opportunities for optimal asset allocation may in fact exist. The highest (lowest) hedging effectiveness can be achieved by taking a short position in Malaysian (Qatarian) bonds. Originality/value To the best of the knowledge, this is the largest sample taken into account to investigate the connectedness between Sukuk and conventional bonds.

Dynamic and frequency connectedness across Islamic stock indexes, bonds, crude oil and gold

2019 ◽  
Vol 12 (3) ◽  
pp. 306-321 ◽  
Author(s):  
Nader Trabelsi
Keyword(s):  
Crude Oil ◽  
The United States ◽  
Asia Pacific ◽  
Volatility Spillover ◽  
Content Type ◽  
Time Frequency ◽  
Volatility Spillovers ◽  
Long Run ◽  
Portfolio Managers ◽  
Stock Indexes

Purpose This paper aims to investigate the connectedness of Islamic Stock Markets in five regional financial systems, namely, the United States, the United Kingdom, Europe (EU), GCC (Gulf Cooperation Council) and APAC (Asia-Pacific Countries), and across different asset classes (i.e. bonds, gold and crude oil). Design/methodology/approach This methodology is inspired by Diebold and Yilmaz (2012) and Barunlik and Krehlik (2017) for performing dynamic variance decomposition network and for studying time–frequency dynamics of connectedness at different frequencies. Findings Results show that the nature of connectedness over the past decade is time–frequency dynamics. The decomposition of the total volatility spillovers is mostly dominated by the long-run component. Furthermore, dominant regions are the largest contributors of spillover index, with the lowest contribution in the system coming from the GCC market. Results also reveal a slightly higher volatility spillover index of Islamic than conventional equity indexes. Finally, the system that encompasses commodities and Islamic finance instruments, generates the much lower volatility spillover. Originality/value The findings have significant implications for portfolio managers who are interested in being able to predict asset returns, as well as for policymakers who are concerned with market stability.

Stock market predictability

2014 ◽  
Vol 31 (4) ◽  
pp. 354-370 ◽  
Author(s):  
Silvio John Camilleri ◽  
Christopher J. Green
Keyword(s):  
Stock Market ◽  
High Frequency ◽  
Serial Correlation ◽  
Design Methodology ◽  
High Frequency Data ◽  
Frequency Data ◽  
Content Type ◽  
Lag Effects ◽  
Daily Data ◽  
Indian Stock Market

Purpose – The main objective of this study is to obtain new empirical evidence on non-synchronous trading effects through modelling the predictability of market indices. Design/methodology/approach – The authors test for lead-lag effects between the Indian Nifty and Nifty Junior indices using Pesaran–Timmermann tests and Granger-Causality. Then, a simple test on overnight returns is proposed to infer whether the observed predictability is mainly attributable to non-synchronous trading or some form of inefficiency. Findings – The evidence suggests that non-synchronous trading is a better explanation for the observed predictability in the Indian Stock Market. Research limitations/implications – The indication that non-synchronous trading effects become more pronounced in high-frequency data suggests that prior studies using daily data may underestimate the impacts of non-synchronicity. Originality/value – The originality of the paper rests on various important contributions: overnight returns is looked at to infer whether predictability is more attributable to non-synchronous trading or to some form of inefficiency; the impacts of non-synchronicity are investigated in terms of lead-lag effects rather than serial correlation; and high-frequency data is used which gauges the impacts of non-synchronicity during less active parts of the trading day.

An investigation of price discovery and volatility spillovers in India’s foreign exchange market

2015 ◽  
Vol 42 (2) ◽  
pp. 261-284 ◽  
Author(s):  
Sanjay Sehgal ◽  
Wasim Ahmad ◽  
Florent Deisting
Keyword(s):  
Price Discovery ◽  
Stock Exchange ◽  
Futures Market ◽  
Volatility Spillover ◽  
Futures Prices ◽  
Content Type ◽  
Volatility Spillovers ◽  
Currency Market ◽  
Short Run

Purpose – The purpose of this paper is to examine the price discovery and volatility spillovers in spot and futures prices of four currencies (namely, USD/INR, EURO/INR, GBP/INR and JPY/INR) and between futures prices of both stock exchanges namely, Multi-Commodity Stock Exchange (MCX-SX) and National Stock Exchange (NSE) in India. Design/methodology/approach – The study applies cointegration test of Johansen’s along with VECM to investigate the price discovery. GARCH-BEKK model is used to examine the volatility spillover between spot and futures and between futures prices. The other two models namely, constant conditional correlation and dynamic conditional correlation are used to demonstrate the constant and time-varying correlations. In order to confirm the volatility spillover results, the study also applies test of directional spillovers suggested by Diebold and Yilmaz (2009, 2012). Findings – The results of the study show that there is long-term equilibrium relationship between spot and futures and between futures markets. Between futures and spot prices, futures price appears to lead the spot price in the short-run. Volatility spillover results indicate that the movement of volatility spillover takes place from futures to spot in the short-run while spot to futures found in the long-run. However, the results of between futures markets exhibit the dominance of MCX-SX over NSE in terms of volatility spillovers. By and large, the findings of the study indicate the important role of futures market in price discovery as well as volatility spillovers in India’s currency market. Practical implications – The results highlight the role of futures market in the information transmission process as it appears to assimilate new information quicker than spot market. Hence, policymakers in emerging markets such as India should focus on the development of necessary institutional and fiscal architecture, as well as regulatory reforms, so that the currency market trading platforms can achieve greater liquidity and efficiency. Originality/value – Due to recent development of currency futures market, there is dearth of literature on this subject. With the apparent importance of currency market in recent time, this study attempts to study the efficient behavior of currency market by way of examining the price discovery and volatility spillovers between spot and futures and between futures prices of four currencies traded on two platforms. The study has strong implications for India’s stock market especially at the time when its currency is under great strain owing to the adverse impact of global financial crisis.

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