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1 – 10 of 226Yadong Liu, Nathee Naktnasukanjn, Anukul Tamprasirt and Tanarat Rattanadamrongaksorn
Bitcoin (BTC) is significantly correlated with global financial assets such as crude oil, gold and the US dollar. BTC and global financial assets have become more closely related…
Abstract
Purpose
Bitcoin (BTC) is significantly correlated with global financial assets such as crude oil, gold and the US dollar. BTC and global financial assets have become more closely related, particularly since the outbreak of the COVID-19 pandemic. The purpose of this paper is to formulate BTC investment decisions with the aid of global financial assets.
Design/methodology/approach
This study suggests a more accurate prediction model for BTC trading by combining the dynamic conditional correlation generalized autoregressive conditional heteroscedasticity (DCC-GARCH) model with the artificial neural network (ANN). The DCC-GARCH model offers significant input information, including dynamic correlation and volatility, to the ANN. To analyze the data effectively, the study divides it into two periods: before and during the COVID-19 outbreak. Each period is then further divided into a training set and a prediction set.
Findings
The empirical results show that BTC and gold have the highest positive correlation compared with crude oil and the USD, while BTC and the USD have a dynamic and negative correlation. More importantly, the ANN-DCC-GARCH model had a cumulative return of 318% before the outbreak of the COVID-19 pandemic and can decrease loss by 50% during the COVID-19 pandemic. Moreover, the risk-averse can turn a loss into a profit of about 20% in 2022.
Originality/value
The empirical analysis provides technical support and decision-making reference for investors and financial institutions to make investment decisions on BTC.
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Susovon Jana and Tarak Nath Sahu
This study aims to investigate the possibilities of cryptocurrencies as hedges and diversifiers in the Indian stock market before and during financial crisis due to the pandemic…
Abstract
Purpose
This study aims to investigate the possibilities of cryptocurrencies as hedges and diversifiers in the Indian stock market before and during financial crisis due to the pandemic and the Russia–Ukraine war.
Design/methodology/approach
Researchers have used daily data on cryptocurrencies and Indian stock prices from March 10, 2015 to August 26, 2022. The researchers have used the dynamic conditional correlations (DCC)-GARCH model to determine the volatility spillover and dynamic correlation between stocks and digital currencies. Further, researchers have explored hedge ratio, portfolio weight and hedging effectiveness using the estimates of the DCC-GARCH model.
Findings
The findings indicate a negative conditional correlation between equities and cryptocurrencies before the crisis and a positive conditional correlation except for Tether during the crisis. Which implies that cryptocurrencies serve as a hedging asset in the stock market before a crisis but are not more than a diversifier during the crisis, except for Tether. Notably, Tether serves as a safe haven during times of crisis. Finally, the study suggests that Bitcoin, Ethereum, Binance Coin and Ripple are the most effective diversifiers for Indian stocks during the crisis.
Originality/value
This study makes several contributions to the existing literature. First, it compares the hedge and diversification roles of cryptocurrencies in the Indian stock market before and during crisis. Second, the study findings provide insights on risk hedging and can serve as a guide for investors. Third, it may help rational investors avoid underestimating risk while constructing portfolios, particularly in times of financial turmoil.
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Shailesh Rastogi and Jagjeevan Kanoujiya
This study aims to analyze the volatility spillover effects of crude oil, gold price, interest rate (yield) and the exchange rate (USD (United States Dollar)/INR (Indian National…
Abstract
Purpose
This study aims to analyze the volatility spillover effects of crude oil, gold price, interest rate (yield) and the exchange rate (USD (United States Dollar)/INR (Indian National Rupee)) on inflation volatility in India.
Design/methodology/approach
This study uses the multivariate Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models (Baba, Engle, Kraft and Kroner [BEKK]-GARCH and dynamic conditional correlation [DCC]-GARCH) to examine the volatility spillover effect of macroeconomic indicators and strategic commodities on inflation in India. The monthly data are collected from January 2000 till December 2020 for the crude oil price, gold price, interest rate (5-year Indian bond yield), exchange rate (USD/INR) and inflation (wholesale price index [WPI] and consumer price index [CPI]).
Findings
In BEKK-GARCH, the results reveal that crude oil price volatility has a long time spillover effect on inflation (WPI). Furthermore, no significant short-term volatility effect exists from crude oil market to inflation (WPI). However, the short-term volatility effect exists from crude oil to inflation while considering CPI as inflation. Gold price volatility has a bidirectional and negative spillover effect on inflation in the case of WPI. However, there is no price volatility spillover effect from gold to inflation in the case of CPI. The price volatility in the exchange rate also has a negative spillover effect on inflation (but only on CPI). Furthermore, volatility of interest rates has no spillover effect on inflation in WPI or CPI. In DCC-GARCH, a short-term volatility impact from all four macroeconomic indicators to inflation is found. Only crude oil and exchange rate have long-term volatility effect on inflation (CPI).
Practical implications
In an economy, inflation management is an essential task. The findings of the current study can be beneficial in this endeavor. The knowledge of the volatility spillover effect of all the four markets undertaken in the study can be significantly helpful in inflation management, especially for inflation-targeting policy.
Originality/value
It is observed that no other study has addressed this issue. We do not find any other research which studies the volatility spillover effect of gold, crude oil, interest rate and exchange rate on the inflation volatility. The current study is novel with a significant contribution to the vast knowledge in this context.
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Sruti Mundra and Motilal Bicchal
The purpose of this study is to assess alternative financial stress indicators for India in terms of tracing crisis events, mapping with the business cycle and the macroeconomic…
Abstract
Purpose
The purpose of this study is to assess alternative financial stress indicators for India in terms of tracing crisis events, mapping with the business cycle and the macroeconomic effect of stress indices.
Design/methodology/approach
The study constructs the composite indicator of systemic stress of Hollo, Kremer and Lo Duca (2012) for India using two different methods for computing time-varying cross-correlation matrix, namely, exponentially weighted moving average (EWMA) and dynamic conditional correlation-generalized autoregressive conditional heteroscedasticity (DCC-GARCH). The derived indices are evaluated with widely used, equal variance and principal component weighting indices in terms of tracing stress events, mapping with the business cycles and the macroeconomic effect. For this purpose, the study identifies various episodes of financial stress and uses the business cycle dates in the sample covering from January 2001 to October 2018.
Findings
The results suggest that stress indices based on EWMA and DCC-GARCH accurately identify the well-known stress periods and capture the recession dates and show an adverse effect on economic activity. Primarily, the DCC-GARCH-based stress index emerges as a better indicator of stress because it efficiently locates all the major-minor events, traces the build-up of stress and reverts to the normal level during stable times.
Practical implications
The DCC-GARCH-based stress index is a very useful indicator for policymakers in regularly monitoring India’s financial conditions and providing timely identification of systemic stress to avoid adverse repercussion effects of the financial crisis.
Originality/value
The 2007–2008 financial crisis and subsequent recurrent instability in the financial markets highlighted the requirement for an appropriate financial stress indicator for a timely assessment of the system-wide financial stress. To the authors’ knowledge, this is the first study that incorporates the systemic nature of financial stress in the construction of stress indices for India and provides a holistic evaluation of the financial stress from an emerging country’s perspective.
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Sang Hoon Kang and Seong-Min Yoon
This paper investigates the price discovery, volatility spillover, and asymmetric volatility spillover effects between the KOSPI 200 market and its futures contracts market. The…
Abstract
This paper investigates the price discovery, volatility spillover, and asymmetric volatility spillover effects between the KOSPI 200 market and its futures contracts market. The investigation was performed using the VECM-DCC-GARCH approach. In the case of returns, we found a significant unidirectional information flow from the futures market to the spot market; this implies that the KOSPI 200 futures market plays an important role on the price discovery in the spot market. In addition, we found a strong bi-directional casualty involving the volatility interaction between the spot and futures markets; this implies that market volatility originating in the spot market will influence the volatility of the futures market and vice versa. We also found strong asymmetric volatility spillover effects between the two markets.
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Sarra Gouta and Houda BenMabrouk
This study aims at exploring the nexus between herding behavior and the spillover effect in G7 and BRICS stock markets.
Abstract
Purpose
This study aims at exploring the nexus between herding behavior and the spillover effect in G7 and BRICS stock markets.
Design/methodology/approach
The authors used the dynamic connectedness approach TVP-VAR model of Antonakakis et al. (2019) to capture the spillovers across different markets. Moreover, to explore herding behavior, the authors used a modified version of the CSAD measure of Chang et al. (2000) including extreme market movements. Finally, to study the link between these two phenomena, the authors estimated a DCC-GARCH model.
Findings
The results show that herding behavior exists in the American market and some BRICS markets. Furthermore, spillover between G7 and BRICS increases in times of crisis. Moreover, the authors find a dynamic conditional correlation between herding behavior and spillovers both in the short and long run. The authors conclude that in times of crisis, the transmission of shocks between markets is more frequent, fuelling uncertainty and pushing investors to suppress their own beliefs and follow the general market trends.
Originality/value
This paper uses the TVP-VAR model to explore the spillover effect and the DCC-GARCH model to explore the connectedness between herding behavior and the spillover effect in G7 and BRICS countries in both the short and long run.
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Gaytri Malhotra, Miklesh Prasad Yadav, Priyanka Tandon and Neena Sinha
This study unravels an attempt to investigate the dynamic connectedness of agri-commodity (wheat) of Russia with 10 financial markets of wheat importing counties during the…
Abstract
Purpose
This study unravels an attempt to investigate the dynamic connectedness of agri-commodity (wheat) of Russia with 10 financial markets of wheat importing counties during the Russia–Ukraine invasion.
Design/methodology/approach
This study took the daily prices of Wheat FOB Black Sea Index (Russia) along with stock indices of 10 major wheat-importing nations of Russia and Ukraine. The time frame for this study ranges from February 24, 2022 to July 31, 2022. This time frame was selected since it fully examines all of the effects of the crisis. The conditional correlations and volatility spillovers of these indices are predicted using the DCC-GARCH model, Diebold and Yilmaz (2012) and Baruník and Křehlík (2018) models.
Findings
It is found that there is dynamic linkage of agri-commodity of with stock markets of Iraq, Pakistan and Tanzania in short run while stock markets of Egypt, Turkey, Bangladesh, Pakistan, Brazil and Iraq are spilled by agri-commodity in long run. In addition, it documents that there is large spillover in short run than medium and long run comparatively. This signifies that investors have more diversification opportunity in short run then long run contemplating to invest in these markets.
Originality/value
To the best of the authors’ understanding this is the first study to undertake the dynamic linkage of agri-commodity (wheat) of Russia with financial market of select importing counties during the Russia–Ukraine invasion.
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Sang Hoon Kang and Seong-Min Yoon
This paper investigates the impact of structural breaks on volatility spillovers between Asian stock markets (China, Hong Kong, India, Indonesia, Japan, Korea, Singapore, and…
Abstract
This paper investigates the impact of structural breaks on volatility spillovers between Asian stock markets (China, Hong Kong, India, Indonesia, Japan, Korea, Singapore, and Taiwan) and the oil futures market. To this end, we apply the bivariate DCC-GARCH model to weekly spot indices during the period 1998-2015. The results reveal significant volatility transmission for the pairs between the Asian stock and oil futures markets. Moreover, we find a significant variability in the time-varying conditional correlations between the considered markets during both bullish and bearish markets, particularly from early 2007 to the summer of 2008. Using the modified ICSS algorithm, we find several sudden changes in these markets with a common break date centred on September 15, 2008. This date corresponds to the collapse of Lehman Brothers which is considered as our breakpoint to define the global financial crisis. Also, we analyse the optimal portfolio weights and time-varying hedge ratios based on the estimates of the multivariate DCC-GARCH model. The results emphasize the importance of overweighting optimal portfolios between Asian stock and the oil futures markets.
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This study investigates the impact of the Russia–Ukraine war (2022) on the volatility connectedness between Egyptian stock market sectors.
Abstract
Purpose
This study investigates the impact of the Russia–Ukraine war (2022) on the volatility connectedness between Egyptian stock market sectors.
Design/methodology/approach
This study employs the newest dynamic conditional correlation (DCC)-generalized autoregressive conditional heteroskedasticity (GARCH)-CONNECTEDNESS approach to examine volatility connectedness in a sample of ten sectors in the Egyptian stock market, namely banks, education, food, healthcare, industry, information technology, real estate, resources, transportation and travel, ranging from February 1, 2019 to May 31, 2022.
Findings
The findings show that connectedness among the Egyptian stock market sectors varies depending on the time. The average dynamic connectedness measure among sectors in Egypt is 73.24%. This average was 85.63% during the Russia–Ukraine War (2022). The author also shows that the transportation sector is the most significant net transmitter of volatility in the remaining sectors during the Russia–Ukraine War (2022).
Practical implications
This study intends for policymakers to examine the co-movements, market variations and volatility spillover of stock markets, particularly during crises. Furthermore, the results help investors gain insight into diversifying the investors' portfolio assets to optimize profits.
Originality/value
To the best of the authors' knowledge, no study has investigated the implications of the war between Russia and Ukraine (2022) on sectoral interconnectedness within the stock markets in any country and discussion and empirical evidence from African countries are lacking. This study fills this gap in the literature. Additionally, the author uses the newest approach, the DCC-GARCH-CONNECTEDNESS approach, to describe the time-varying volatility spillover between economic sectors in Egypt.
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Farman Afzal, Ayesha Shehzad, Hafiz Muhammad Rehman, Fahim Afzal and Mohammad Mushfiqul Haque Mushfiqul Haque Mukit
Cost estimation is a major concern while planning projects on public–private partnership (PPP) terms in developing countries. To bridge the gap of the right approximation of cost…
Abstract
Purpose
Cost estimation is a major concern while planning projects on public–private partnership (PPP) terms in developing countries. To bridge the gap of the right approximation of cost of capital, this study aims to sermon a significant role of investor’s risk perception as unsystematic risk in PPP-based energy projects.
Design/methodology/approach
To investigate the effective mechanism of determining cost of capital and valuing the capital budgeting, a pure-play method has been acquired to measure systematic risk. In addition, dynamic conditional correlation (DCC) and generalized autoregressive conditional heteroscedasticity (GARCH) models have been applied to calculate weighted average cost of capital.
Findings
Initially, a joint cost of capital of energy-related projects has been calculated using DCC-GARCH and pure-play method. Investors risk perception has been discussed through market point of view using country risk premium modeling. Latter yearly betas have been calculated using DCC signifying the final outcomes that applying a dynamic model can provide a better cost estimation in emerging economies.
Practical implications
The findings are implicating that due to the involvement of international investors, domestic risk is linked with country risk. In such situations, market-related information is a key factor to find out the market performance, helping in the estimation of cost of capital through capital asset pricing model (CAPM). High dynamic nature of emerging economies causes an impediment in the estimation of cost of capital. Consequently, to calculate risk in dynamic markets, this study has acquired DCC model that can predict the value of beta factor.
Originality/value
Study contributes to the body of knowledge by addressing an important issue of investor’s risk perception and effective implication of CAPM using pure-play and DCC-GARCH when data is not promptly available in dynamic situations.
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