Time-varying Parameter Vector Autoregression Research Articles

Do clean and dirty cryptocurrencies connect with financial assets differently? The role of economic policy uncertainty

This paper analyzes time-varying networks of clean and dirty cryptocurrencies with green and traditional assets through a dynamic connectedness approach established by the time-varying parameter vector autoregressive (TVP-VAR) model. The underlying asymmetry of the dynamic pairwise connectedness when facing uncertainty shocks is further studied through a non-parametric quantile causality method. Our results demonstrate a limited information transmission of volatility from cryptocurrencies to both traditional and green assets, while the connection of clean cryptocurrencies (CI) with the financial system is even weaker compared to that of dirty cryptocurrencies (DI), especially after the COVID-19 pandemic. In contrast, connection within the financial system is found to be relatively closer. Moreover, causal relationships between economic policy uncertainty (EPU) and cryptocurrency-financial asset linkages are generally enhanced after the pandemic onset, while such the causality of uncertainty with DI related asset linkages tends to be even stronger. Most of the above causalities are shown to be negligible during market depression, further implying the sheltering role of the market linkages against uncertainty.

Extreme risk dependence and time-varying spillover between crude oil, commodity market and inflation in China

The fluctuation of general commodity prices, particularly crude oil prices, exerts a substantial influence on the economic development of all countries. Based on the mean and quantile spillover index of time-varying parameter vector autoregressive (TVP-VAR), we measure the spillover effects among crude oil, commodity market, and inflation in China under normal and extreme conditions from April 2018 to January 2023. The results show the interdependence among crude oil, commodity markets, and inflation; however, the degree of correlation varies at different quantiles. Notably, the spillover index is significantly higher at the extreme quantile, indicating that extreme risks can easily affect the spillover effect among crude oil, commodity markets, and inflation. Meanwhile, the tail risk spillover effect among crude oil, commodity markets, and inflation exhibits asymmetry, indicating that the spillover effect is greater during downward market conditions than upward ones. Furthermore, oil prices serve as a significant provider of systemic risk, both in normal market conditions and during times of extreme risk. In most cases, oil prices transfer more risks to inflation and commodity markets, commodity markets transfer more risks to inflation, and the paired spillover effects between commodity markets fluctuate over time. Finally, our results hold significant implications for investors, market regulators, and policymakers.

Higher-order moment risk spillovers and optimal portfolio strategies in global oil markets

Investigating the dynamic interdependencies and risk connectedness in global oil markets is paramount for investors and regulatory bodies. Nevertheless, prevailing research has predominantly focused on the examination of lower-order moments, thereby imposing limitations on the extent of inquiry. This paper undertakes an analysis of higher-order moment risk spillovers in global oil markets employing the innovative time-varying parameter vector autoregression (TVP-VAR) extended joint connectedness approach. Moreover, this study pioneers the construction of minimum connectedness portfolio strategies encompassing oil markets worldwide, incorporating the outcomes of higher-order moment risk connectedness into multivariate portfolio construction. Our empirical investigation unequivocally highlights the ascendance of OPEC oil as the preeminent net transmitter of connectedness across all moments, reinforcing its pivotal role in the transmission of risk. Furthermore, our findings disclose significant heterogeneity in the connectedness outcomes, with discernible variations across diverse moment orders. Remarkably, the oil benchmarks of WTI and Brent undergo a discernible transformation, transitioning into net recipients when subjected to higher-order moment risk spillovers. Additionally, the dynamics of equicorrelations, pairwise correlations, as well as the time-varying total, net, and net-pairwise connectedness indices, exhibit a high degree of responsiveness to major crisis events. Finally, the incorporation of higher-order moment risk spillovers into oil portfolio strategies manifests enhanced efficacy in risk management. The policy implications derived from our findings bear practical significance for an array of stakeholders, including cross-market investors, portfolio managers, regulatory entities, and policymakers.

Fluctuations in gold prices in Vietnam during the COVID-19 pandemic: Insights from a time-varying parameter autoregression model

Vietnam ranks 37th in the world's ranking of national economies but has the largest gold consumption in Southeast Asia and the 7th largest gold consumption in the world. The price of gold in Vietnam often increases substantially in an abnormal way. Therefore, we employ a time-varying parameter vector autoregression approach based on extended joint connectedness to explain Vietnam's high gold price. We analyze the connectedness between five markets—the foreign exchange, the Vietnamese stock, gold, crude oil, and cryptocurrency markets—and the global gold traded in Vietnam from January 1, 2018, to August 1, 2021. Our results show heterogeneous connections at the system level. Vietnam's gold market appears to act as a net receiver of shocks, while other markets, such as cryptocurrencies, foreign exchanges, and stocks, act as net transmitters. One of the most critical transmitters among these channels is gold. The gold market in Vietnam has behaved differently over the course of the COVID-19 pandemic than it did in the past. The Vietnamese gold market appears to be a net receiver during uncertain periods, whereas it is a net transmitter in the normal course of events. Crude oil and world gold act as long-term transmitters of shock during the pandemic period. By contrast, cryptocurrency accounts for an insignificant portion of the fluctuations in Vietnam's gold market, and its role is limited to the short term.

Interconnectedness of Cryptocurrency Uncertainty Indices with Returns and Volatility in Financial Assets during COVID-19

This paper investigates the dynamic relationship between cryptocurrency uncertainty indices and the movements in returns and volatility across spectrum of financial assets, comprising cryptocurrencies, precious metals, green bonds, and soft commodities. It employs a Time-Varying Parameter Vector Autoregressive (TVP-VAR) connectedness approach; the analysis covers both the entire sample period spanning August 2015 to 31 December 2021 and the distinct phase of COVID-19 pandemic. The findings of the study reveal the interconnectedness of returns within these asset classes during the COVID-19 pandemic. In this context, cryptocurrency uncertainty indices emerge as influential transmitters of shocks to other financial asset categories and it significantly escalates throughout the crisis period. Additionally, the outcomes of the study imply that during times of heightened uncertainty, exemplified by events such as the COVID-19 pandemic, the feasibility of portfolio diversification for investors might be constrained. Consequently, the amplified linkages between financial assets through both forward and backward connections could potentially compromise financial stability. This research sheds light on the impact of cryptocurrency uncertainty on the broader financial market, particularly during periods of crisis. The findings have implications for investors and policymakers, emphasizing the need for a comprehensive understanding of the interconnectedness of financial assets and the potential risks associated with increased interdependence. By recognizing these dynamics, stakeholders can make informed decisions to enhance financial stability and manage portfolio risk effectively.

Do climate policy uncertainty and investor sentiment drive the dynamic spillovers among green finance markets?

Green finance is an essential instrument for improving the environment and addressing climate change. This study investigates the dynamic spillovers among green finance markets using time-varying parameter vector autoregression (TVP-VAR) spillover indices, and further investigates the impact of climate policy uncertainty and investor sentiment on spillovers based on the generalised autoregressive conditional heteroscedasticity mixed data sampling (GARCH-MIDAS) model. The results indicate that: (i) environmental, social and governance (ESG), clean energy and water markets are information transmitters in the green finance system, whereas green building, green transportation, green bond and carbon markets are mainly information receivers; (ii) green stock markets including clean energy, non-energy and ESG markets transmit and receive greater information in the green finance system, while green bond and carbon markets do less; (iii) the green bond market is more interconnected with other green finance markets after the COVID-19 outbreak; (iv) investor sentiment contributes more to the net total directional spillovers of green resource markets (water and clean energy), while climate policy uncertainty contributes more to total spillovers and the net total directional spillovers of other green finance markets. These findings offer invaluable guidance for both policymakers and environmental investors.

Global geopolitical risk and volatility connectedness among China's sectoral stock markets

Employing the time-varying parameter vector autoregressive (TVP-VAR) connectedness index and nonparametric causality-in-quantiles test, this paper investigates the causal effect of global geopolitical risk on the dynamic volatility connectedness within China's sectoral stock markets. The TVP-VAR connectedness index results reveal significant volatility connectedness among China's stock market sectors, and the industrial, consumer discretionary, and raw material sectors play a critical systemic role throughout the sample period. The Granger causality test results show that the causality-in-quantiles test demonstrates superior performance compared to the linear Granger causality test, and it is evident that global geopolitical risk exhibits significant nonlinear causality effects on both the overall volatility connectedness among sectors and the net volatility connectedness across different sectors.

Interoperability of the revolutionary blockchain architectures and Islamic and conventional technology markets: Case of Metaverse, HPB, and Bloknet

This study examines how Islamic and conventional technology stock indices interact with blockchain technology assets including Metaverse, High-Performance Blockchain, and Blocknet, throughout different market conditions and horizons. A three-pronged approach is employed, namely the quantile cross-spectral coherency approach, the time-varying parameter vector autoregressions (TVP-VAR), and the Causality in quantiles methodology. The quantile coherency results show that connectivity between new generation blockchains and conventional and Islamic markets varies. It was found that the connectedness of the network of variables rises in the medium run and reaches its peak in the long run as a result of the TVP-VAR approach. It is evident from the results that the application of sharia screening only impacts the relationship between the technology stock index and digital assets significantly, but does not affect the connectedness and causality between the two. Technology managers, policymakers, and blockchain designers are able to gain new insights from these research findings.

Volatility Spillover and Directionality in Cryptocurrency and Metal Markets

This article investigates the dynamic relationship between cryptocurrencies and metals, examining the existence and direction of volatility spillovers. While previous studies have explored the relationships between different cryptocurrencies and between base metals and gold, there is a notable gap in understanding the volatility spillover nexus among cryptocurrencies. This study makes a significant contribution by employing the Time-Varying-Parameter-Vector-Autoregressive (TVP-VAR) total connectedness measure to assess the strength of association between these assets. Our analysis employs 10-year daily returns data for three cryptocurrencies (Bitcoin, Litecoin, and Ethereum) and two metals (Gold and Copper). As we witness major economic events worldwide, this study is particularly relevant, as it provides insights into potential hedging opportunities. To comprehend the risk contagion patterns, various measures of partial and dynamic connectedness are computed, supporting the earlier TVP-VAR analysis. The findings indicate that Litecoin and Ethereum exhibit a high level of connectedness, while Bitcoin remains relatively less connected. Among the metals, Gold and Copper demonstrate similar levels of connectedness in certain cases. Notably, there is a significant risk contagion between Litecoin and metals. These results hold essential implications for policy-makers and portfolio managers with different time horizons, offering valuable insights into risk contagion within the cryptocurrency and metal markets. JEL Codes: C32; G15; G17; G41

Dynamic interrelations between environmental innovations, human capital, and energy security in Vietnam: new evidence from an extended TVP-VAR approach.

This study employs an extended joint connectedness approach, as well as a time-varying parameter vector autoregression technique (ETVP-VAR), to characterize the interrelationships among six factors, namely, renewable energy, environmental innovations (EI), gross domestic product (GDP), human capital, energy consumption, and energy productivity (EP), from 1995 to 2019 in Vietnam. Taking a holistic view, it appears that both energy consumption and GDP are net recipients of contagion shocks. In addition, renewable energy, as well as environmental innovations, is critical net transmitters during our studied period. From 1995 to 2003, human capital constantly acted as a crucial net shock absorber before switching to a crucial net transmitter after that year. With a significant exception around 1997, this variable is mostly a shock transmitter when it comes to energy production. Pairwise connectedness reveals that renewable energy consistently appears as a shock transmitter to GDP, human capital, energy consumption, and energy productivity. That being said, environmental innovations consistently contribute to renewable and green energy volatility by transmitting shocks from other markets.

Dynamic interlinkages between cryptocurrencies, NFTs, and DeFis and optimal portfolio investment strategies

PurposeThis study aims to scrutinize time-varying return and volatility interlinkages among major cryptocurrencies, NFT tokens and DeFi assets between 1 July 2018 and 19 February 2023 and determine optimal portfolio allocations and hedging effectiveness under different portfolio construction techniques.Design/methodology/approachThis work examines time-varying return and volatility interlinkages among major cryptocurrencies, NFT tokens, and DeFi assets between 1 July 2018 and 19 February 2023. To this end, the time-varying parameter-vector autoregression (TVP-VAR)-based connectedness methodology of Antonakakiset al.(2020) This approach is an extended version of the Diebold–Yilmaz (DY) method (Diebold and Yılmaz, 2014) and has advantages over the original DY. First, unlike the DY, it is free of the selection of a particular window size. Second, it has robustness for the outliers. Furthermore, following Broadstocket al.(2022), the author estimates time-varying optimal portfolio weights and hedging effectiveness under different portfolio construction scenarios.FindingsThis study's results indicate the following results: (1) The overall connectedness indices prominently capture well-known financial/geopolitical distress incidents; (2) the leading cryptocurrencies (ETH, BTC and BNB) are the largest transmitter of return shocks, while LINK and BTC are the largest transmitters/recipients of volatility shocks; (3) cryptocurrencies, NFTs and DeFi form distinct cluster groups in terms of return and volatility connectedness; (4) the connectedness networks estimated around the 2022 cryptocurrency crash and the FTX's filing for the bankruptcy are characterized by the strongest return and volatility interlinkages; (5) optimal portfolio strategies computed by different portfolio construction techniques display similar motifs and have sustained growth paths except for some short-lived drop backs.Research limitations/implicationsThis study's findings imply several policy suggestions for investors, stakeholders and policymakers. First, the study's time-based dynamic interlinkages can help market participants in their optimal portfolio decisions. In particular, the persistent net receiving roles of the DeFi assets and the NFTs throughout the episode, especially around the financial/geopolitical turmoil, underpin their safe haven potentials (Umaret al., 2022a, b). Finally, since the total connectedness indices (TCIs) are prone to significantly increase around financial/geopolitical burst times, these tools can be valuable for policy makers to monitor risk.Originality/valueThe contribution of knowledge is at least threefold. First, the author focuses on the dynamic time interlinkages among major cryptocurrencies, NFTs and DeFi assets in July 2018 and February 2023 considering the prominent recent financial/geopolitical incidents. Second, the author estimates network topologies of dynamic connectedness around financial/geopolitical bursts and compared them in terms of interlinkages. Finally, the author calculates the time-varying optimal portfolio allocations and hedging effectiveness under different portfolio construction techniques.

Dynamic spillovers and portfolio implication between green cryptocurrencies and fossil fuels.

Are green investments decoupled from the dirty investment such as the fossil fuel markets? We address this issue by extending the literature on environmental, social, and governance (ESG) assets by examining the dynamic relationship between fossil fuels and digital ESG assets proxied by green cryptocurrencies using the TVP-VAR(Time-varying parameter vector auto regression) spillover framework. Furthermore, we analyze the hedging attributes of green cryptocurrencies and fossil fuels in a minimum connectedness framework. The main findings are as follows: First, green cryptocurrencies are the main shock transmitters in all asset systems. Second, the dynamic connectedness between green cryptocurrencies and fossil fuels increased during the COVID-19 and Russia-Ukraine conflicts. Third, green cryptocurrencies have shown considerable hedging effectiveness against the fossil fuels. Our study has important implications for investors, regulators, and policy makers, such as shifting to green cryptocurrencies, regulation of carbon footprint, and promoting eco-friendly assets.

Diversification of the Islamic stock market, Bitcoin, and Bullions in response to the Russia-Ukraine conflict and the COVID-19 outbreak

This study investigates the interconnectedness of the Islamic stock market, Bullion, and Bitcoin as diversifiers for portfolios, exploring their role as hedges and safe havens. The analysis covers the period from January 2015 to December 2022, with a particular focus on the influence of the COVID-19 pandemic and the Russia-Ukraine War on the MSCI World Islamic Index, bullions (Gold, Silver, Platinum, Nickel, Palladium, and Aluminium), and Bitcoin, employing a time-varying parameter vector autoregression (TVP-VAR) model. During crisis periods, our findings reveal that the transmission and reception of shocks among these assets varied, with a heightened level of co-movement observed during the pandemic and war periods. These results emphasise the importance of considering the dynamic nature of financial assets' connectedness in asset investment decisions, particularly in times of crisis. Furthermore, the findings suggest that Bullion can serve as a hedge for both Bitcoin and the Islamic stock market. The study also explores the optimal diversification of investment portfolios and highlights the importance of adhering to Islamic principles in portfolio diversification. By integrating Islamic rules into the diversification process, investors can enhance the effectiveness and relevance of their investment strategies.

Research on the Impact of Global Economic Policy Uncertainty on Manufacturing: Evidence from China, the United States, and the European Union

Events such as COVID-19 and the Russia–Ukraine conflict have significantly increased the uncertainty and volatility of global economic policies. In the context of economic globalization, the key question we investigate is whether global economic policy uncertainty will have different impacts on the manufacturing of the three major economies in China, the United States, and Europe Union. This study employs the time-varying parameter vector autoregressive (TVP-VAR) model to examine how global economic policy uncertainty (GEPU) affected manufacturing from March 2008 to March 2023. The empirical results show that the effects of GEPU are time varying; its short-term effects on Chinese manufacturing are slightly greater than its medium- and long-term effects, whereas its medium- and long-term effects on manufacturing in the United States (US) and European Union (EU) are significantly greater than its short-term effects. The impact of European debt crisis, the China–US trade war and Russia–Ukraine conflict on EU manufacturing is higher than that of China and the US, and the impact of the COVID-19 pandemic on China’s manufacturing is much smaller than that of the US and the EU; thus, Chinese manufacturing has a greater capacity for risk mitigation than US and EU manufacturing. This study not only provides a new perspective on the study of global economic policy uncertainty; it also provides new empirical evidence on how global economic policy uncertainty affects the manufacturing sector in China, the US and Europe and provides policymakers with guidance for decision making.

Stablecoins as the cornerstone in the linkage between the digital and conventional financial markets

Do stablecoins mean anything to risk management across financial markets? We answer this question by examining the interrelationships between stocks, treasuries, stablecoins, and cryptocurrencies in nonparametric quantile-causality-in-means and quantile-on-quantile regression models. Differently from previous studies of stablecoins’ price volatility, we focus on stablecoins’ trade volumes. The results extend the evidence on stablecoins’ responses to other crypto assets, whose declining returns, through the phenomena designated by us as “flight-to-cryptosafety”, fuel the stablecoins’ market capitalization. Issuance of new stablecoins, because of their backing by conventional safe assets, raises US Treasuries’ demand. And then, decaying Treasury yields cause “flight-to-safety” from stocks to Treasuries. Results from the quantile-based fixed-parameter models are robust to the dynamic connectedness metric based on the time-varying parameter vector auto-regression (TVP-VAR) framework.

Sectoral risk contagion and quantile network connectedness on Chinese stock sectors after the COVID-19 outbreak

PurposeThis study aims to explore the risk spillover effects among different sectors of the Chinese stock market after the outbreak of COVID-19 from both Internet sentiment and price fluctuations.Design/methodology/approachThe authors develop four indicators used for risk contagion analysis, including Internet investors and news sentiments constructed by the FinBERT model, together with realized and jump volatilities yielded by high-frequency data. The authors also apply the time-varying parameter vector autoregressive (TVP-VAR) model-based and the tail-based connectedness framework to investigate the interdependence of tail risk during catastrophic events.FindingsThe empirical analysis provides meaningful results related to the COVID-19 pandemic, stock market conditions and tail behavior. The results show that after the outbreak of COVID-19, the connectivity between risk spillovers in China's stock market has grown, indicating the increased instability of the connected system and enhanced connectivity in the tail. The changes in network structure during COVID-19 pandemic are not only reflected by the increased spillover connectivity but also by the closer relationships between some industries. The authors also found that major public events could significantly impact total connectedness. In addition, spillovers and network structures vary with market conditions and tend to exhibit a highly connected network structure during extreme market status.Originality/valueThe results confirm the connectivity between sentiments and volatilities spillovers in China's stock market, especially in the tails. The conclusion further expands the practical application and theoretical framework of behavioral finance and also lays a theoretical basis for investors to focus on the practical application of volatility prediction and risk management across stock sectors.

Dynamic connectedness between global commodity sectors, news sentiment, and sub-Saharan African equities

We examine the static and time-varying spillovers between global commodity sectors, economic news sentiment, and sub-Saharan African (SSA) equities. In a time-varying parameter vector autoregressions spillover connectedness approach, we find that: (i) there are significant spillovers between SSA equities, economic news sentiment, and global commodity sectors; (ii) the connectedness evolves across different times and is amplified by market crises; (iii) there are suitable market-specific SSA equities that bear safe-haven and hedge attributes for global commodity investors; (iv) agriculture is the main source of return and volatility spillovers; and (v) economic news sentiment index is a net transmitter of spillover during systemic crisis periods such as GFC and the COVID-19 pandemic. Our findings have notable implications for international investors, regulators, and practitioners.

How Russian-Ukrainian geopolitical risks affect Chinese commodity and financial markets?

In this paper, we explore whether and how the Russian-Ukrainian conflict impact Chinese commodity and financial markets. For this purpose, we use the connectedness method based on time-varying parameter vector autoregression (TVP-VAR) model to measure the time and frequency domains dynamic connectedness among ten commodity markets and five financial markets. The empirical findings include: (i) the total connectedness increased due to the conflict, but the impact of Russia-Ukrainian conflict is weaker than that brought about by COVID-19 epidemic; (ii) the Russia-Ukrainian conflict affects the stability of Chinese commodity and financial systems mainly by affecting Chinese commodity markets, especially by increasing risks in energy, chemical and grain markets; (iii) the conflict increased short-term connectedness more and has weaker impact on long-term connectedness, and the increase in short-term connectedness is mainly attributable to the increased risk of crude oil and chemical commodities, while the increase in long-term connectedness is not only attributable to the increased risk of crude oil and chemical commodities, but also related to the increased risk of grain commodities.

The dynamic linkages among crude oil price, climate change and carbon price in China

Crude oil prices, weather changes, and carbon prices are closely related, but little research investigates their dynamic relationship. Studying how crude oil prices and weather changes dynamically affect carbon prices is significant to investors and producers. Taking carbon prices in Hubei and Guangdong as examples, we use the time-varying parameter vector autoregressive (TVP-VAR) model to investigate the time-varying influence of WTI and weather changes (average temperature, sunshine duration, and precipitation) on China's carbon prices. Our empirical results show that: firstly, the impact of crude oil prices and weather changes on carbon prices is obviously time-varying and lagging. Secondly, the short-term impact of crude oil prices and weather changes on carbon prices is higher than the long-term impact. And crude oil price mainly positively affects carbon prices in the short term. Finally, due to the differences in industrial development and weather conditions between Hubei and Guangdong, there are certain regional differences in the impact of crude oil prices and weather changes on carbon prices. Based on these research results, some suggestions are provided for global sustainable development and green transformation of enterprises.

Time-varying spillover networks of green bond and related financial markets

In this paper, we investigate the interrelationship between the green bond market and other major financial markets by using the Granger causality test and spillovers network analysis based on a time-varying parameter vector autoregressive (TVP-VAR) model. Our empirical findings include: (i) there exists a significant bidirectional spillover effect between the green bond market and the U.S. Treasury market; (ii) the connectedness between green bonds and other markets has increased significantly, especially the risk spillovers from the stock markets, during periods of economic turmoil; (iii) the carbon market and the energy futures market had spillovers on the green bond market before the publication of the Green Bond Principles in 2014. We also explore the influence of COVID-19 on the spillovers network. There are strong implications of these findings for investors to manage portfolios and for policymakers to improve reregulation.