Time-varying Parameter Vector Autoregressive Model Research Articles

The time-varying dynamic impact of US renewable energy prices on agricultural prices in China: the case of fuel ethanol.

This paper intends to look into the time-varying dynamic impact of US fuel ethanol, one of the renewable energy sources, on the prices of agricultural products (specifically corn, soybeans, rice, and wheat) in China based on monthly price data from January 2000 to January 2023. To achieve this, a time-varying parameter vector autoregressive (TVP-VAR) model is employed, which takes into account structural changes in emergencies through time-varying parameters. The empirical results show that the equal-interval impulse responses of price fluctuations in agricultural commodities are primarily positive to variations in fuel ethanol prices and production. And the intensity and direction of the effects vary at distinct time lags. Additionally, the magnitude of these responses is most pronounced in the short term for all agricultural commodities except for corn, and the duration of the impulse responses at different time points is generally longer for corn prices compared to other commodities. The study also reveals that the influence of US fuel ethanol on Chinese agricultural commodity prices is not substantial on the whole. Therefore, there is a necessity to advance the growth of biofuels and provide policy support and financial subsidies for agricultural products earmarked for food production. These actions could shed insights into the progression of Chinese renewable energy and food policies, ensuring the stability of the market in the long run.

Dynamic connectedness among market volatilities: a perspective of COVID-19 and Russia-Ukraine conflict

Purpose This paper aims to investigate the dynamic volatility connectedness among 13 G20 countries by using the volatility indices. Design/methodology/approach The connectedness approach based on the time-varying parameter vector autoregression model has been used to investigate the linkage. The period of study is from 1 January 2014 to 20 April 2023. Findings This analysis revealed that volatility connectedness among the countries during COVID-19 and Russia–Ukraine conflict had increased significantly. Furthermore, analysis has indicated that investors had not anticipated the World Health Organization announcement of COVID-19 as a global pandemic. Contrarily, investors had anticipated the Russian invasion of Ukraine, evident in a significant rise in volatility before and after the invasion. In addition, the transmission of volatility is from developed to developing countries. Developed countries are NET volatility transmitters, whereas developing countries are NET volatility receivers. Finally, the ordinary least square regression result suggests that the volatility connectedness index is informative of stock market dynamics. Originality/value The connectedness approach has been widely used to estimate the dynamic connectedness among market indices, cryptocurrencies, sectoral indices, enegy commodities and metals. To the best of the authors’ knowledge, none of the previous studies have directly used the volatility indices to measure the volatility connectedness. Hence, this study is the first of its kind that has used volatility indices to measure the volatility connectedness among the countries.

Carbon Market Efficiency and Economic Policy Uncertainty: Evidence from a TVP-VAR Model

This paper examines the dynamic linkages among economic policy uncertainty (EPU), the green bond market, the carbon market, and the macroeconomy using the time-varying parameter vector autoregressive (TVP-VAR) model with monthly data spanning from January 2016 to December 2021. Additionally, it assesses the robustness and accuracy of the empirical results through the Bayesian vector autoregressive (BVAR) model. The findings indicate that EPU negatively affects the green bond market in the short term but has a positive impact in the medium and long term. Conversely, EPU has a positive impact on the carbon market in the short term but a negative impact in the medium and long term. Furthermore, the green bond market negatively influences the carbon market in both the short and medium to long term. These results suggest that emerging markets, such as the green bond and carbon markets, are influenced by EPU. The adverse impact of the green bond market on the carbon market, however, contributes to expediting China’s attainment of its low-carbon objectives. Appropriate economic policies can play a vital role in accelerating the transition to a low-carbon economy. The study also reveals that the US-China trade war has expedited the development of green capital markets in China, despite its impact on the green economic transition in the country. These findings provide insights for the government and investors to formulate suitable strategies for risk mitigation.

Volatility Connectedness of MOVE Index and Bond Returns

Research Originality: To the best of our knowledge, this is the first study which measures the impact of the MOVE index on bond yield volatility by comparing its effect on both short-term and long-term. The paper contributes to the existing literature by providing a better understanding of the relationship between maturity and volatility spillover. Research Objectives: This study examines the dynamic volatility connectedness between 2 and 10-year bond yields and the MOVE Index. Furthermore, it is aimed at detecting the transmitter of volatility among variables. Research Methods: Using a data set from 2010 to 2022, the study utilizes a Time-Varying Parameter Vector Autoregression (TVP-VAR) model to analyse the dynamics and relationship between bond yields and the MOVE Index. Empirical Results: The study finds a significant volatility spillover between the MOVE Index and Türkiye's bond yields. Notably, the linkage between the MOVE Index and the 10-year bond rates is stronger than with the 2-year rates. Additionally, the MOVE Index emerges as the primary transmitter of volatility, impacting both bond yields. Implications: This study sheds light on the complexity and dynamics of volatility spread in Türkiye's bond market, providing essential insights for forecasting bond yields and shaping financial policies.JEL Classification: G17, C22, G12How to Cite:Baykut, E., Gökgöz, H. (2024). Volatility Connectedness of MOVE Index and Bond Returns. Etikonomi, 23(1), 27 – 46. https://doi.org/10.15408/etk.v23i1.36131.

Consumer and business confidence connectedness in the euro area: a tale of two crises

Purpose The purpose of this study is to examine the propagation of consumer and business confidence in the euro area with a particular focus on the global financial crisis (GFC), the European sovereign debt crisis (ESDC) and the COVID-19-induced Great Lockdown. Design/methodology/approach The authors apply Diebold and Yilmaz’s connectedness framework and the improved method based on the time-varying parameter vector autoregressive model. Findings The authors find that although the evolution of business confidence marked the GFC and the ESDC the role of consumer confidence (mainly in those countries with stricter containment and closure measures) increased in the COVID-19-induced crisis. Originality/value The findings are related to the different origins of the examined crisis periods, and the analysis of their interrelationship is a very relevant topic for future research.

Green Shocks: The Spillover Effects of Green Equity Indices on Global Market Dynamics

This study investigates the impact of green equity indices on global market dynamics using a time-varying parameter vector autoregression (TVP-VAR) model. We uncover a significant shift in the role of the global market, transitioning from a shock transmitter to a shock receiver, as the influence of green finance grows. By directly comparing green equity indices with their corresponding global parent indices, we adopt a global perspective that transcends the limitations of studies focusing on specific regions, such as the USA, China, or Europe. This novel approach minimizes the potential biases in the transmission channels within regional markets, enabling a more comprehensive understanding of the relationship between green finance and global market dynamics. Moreover, by focusing on equity indices we ensure a consistent comparison of financial instruments, avoiding the complexities that arise when comparing different asset classes such as green bonds and conventional equities. For global investors, our results highlight the importance of dynamic and flexible hedging strategies that adapt to the distinct characteristics of green assets and their growing influence on the global market. Risk managers should incorporate these time-varying spillover effects into their models to better assess and mitigate potential risks. Policymakers should consider the growing influence of green finance on the broader market when formulating regulations and incentives to support sustainable investing, as our findings underscore the increasing importance of this sector in shaping market dynamics.

Natural resource development, economic policy uncertainty and urban residents’ consumption in China: a nonlinear ARDL and time-varying parameter vector autoregressive with stochastic volatility approach

AbstractIncreased risks of economic policy uncertainty and overexploitation of natural resources exist in China. At the same time, the growth rate of urban residents’ consumption has generally declined. The paper analyses the role of economic policy uncertainty (EPU) and natural resource exploitation on the urban residents’ consumption in China. Based on the data from the first quarter of 2002 to the third quarter of 2021, the paper uses a nonlinear autoregressive distributed lag model to verify the asymmetric effects. Then the paper constructs a time-varying parameter vector autoregressive model with stochastic volatility term to analyze the nonlinear responses. Impulse response analysis was used to further explain the relationship between the three. The negative impact of rising EPU on urban residents’ consumption is larger than its reduction. Negative shocks to natural resource development increase the urban residents’ consumption. Positive shocks reduce the urban residents’ consumption. There is a time-varying non-linear effect of EPU and natural resource development on urban residents’ consumption in China. The negative impact of EPU on urban consumption has been further exacerbated by major crises such as the financial crisis, COVID-19 and the post-crisis period. The negative impact of natural resource development diminished after the government introduced industrial upgrading policies and environmental regulations. This study provides constructive suggestions for the optimization of economic policies and the improvement of urban consumption. This study also enriches consumer theory and provides new evidence for the resource curse hypothesis.

Does oil spin the commodity wheel? Quantile connectedness with a common factor error structure across energy and agricultural markets

Should investors and policy makers in agricultural markets consider oil market's incontestable impact on portfolio risk management? This paper investigates the time-varying market linkages between energy and agricultural commodities in the presence of two important exogenous shocks, viz., the COVID-19 pandemic and the subsequent 2022 Russia–Ukraine military conflict. We use a novel time-varying parameter vector autoregressive model with a common factor error structure to estimate the tail connectedness between energy and agricultural commodities for the period December 31, 2019 to December 18, 2023. Our findings provide clear evidence of asymmetry in the volatility evolution. We determine that volatility spillover magnitudes are much stronger across quantiles than at the mean. We note that crude oil is the main transmitter of shocks in the system before the onset of the 2022 Russia-Ukraine conflict at the lower tail of the distribution. While crude oil and natural gas transmit volatility in both pre- and post-conflict announcement periods. Furthermore, the 2022 Russia–Ukraine conflict is found to impact the transmission of volatility between energy and agricultural commodities. Numerous agricultural commodities are observed to shift their position from transmitters to receivers of volatility, and vice versa, due to the military conflict in Ukraine. Our causality results depict time-varying patterns in the connectedness between crude oil and other commodities. We determine that crude oil has varying impact on agricultural markets in pre- and post-conflict announcement periods. Commodities for which both conflicting countries are major world exports of, such as wheat, have notably increased their dependency on crude oil. Thus, we advise investors and policymakers in agricultural markets to seriously consider oil market's impact on portfolio risk management and monitoring policies.

Sentiment matters: the effect of news-media on spillovers among cryptocurrency returns

This paper explores the relationship between news media sentiment and spillover effects in the cryptocurrency market. By employing a time-varying parameter vector autoregressive model, we initially develop measures of spillover specific to individual cryptocurrencies. Subsequently, we employ unique data on cryptocurrency-specific sentiment to assess its impact on these spillover measures using panel fixed effects regression analysis. Our findings indicate that news media sentiment plays a significant role in explaining the spillover dynamics within the cryptocurrency market. Unlike traditional assets, it appears that only positive sentiment affects the spillovers among cryptocurrencies, suggesting an asymmetric effect. Taking into account various characteristics of cryptocurrencies, we find that sentiment's impact on spillover is more pronounced in community-based coins than in those driven by firms. An examination of news content suggests that sentiment pertaining to emotional and risk aspects of cryptocurrencies predominantly influences these spillovers. Additionally, a comparative analysis of sentiment derived from social media and traditional news sources reveals a stronger influence of the former on spillover effects. Through extensive robustness checks, our research consistently affirms the pivotal role of sentiment in driving spillovers among cryptocurrency returns, underlining the importance of sentiment analysis in understanding the dynamics of the cryptocurrency market.

Exploring interconnectedness between climate change, renewable energy, technological innovation, and G-17 banking stock markets

This study schemes to explore novel insights from crisis risk spillovers and volatility connectedness among climate change, renewable energy, technological innovation, and G17-banking stocks. We adopt a diversified approach combining the time-varying parameter vector autoregressive (TVP-VAR) model for dynamic analysis with the Diebold-Yilmaz framework for static analysis and found that all markets have significant risk spillover and connectedness during the crisis. The ECOI index, OCEAN, and NEX are risk spillover transmitters and designed significant connectedness networks with these markets in crises. Further, France, Canada, and the US banking sectors are the main transmitters in the network during COVID-19. The US banking sector is also the primary risk transmitter during the Russia-Ukraine war. However, Japan and Korea banking stock markets consistently receive risk spillovers in the COVID-19 and Russia-Ukraine wars. This study provides significant implications for all stakeholders across the globe in these financial markets and for sustainable climate and environment.

Geopolitical risk and the predictability of spillovers between exchange, commodity and stock markets

We utilize the cross-quantilogram method to assess the predictive capacity of geopolitical risk (GPR) on volatility spillovers calculated by the time-varying parameter vector autoregressive model, across international commodity, exchange, and U.S. and Chinese stock markets. The findings yield three notable observations: First, we establish the directional predictive influence of GPR on net and net pairwise volatility spillovers, indicating discernible shifts in the risk roles of specific markets and transmission pathways. Second, these shifts, anticipated by GPR, manifest swiftly within a single day and subside within a quarter, albeit with varying durations contingent on market categories and transmission pathways. Third, disparities are evident in the predictive effectiveness of geopolitical acts and geopolitical threats. These findings remain robust even when considering factors such as economic policy uncertainty, alternative proxies, and other spillover models.

How do climate risks impact the contagion in China's energy market?

Energy security is a critical facet of national security, especially in energy-importing countries, and significant fluctuations in the energy market can profoundly influence the real economy. With the intensification of global climate change, extreme weather events and diverse energy transition policies emerge as prominent risk factors driving energy market volatility. In this study, we employed a time-varying parameter vector autoregression (TVP-VAR) model and the spillover index method of generalized variance decomposition to assess the endogenous contagion and exogenous shock risk of China's energy prices and the systematic risk in the broader energy market. Our analysis included the coal, oil, and natural gas prices in both Chinese and international markets. The results indicate that the international oil market plays a dominant role in the energy risk spillover network, with the exogenous shock risk in China's energy market surpassing the endogenous contagion risk. Further, quantile models were used to investigate the impact of climate risks, namely climate disasters, climate policy uncertainty, and climate risk concern. We found that in high-risk environments, all climatic factors exert pronounced impacts on China's energy market risks, with climate risk concern being especially influential. Overall, this study has important policy implications for energy security and adaptation to climate change.

Market volatility and crisis dynamics: a comprehensive analysis of U.S., China, India, and Pakistan stock markets with oil and gold interconnections during COVID-19 and Russia–Ukraine war periods

The objective of this paper is to explore the interconnectedness of volatility among the stock markets of U.S., China, India, and Pakistan in conjunction with oil and gold markets. Employing the novel Time-Varying Parameter Vector Autoregression (TVP-VAR) model for assessing connectedness, the study scrutinizes key patterns of dependency and interrelation between these markets. Furthermore, this study investigates the dynamic connectedness during the global health crisis due to COVID-19 and the geopolitical crisis due to Russia–Ukraine war periods to identify the changes in their relationship following the two crises episodes. The findings underscore the significance of volatility transmissions emanating from the U.S., a developed market, in shaping these dynamic linkages. It is observed that oil and gold returns play a limited role as sources of shocks for market returns in China, India, and Pakistan, suggesting a relatively lower contribution of oil and gold to equity market volatility. The results also emphasize the safe-haven characteristics of gold during periods of crisis such as the COVID-19 pandemic and the Russia–Ukraine war. Moreover, the study indicates that the volatility transmissions during the COVID-19 pandemic are more pronounced compared to the Russia–Ukraine war crisis. These findings hold notable implications for both investors and policymakers, emphasizing the need for a nuanced understanding of market dynamics and the development of risk-averse strategies, particularly in times of crisis.

Spillover effects between fossil energy and green markets: Evidence from informational inefficiency

This paper builds an indicator of the market inefficiency degree by quantifying the extent of the market deviation from an efficient status. From a novel perspective of informational inefficiency, we further model the dynamic spillover effects between fossil energy and green markets by using a time-varying parameter vector autoregressive (TVP-VAR) model. Our findings indicate that the market inefficiency degree and its spillovers exhibit evident time-varying dynamics and are sensitive to extreme events. The international crude oil benchmarks serve as the most significant information transmitter and receiver in the spillover network of market inefficiency. Within green-related markets, carbon market demonstrates the strongest correlation with the fossil energy markets. Additional analyses involving consideration of economic policy uncertainty reassure the robustness of our findings.

Carbon volatility connectedness and the role of external uncertainties: Evidence from China

This paper investigates the volatility connectedness between China’s carbon pilot markets. Using Diebold and Yilmaz (2014)’s approach based on the time-varying parameter vector autoregression model with a variety of parameter sets, we obtain the average across 40 results to capture the volatility connectedness between the markets. We further use the linear and nonlinear autoregressive distributed lag models to assess the role of external uncertainties in shaping volatility connectedness. Several findings emerge: (1) Guangdong (Chongqing) is the largest net transmitter (receiver) in terms of volatility connectedness; (2) Volatility connectedness shows a declining trend, with its cycle fluctuations caused by compliance-driven trading; (3) Volatility connectedness correlates negatively with external uncertainties. Both economic policy and climate policy indices have impacts on volatility connectedness. We recommend introducing market makers to enhance market liquidity and reduce risk spreading. We also highlight the need for further research to pinpoint idiosyncratic factors that affect different markets.

The impact of macroprudential cross-border capital flow management on the linkage of domestic and foreign bond markets

While cross-border financing activities have increased significantly in recent years, the inflow of capital also leads to accumulated financial risks. To mitigate financial risks and promote the opening of financial markets, macroprudential management policies for cross-border financing have been implemented since 2016. This paper examines the effectiveness of macroprudential management policies in opening financial markets and managing foreign financial risks. We employ a time-varying parameter vector autoregressive model (TVP-VAR) to quantitatively analyze changes in the spillover effects between China’s bond market and foreign bond markets under different implementation stages of cross-border financing macroprudential policy. Our analysis reveals that the implementation of macroprudential management of cross-border financing has increased the total spillover effect between different bond markets, as well as the spillover effect from other bond indices to the Chinese RMB Bond Index and the spillover effect from other indices to the Chinese USD index. Moreover, our findings indicate that macroprudential management has reduced the total volatility spillover effect and the volatility spillover effect from other indices to the Chinese RMB Bond Index. These results highlight the importance of preventing external risk transmission when China’s financial market is opening to the world.

Relationship between mateverse cryptocurruncies (meta coins) and sustainability indices

Purpose- The purpose of this study is to determine the relationship between metaverse and sustainability indices. MANA token was used as a metaverse indicator in the study. Dow Jones Sustainability Emerging Markets Index (DJS_EMI), Dow Jones Sustainability World Index (DJS_WI), Dow Jones Sustainability Europe Index (DJS_EI) were used as sustainability indices. Methodology- In this research, the dynamic relationships between Metaverse and 3 sustainable indices used in the study were analyzed with a time-varying parameter vector autoregressive (TVP-VAR) model. This method is based on the fixed parameter sliding window VAR approach, first developed by Antonakakis and Gabauer (2017) and later updated by Diebold and Yılmaz (2009, 2012, 2014). Findings- DSJ_EMI variable carries a volatility spread of 24.85% to “DSJ_WI”, 24.81% to “DSJ_EI” and 25.40% to “METAVERSE”. “DSJ_WI” has a 59.68% volatility spread to other variables. The variables “DSJ_EMI” 25.04%, “DSJ_EI” 25.10% and “METAVERSE” 24.69% carry out volatility spread. Finally, while 70.79% of the volatility change in the "METAVERSE" variable is due to itself, a total of 29.21% is due to other variables. The variables “DSJ_EMI” 9.74%, “DSJ_WI” 9.74% and “DSJ_EI” 9.73% perform volatility spread. Conclusion- Mana (Decentraland Token) is the cryptocurrency used in the virtual reality platform called Decentraland. Metaverse refers to a digital universe called virtual reality, augmented reality and virtual world. Decentraland is a decentralized virtual reality platform that allows users to create, trade and interact with their own digital content.As a result of the analysis, it was determined that MANA, a Metavarse crypto asset, spreads volatility to sustainability indices. In other words, the MANA token spreads volatility to sustainability indices. Keywords: Metaverse, metaverse cryptocurrincies, meta coins, sustainability ındex, TVP-VAR JEL Codes: C32, C55, C58

Gold Price, Oil Price, and Stock Market Return Spillovers: Empirical Evidence from Vietnam

This paper analyzes the spillovers of oil prices, gold prices and stock market returns in Vietnam. By adopting the time-varying parameter vector autoregression model (TVP-VAR), the results show a moderate interdependence among the variables from 2010-2022. Additionally, the relationship between oil prices, gold prices, and stock market returns changes over time and is influenced by economic and political events. Overall, stock market returns are net shock transmitters with the highest volatility among all the variables, while the oil and gold markets are net recipients. Finally, our results remain robust to Vietnam's alternative stock market index.

Changes in monetary policy implementation over time

In this study, we utilise a time-varying parameter vector autoregressive model to investigate the relationship between central bank liabilities and the overnight policy rate across three countries with distinct monetary policy frameworks. Our findings reveal that a consistent negative correlation between these variables is present only in the case of the conventional reserve regime. For the other regimes, an initial significant negative relationship between reserves and interest rates is observed, but this effect diminishes with the adoption of new frameworks. These results suggest that the emergence of novel operational structures for central banks has disrupted the traditional approach to monetary policy implementation. One practical consequence of this” decoupling” between interest rates and reserves is that central banks in the United States, Canada, and similar systems can effectively employ their balance sheets in conjunction with standard interest rate policies. However, this does not apply to reserve regimes that continue to depend on open market operations for steering interest rates

Dynamic spillovers in higher moments and jumps across ETFs and economic and financial uncertainty factors in the context of successive shocks

Hedging is a particularly important tool in the Exchange Traded Fund (ETF) markets where market makers seek the best ways to mitigate the uncertainty of their exposures. This study relies on high frequency data to assess the spillover effects among ten US sector ETFs and various economic and financial uncertainty indexes based on realized volatility, realized higher moments as well as jumps under a relatively new spillover framework. Next, a time-varying parameter vector autoregression (TVP-VAR) model is used to examine the dynamic connectedness among ETFs and uncertainty factors volatilities while avoiding the sensitivity of spillover results to the choice of the rolling window. Our results showcase higher total connectedness between the different uncertainty indexes and ETFs, though with varying sensitivities. Notably, skewness and kurtosis can spread from one market to another, especially during times of market turbulence, reflecting the significant spillovers in higher-order moments. Interestingly, the market’s 30-day forward looking expectations of US stock market volatility (VIX) has stronger effect on the US sector equity ETFs than the expected 30-day volatility of returns on oil and gold. This analysis emphasizes the implications and contributions of assessing the spillover in higher-order moments covering volatility, skewness, and kurtosis to portfolio hedging and financial risk management. Overall, the results are of considerable practical interest for economic and market agents who are keen to understand market integration and systemic risk propagation to infer asymmetric or fat tail risk related to extreme or downside/upside risks.