Time-varying Parameter Vector Autoregression Research Articles

Impact of Geopolitical Risk on G7 Financial Markets: A Comparative Wavelet Analysis between 2014 and 2022

This study investigates co-movements between the GPR generated by the Crimean Peninsula’s annexation in 2014, the Russia–Ukraine war in 2022, and the volatility of stock markets in the G7 states. Using wavelet analysis, concentrated co-movement was found for all indices in both periods. Contrary to the general perception, we find that the G7 financial market response in 2014 was robust. Using a time-varying parameter vector autoregression (TVP-VAR) test, we found a larger reaction in the amplitude of the G7 financial markets in 2022 than in 2014. The financial markets in France, Germany, and the UK showed a similar reaction in 2022. We have identified some common aspects, even if the political and military contexts of the two studied events were completely different. Our findings offer new and interesting implications for understanding how geopolitical risk affects financial assets for market participants with multiple investment horizons and strategies.

Unveiling hidden connections: Spillover among BRICS' cryptocurrency-implied exchange rate discounts and US financial markets

This study employs connectedness approach based on Time-Varying Parameter Vector Autoregression (TVP-VAR) and Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) methods, to scrutinize the connectedness between the cryptocurrency implied exchange rate discount in BRICS countries (Brazil, Russia, India, China, and South Africa) and US financial markets. Empirical evidence suggests that Bitcoin market volatility substantially influences other markets. Moreover, given the intimate connections between BRICS countries and developed nations such as the US in today's globalized context, fluctuations in the Bitcoin market within BRICS countries are apt to impact US financial markets. Our empirical findings specifically indicate the following: (i) Among the total spillover effects of the cryptocurrency implied exchange rate discount in BRICS countries, Russia exhibits the most potent total spillover effect and contributes the most to market connectedness. (ii) The Brazil directional spillover index is elevated in both spillover relationship categories, and the spillover effect of the cryptocurrency implied exchange rate discount in Brazil on US financial variables is notably pronounced. (iii) Of the US financial variables, the US Financial Stress Index demonstrates the most robust connectedness with the cryptocurrency implied exchange rate discount in BRICS countries and exhibits heightened sensitivity to uncertainty.

Tail risk connectedness in G7 stock markets: Understanding the impact of COVID-19 and related variants

This study uses a time-varying parameter vector autoregression (TVP-VAR) approach to examine the transmission of tail risk among G7 stock markets from January 2000 to September 2022, focusing on major financial episodes like the dot-com collapse, the 2008 Global Financial Crisis, and the European debt crisis, as well as the COVID-19 pandemic and its variants. Our analysis shows fluctuating tail risk connectedness across G7 markets during the pandemic, influenced by lockdowns, supply chain issues, interventions, and investor sentiment. Notably, the UK and Italy were major tail risk transmitters, whereas Japan predominantly absorbed risk, highlighting its distinct vulnerability. The findings stress the need for a comprehensive understanding of tail risk dynamics to inform decisions by investors, governments, and regulators, ensuring financial system resilience during heightened economic stress.

Time-varying relationship between international monetary policy and energy markets

This paper examines the spillover dynamics and connectedness between international monetary policies of four developed economies (i.e., UK, US, Japan and Eurozone) and energy markets (i.e., crude oil, heating oil, gasoline and propane) while accounting for the impact of global volatility, economic uncertainty and geopolitical risk factors. This paper uses the time-varying parameter vector auto-regression (TVP-VAR) and TVP-VAR-based extended joint connectedness models to explore the connectedness of monetary policy and energy markets using daily data from June 1, 2007, to March 31, 2022. The results reveal a time-varying connectedness between the energy markets and international monetary policy, and global events affect the magnitude of connectedness. In all, energy markets were found to be the major shock transmitters and monetary policy the receiver. Additionally, we found that energy dependence explains why the Euro Area shadow short rate is more related to energy markets than other monetary policies. The findings also show “Oil to monetary policy” risk spillover, which suggests oil price controls monetary policy. Finally, economic uncertainty positively affects monetary policy and energy price connectedness.

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.

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.

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.

Measuring the degree of connection between currency futures: Empirical dive into higher moments

PurposeThe purpose of this study is to unfold the existing information channel in the higher moments of currency futures for different time horizons.Design/methodology/approachThe authors use a quasi-Bayesian local likelihood approach within a time-varying parameter vector autoregression (TVP-VAR) framework and a dynamic connectedness measure to study the volatility, skewness and kurtosis of most traded currency futures.FindingsThe authors’ results suggest a time-varying presence of dynamic connectedness within higher moments of currency futures. Most spillovers pertain to shorter time horizons. The authors find that in net terms, CHF, EUR and JPY are the most important contributors to the system, while the authors emphasize that the role of being a transmitter or a receiver varies for pairwise interactions and time windows.Originality/valueTo the best of the authors’ knowledge, this is the first study that looks upon the connectivity vis-á-vis uncertainty, asymmetry and fat tails in currency futures within a dynamic Bayesian paradigm. The authors extend the current literature by proposing new insights into asset distributions.

Integration of the international carbon market: A time-varying analysis

Emission Trading Schemes (ETSs) have become vital for meeting global emission reduction targets. They are gaining momentum, as witnessed by increasing market size and improving information mechanisms. Examining key emission markets — European Union, New Zealand, California, and Hubei (China) — from April 2014 to December 2021, a Time-Varying Parameter Vector Autoregressive (TVP-VAR) model is applied to discern the markets' connectedness. In a novel approach to global carbon market research, this study uniquely combines the TVP-VAR with the connectedness approach, overcoming fixed parameters estimation and ensuring precise parameter estimates. The approach sheds light on patterns of total, directional, and net return/volatility spillovers, striving to identify which markets act as transmitters and which are receivers. Linking market spillovers to market characteristics, events, and policies offers insights for investors and policymakers. The total connectedness index of 10–12 % suggests a relatively low level of spillover, when compared to other market integration studies. The dynamic nature of return and volatility spillovers is evident, especially during the energy crisis and Covid-19 outbreak. The EU's ETS consistently acts as a net transmitter, predominantly in return connectedness, while New Zealand's ETS emerges as a major shock receiver in both return and volatility systems. Global climate negotiations and carbon market events have only a minor impact on the level of connectedness, in contrast to energy or financial crises and the Covid-19 outbreak. By highlighting the intricacies of carbon price volatility and market transmissions, the findings equip stakeholders with invaluable, actionable insights.

Nexus between inflation and inflation expectations at the zero lower bound: A tiger by the tail

Ultra-low policy rates and zero lower bounds (ZLB) have been major issues for monetary policy formulation. In this context, we investigate the nexus between inflation and inflation expectations. Employing a non-linear ADRL framework on UK and Canadian datasets, we find significant asymmetries in the nexus among inflation and inflation expectations and their determinants, including labour market slack, output gap, oil shocks, fiscal deficit and exchange rates. Heterogeneities are explicit in the short and long-term as well as in the pre-and post-ZLB regimes. Inflation and inflation expectations become more responsive to their determinants as key policy rates approach the ZLB. There is strong evidence of persistence in inflation and inflation expectations in both regimes manifesting the importance of keeping expectations well anchored. Robustness testing with Time-Varying Parameter Vector Auto-Regression and Network Analysis confirms our findings. To mitigate the persistence of a high inflation regime, it would be vital to keep the inflation expectations well anchored.

TVP-VAR Frequency Connectedness Between the Foreign Exchange Rates of Non-Euro Area Member Countries

Abstract Research background The main purpose of monetary integration between EU countries is to eliminate excessive fluctuations in exchange rates. High volatility in exchange rates can cause various negative economic and financial effects, especially during periods of economic shocks. In addition, estimating the volatility between currencies and their interactions is of great importance for effective portfolio management. Purpose The objective of this research is to scrutinize the transmission of volatility between the currencies of those European Union nations that do not participate in the EURO area, focusing on the exchange rate parity of the US Dollar with seven non-EURO zone currencies. Research methodology Daily volatility in exchange rates was calculated using the Garman-Klass-Yang-Zhang (GK-YZ) method. To investigate the connectedness between these volatilities, we used the Time-Varying Parameter Vector Autoregression (TVP-VAR) frequency connectedness approach. Results The Average Total Connectedness Index exhibits a significant degree of connectedness of approximately 71.84%. The Net Total Directional Connectedness Index indicates that the CZK, DKK and RON exchange rates are net beneficiaries in aggregate and in a longer term perspective, whereas the DKK, HUF and PLN exchange rates are net beneficiaries in a shorter term horizon. In the context of major global events such as the onset of the COVID-19 outbreak in March 2020 and the start of the Russia-Ukraine conflict in February 2022, it could be observed that the dynamic Total Connectedness Index exhibited a substantial increase, both overall and from a long-term perspective, corroborating theoretical expectations. According to the Net Pairwise Directional Connectedness index, the highest bilateral connectedness overall and in the short run was between DKK and RON, while in the long run between BGN and DKK. Novelty Examining the connectedness of currencies is of great importance for investors doing business with foreign currency, international cooperation and policies, risk management and portfolio management. Determining the connectedness in different frequency (short and long-term) ranges provides important information for hedging risk. In addition, the bilateral connectedness between currencies is a guide for effective portfolio diversification.

Cryptocurrency Environmental Attention, Green Financial Assets, and Information Transmission: Evidence From the COVID-19 Pandemic

This paper explores the connectedness between the cryptocurrency environmental attention index and four major green financial assets using time-varying parameter vector autoregression model from January 2014 to December 2021. Findings reveal that connectedness follows a heterogeneous trajectory over time. Results show evidence of higher volatility transmission during the COVID-19 period relative to the entire sample period. The high volatility transmission is a source of concern to policymakers, green stakeholders, and investors, due to limited diversification options.

Connectedness of Vietnamese bank stock returns under the impact of the COVID-19 pandemic

The COVID-19 pandemic highlighted the sensitivity of connectedness among bank stock returns in Vietnam. The aim of this study is to examine the strength of this connectedness along with the effect of government lockdown policy and COVID-19 cases on the total connectedness index (TCI) of 16 listed banks on Vietnamese stock exchanges. They are assessed using the database of FiinPro on the banking sector between January 2020 and July 2022, Vietnam Center for Disease Control and Prevention (CDC), and The World Health Organization (WHO) on the COVID-19 pandemic, employing a time-varying-parameter vector autoregressive (TVP-VAR) connectedness framework and the conditional quantile regression model. The results show that at the firm level, there is strong interdependence among bank stock returns with the average TCI being as high as 90.66%. It is also revealed that medium and large-sized banks are receivers of shock, while smaller banks are transmitters. As far as the impact on TCI is concerned, the widespread of the pandemic with the increasing number of COVID-19 cases is significantly negative, whereas the tightening of lockdown is significantly positive. Besides, the degree of the impact varies according to the 95th, 75th, 50th and 25th levels of conditional quantile regression. Based on the study’s findings, individual investors are recommended to thoroughly analyze the connectedness of banks before making investment decisions, while bank regulators should strengthen controls on credit relationships with small banks. Regarding policy makers, it is proposed to apply flexible restrictions and short-term lockdown depending on the actual outbreak of the pandemic. AcknowledgmentThe paper was conducted within the scope of Project QG21.48 of Vietnam National University.

Carbon conundrums: Geopolitical clashes and market mayhem in the race for sustainability

This research presents an in-depth investigation into the dynamic correlation between geopolitical conflicts and carbon markets utilizing the Time-Varying Parameter Vector Autoregression (TVP-VAR) technique. The analysis focuses on the interconnectedness between the Geopolitical Risk Index Daily (GPRD) and vital carbon pricing instruments, specifically the Intercontinental Exchange Endex European Union Allowance (ECEFDC), KraneShares California Carbon Allowance Strat ETF (KCCAK), Shanghai Environment and Energy Exchange China Emission Allowances Online Transactions (SAXCEA), and S&P Global Ex-Japan LargeMidCap Carbon Efficient Index (SPGJ). The daily fluctuations were traced from May 2021 to July 2023. The analysis is divided into short- and long-term connectedness, with particular emphasis on the impact of the Russia-Ukraine conflict on the GPRD's spillover on carbon markets. The short-term connectedness (1–5 days) between GPRD and ECEFDC shows variability, fluctuating between 10% and 40%. Conversely, long-term connectedness exhibited a significant increase during the conflict, peaking at approximately 34% by mid-2022. The analysis of the Total Dynamic Connectedness (TCI) between the GPRD and the KCCAK indicates comparable magnitudes, although with minor initial discrepancies. The short-term connectedness of GPRD and KCCAK decreases from its peak of approximately 10% to approximately 1%. Conversely, long-term connectedness varies between approximately 32% and 2% from May 2022 onwards. The long-term connectedness between GPRD and SAXCEA revealed variable patterns, peaking at around 18% at the beginning of the sample period and rapidly reducing to around 1% within two months. The analysis of the connectedness between GPRD and the SPG) identifies intense fluctuations in both TCI and long-term connectedness. After an initial increase and decrease, these patterns rebound and experience another increase. This research provides significant insights into the complex dynamics of geopolitical conflicts and carbon markets, particularly the impact of the Russia-Ukraine conflict on carbon market behavior.

Dynamic impact of the COVID-19 lockdown intervention policies on network structure of energy futures return connectedness

This work investigates the dynamic impact of COVID-19 lockdown policies, implemented to curb the pandemic's spread, on the structure of the energy futures return connectedness network (EFRCN). Firstly, we measured the connectedness of 20 energy futures return series using the time-varying parameter vector autoregression (TVP-VAR) frequency connectedness approach and constructed a directed multi-layer dynamic EFRCN. Secondly, we developed a novel statistical indicator, the ratio of average vertex out-strength to average vertex in-strength, to represent the spillover intensity of a specific network layer. Finally, we utilized the pruned exact linear time (PELT) algorithm to pinpoint structural changepoints of COVID-19 lockdown policies and explored the change in the EFRCN structure before and after these changepoints. The empirical findings demonstrate that the scales of population and gross domestic product (GDP) impacted by the COVID-19 lockdown positively drive the average vertex strength, density, and clustering coefficient of the EFRCN, as well as the return spillover intensity of oil and power futures markets, respectively. Conversely, the regression results exhibit noteworthy negative causal relationships between the scales of population and GDP impacted by the COVID-19 lockdown and the return spillover intensity of coal and natural gas futures markets for non-periodic and short-term networks. Furthermore, the empirical results illustrate distinct structural changes in the evolution of global indicators of the non-periodic and short-term networks at the changepoints of COVID-19 lockdown policies before June 2020. To sum up, COVID-19 lockdown intensification increases the clustering of the EFRCN and significantly enhances the net spillover effects of oil and power futures markets within the EFRCN. Moreover, the changes in COVID-19 lockdown policies significantly influence the EFRCN's structure.

Time-varying and spillover effects of the macroeconomy on nonfinancial corporate financialization: Evidence from China

Using quarterly data from 2007 to 2020 in China and the time-varying parameter vector autoregression model (TVP-VAR), this study systematically investigates the time-varying responses of corporate financialization to various macroeconomic shocks. The empirical findings reveal that nonfinancial corporate financialization is significantly and dynamically influenced by macroeconomic factors. Specifically, a decrease in economic policy uncertainty (EPU) generates a transient promotive effect on corporate financialization within six months, while monetary policy easing has a strong and persistent positive effect on the corporate financialization over two years. Conversely, the impact of economic growth on corporate financialization has experienced structural shifts throughout the sample period. Furthermore, a dynamic connectedness index based on TVP-VAR is employed for the first time to investigate the spillover effects among variables. The results show that nonfinancial corporate financialization is the net receiver of the fluctuations of M2, GDP growth, and EPU volatility. Quantitatively, 28.36% of corporate financialization variation is caused by M2 supply changes. Moreover, the spillover effects on financialization are time-varying and significantly greater during the extreme economic events. Our conclusions enrich the research findings of corporate financialization determinants.

Are there inextricable connections among automobile stocks, crude oil, steel, and the US dollar?

According to the International Organization of Motor Vehicle Manufacturers (OICA), Automobile manufacturers use 40% of steel to make the body structure, 23% to make engines and gears, and 12% for suspensions. We untangled the perceived inextricable connections between crude oil, steel, the US dollar, and stocks of Toyota, Daimler, and Volkswagen as the automobile firms with the largest capitalization by using the time series daily data from 1998 to 2023. We used an augmented version of the Diebold and Yilmaz (2012) connectedness approach, which is based on the Time-Varying Parameter Vector Auto-regression (TVP-VAR) approach as introduced by Antonakakis et al. (2020). Findings suggested that Volkswagen, Daimler, and Toyota are more affected by shocks in steel prices than by USD and oil price shocks, with a spillover of 9%, 11.72%, and 8.72% respectively from steel. The next transmitter is USD, which contributes 4.94%, 6.37% and 3.96% of shocks to Volkswagen, Daimler, and Toyota stocks, and the third variable is the oil price shocks, which contributes 4.25%, 5.41% and 3.50% of spillover effects to Volkswagen, Daimler, and Toyota stocks, respectively. This shows the heterogeneous responses of automobile stocks with the highest capitalization and sales to the error variances transmitted from steel, USD as well as oil. Most notably, overall steel is a net transmitter and USD as net receiver in TYP-VAR system for Toyota and Daimler and can therefore be used as a diversifier. Our findings are impactful for the shareholders as well as portfolio managers for risk reduction and efficient portfolio management.

Dynamic Shrinkage Priors for Large Time-Varying Parameter Regressions Using Scalable Markov Chain Monte Carlo Methods.

Time-varying parameter (TVP) regression models can involve a huge number of coefficients. Careful prior elicitation is required to yield sensible posterior and predictive inferences. In addition, the computational demands of Markov Chain Monte Carlo (MCMC) methods mean their use is limited to the case where the number of predictors is not too large. In light of these two concerns, this paper proposes a new dynamic shrinkage prior which reflects the empirical regularity that TVPs are typically sparse (i.e. time variation may occur only episodically and only for some of the coefficients). A scalable MCMC algorithm is developed which is capable of handling very high dimensional TVP regressions or TVP Vector Autoregressions. In an exercise using artificial data we demonstrate the accuracy and computational efficiency of our methods. In an application involving the term structure of interest rates in the eurozone, we find our dynamic shrinkage prior to effectively pick out small amounts of parameter change and our methods to forecast well.

An Associative Analysis Method to Estimate Impact between Financial Market Risk and Macroeconomic Risk

The intertwining and close correlation between financial market risk and macroeconomic risk have been a focal point of academic research. By constructing financial stress index and macroeconomic risk index, and employing the Time-Varying Parameter Vector Autoregression (TVP-VAR) model, this study analyzes the complex dynamic interactions between financial market risk and macroeconomic risk. The results indicate a bidirectional and intersecting relationship between financial market risk and macroeconomic risk. Financial market risk exerts a relatively significant impact on macroeconomic risk, and its accumulation exacerbates the downward pressure on the macroeconomy, while its alleviation does not promptly lead to economic prosperity. Moreover, there exists a significant time-varying correlation between financial market risk and macroeconomic risk, with macroeconomic risk continuously augmenting its promoting effect on financial market risk.