Time-varying Parameter Vector Autoregression Research Articles

Estimating the natural rate of interest for the emerging Asia

This paper evaluates the natural rate of interest (NRI) for Indonesia, Malaysia, Philippines, Singapore, South Korea, [Taipei, China], and Thailand and aggregates them using a GDP-weighted average. This paper discusses four key points. Firstly, to estimate the NRI in emerging Asian economies, this paper introduces a Time Varying Parameter Vector Autoregression (TVP-VAR) model that integrates exchange rates alongside conventional growth and inflation variables. Secondly, the results show that NRIs in the emerging Asia have been declining in trend since the 1990s. Thirdly, the behavior of the NRI for each economy is not uniform across crises—the Asian Financial Crisis, the Global Financial Crisis, and the COVID-19 pandemic. Fourthly, the findings reveal that the ascertained Asian NRI decreased to the vicinity of 0% during the COVID-19 pandemic period.

Volatility and returns connectedness between cryptocurrency and China’s financial markets: A TVP-VAR extended joint connectedness approach

We employ a time-varying parameter vector autoregression (TVP-VAR) joint connectedness approach to study the dynamic risk spillover effects between cryptocurrencies and China’s financial market, further exploring the impact of cryptocurrencies on China’s financial market. Our results show that there is asymmetric risk transmission between cryptocurrencies and China’s financial market, and the risk spillover effect is very weak. Specifically, the spillover of cryptocurrencies to China’s financial market is significantly stronger than the spillover of China’s financial market to cryptocurrencies. Cryptocurrencies have a stronger spillover effect to China’s exchange rate and gold. The net spillover effect of cryptocurrencies is weakening over time. Overall, the return spillover impact of cryptocurrencies on China’s financial market is greater than the volatility spillover impact, and the degree of impact of different cryptocurrencies is heterogeneous. The findings of this study have several implications for policymakers and investors.

Time-varying interrelations between digitalization and human capital in Vietnam

In this study, we use a time-varying parameter vector autoregression in conjunction with the extended joint connectedness approach (TVPVAR-EJC) to examine the interrelationships among five variables, namely digitalization measured by the number of individuals using the Internet (% of the population), mobile cellular subscriptions (per 100 people), human capital index, CO2 emissions, and GDP, from 1996 to 2019 in Vietnam. Net total directional connectedness of digitalization and other variables suggests that it consistently acts as a net shock transmitter, whereas human capital was found to be a net receiver of corresponding shocks from 1997 to 2019. The emission of CO2 was a net critical receiver in 1998 but remained as a net transmitter for the rest of the period. Pairwise connectedness reveals that digitalization appeared as a net shock transmitter in connection with the human capital index and GDP. In the relationship with human capital, digitalization’s role was critical.

Asymmetric connectedness between conventional and Islamic cryptocurrencies: Evidence from good and bad volatility spillovers

This paper examines the dynamics of the asymmetric volatility spillovers across four major cryptocurrencies comprising nearly 61% of cryptocurrency market capitalization and covering both conventional (Bitcoin and Ethereum) and Islamic (Stellar and Ripple) cryptocurrencies. Using a novel time-varying parameter vector autoregression (TVP-VAR) asymmetric connectedness approach combined with a high frequency (hourly) dataset ranging from 1st June 2018 to 22nd July 2022, we find that (i) good and bad spillovers are time-varying; (ii) bad volatility spillovers are more pronounced than good spillovers; (iii) a strong asymmetry in the volatility spillovers exists in the cryptocurrency market; and (iv) conventional cryptocurrencies dominate Islamic cryptocurrencies. Specifically, Ethereum is the major net transmitter of positive volatility spillovers while Stellar is the main net transmitter of negative volatility spillovers.

Monetary policy spillovers between the US and African Central Banks: A time- and frequency-varying connectedness study

This study investigates time and frequency connectedness between monetary policy in the US and 7 African countries to determine the extent to which US monetary policy influences policy conduct amongst African Central Banks. We use a time-varying parameter vector autoregressive (TVP-VAR) framework with frequencies to extract the spectral representation of the forecast error variance decompositions of the TVP-VAR and form 3 bands of frequency strata corresponding to long-run, medium-run and short-run. The results of the study are as follows: firstly, the static analysis shows that the US is the dominant transmitter of systemic shocks across all frequencies and the vulnerability of African countries as recipients of these shocks varies across frequency bands. Secondly, the dynamic analysis further reveals stronger short- and medium-run systemic connectedness during the periods of the Large Scale Asset Purchase programme and forward guidance policies whereas long-run connectedness is prominent during periods of US conventional monetary policy particularly in the COVID-19 era. The findings from the country-by-country dynamic spillover specifically show that countries which are more (less) responsive to US monetary policy shocks have lower (higher) inflation rates averages since the start of the pandemic. The findings suggest that African Central Banks can be benefit from higher coordination with the US Federal Reserve and we further propose that Central Banks worldwide in setting similar inflation targets.

Unveiling the mystery of the responsiveness of inbound tourism to economic policy uncertainty: New evidence from Australia

Using a time-varying parameter vector autoregressive (TVP-VAR) model, this study analyzes how inbound tourist arrivals (TOUR) respond to EPU in Australia. Empirical results show that EPU can Granger cause TOUR in most cases before COVID-19, whereas little evidence supports this finding after the pandemic. At the national level, it is found that EPU was the net risk transmitter before COVID-19 but became the net risk receiver after COVID-19, while TOUR was the net risk receiver over March 2007-March 2020 but became the net risk transmitter in the remaining months. The impulse response functions reflect that a 2.81% decline in the total TOUR was observed due to a 1% increase in EPU, which gradually decayed within 9 months for the sub-sample before COVID-19. Similar findings hold at the state/territory level. These findings provide profound evidence for the national and state/territory governments to allocate tourism resources and formulate supportive tourism policies.

Spillover dynamics in DeFi, G7 banks, and equity markets during global crises: A TVP-VAR analysis

Decentralized finance (DeFi) has become of significant interest for investors in both the financial and digital sectors. We use a time-varying parameter vector autoregression (TVP-VAR) approach to estimate the static and dynamic connections between and within DeFi, G7 banking, and equity markets. We focus on critical events such as the COVID-19 pandemic, the cryptocurrency bubble, and the Russia-Ukraine conflict. The results highlight interconnectedness and significant spillovers within and between the markets, especially during the COVID-19 pandemic. Notably, there were significant spillover effects from the G7 banking and equity markets to Japan and DeFi assets. The findings demonstrate a robust connection between DeFi platforms, G7 banking, and stock markets throughout these tumultuous periods. Policymakers, investors, and entrepreneurs are recommended to keep a close eye on changes in traditional banking and equity markets to adjust the risk of DeFi assets.

A TVP-VAR assessment of the spillover effects of geopolitical risk shocks on macroeconomic variability: a study of the Ghanaian economy

Our study verified the implications of the spillover of geopolitical risk (GPR) shocks to the economic crisis in Ghana. Our analysis employed the VAR-based spillover models by Diebold and Yilmaz (Int J Forecast 28:57–66, 2012; J Econ 182:119–134, 2014) and the Time-Varying Parameter Vector Autoregressive (TVP-VAR) connectedness approach by Gabauer and Antonakakis (Munich personal RePEc archive refined measures of dynamic connectedness based on TVP-VAR refined measures of dynamic connectedness based on TVP-VAR*, 2017). We scrutinized the interconnections and transmission mechanisms among key macro-financial variables spanning from 2000 to 2022. Our findings indicate that GPR is a fundamental source of shocks to the foreign exchange reserve (FXI), real exchange rate (REER), consumer price index (CPI), and debt. Other significant contributors include export (EXP) and import (IMP), with EXP standing out as the main shock transmitter. On the receiving end, CPI is most impacted by transmissions from IMP and GPR. Our study demonstrates that EXP and IMP are the top shock contributors, while FXI and CPI are the major recipients of these shocks. Such findings provide policymakers with valuable insights into the ramifications of geopolitical risk on the macroeconomic environment. Hence, policymakers are expected to provide necessary buffers to curb the influence of geopolitical risks on the economy.

When you need them, they are not there: hedge capacities of cryptocurrencies disappear in downtrend markets

We provide empirical evidence supporting the economic reasoning behind the impossibility of diversification benefits and the hedge attributes of cryptocurrencies remaining in force during the downside trends observed in bearish financial markets. We employ a spillover connectedness model driven by time-varying parameter vector autoregressions on daily data covering January 2018 to November 2022 to analyze spillover transmissions between conventional and digital markets, focusing on the role of stablecoin issuances. We study the stock, bond, cryptocurrency, and stablecoin markets and find very high connectedness, which varies over time in response to up/down trends in financial markets. The results show that during financial turmoil, cryptocurrencies amplify downside risks rather than serve as diversifiers. In addition to risky assets from conventional financial markets, cryptocurrencies champion the transmission of spillovers to digital and conventional markets. In contrast, changes in stablecoin issuances produce few shocks because of their pegged prices, but they facilitate investors’ switch from volatile cryptos to more stable digital instruments; that is, we observe a phenomenon designated by us as the “flight-to-cryptosafety.” We draw insightful conclusions, provoking new thinking regarding portfolio hedge strategies that could potentially benefit investors when searching for less volatile investment performance.

Comparing Islamic and conventional stock markets in GCC: a TVP-VAR analysis

PurposeThis study investigates the dynamic interdependence between Islamic and conventional stock markets in the Gulf Cooperation Council (GCC) economies and the influence of global financial uncertainties on this interconnection.Design/methodology/approachThe study employs the time-varying parameter vector autoregressions (TVP-VAR) technique and analyzes daily data from December 1, 2008 to July 14, 2021.FindingsThe research reveals robust interconnectedness within individual countries between Islamic and conventional stock markets, particularly during crises. Islamic stock markets exhibit greater susceptibility to spillover effects compared to conventional stocks. The UAE and Kingdom of Saudi Arabia (KSA) stock markets are identified as net transmitters of spillovers, while Oman, Bahrain and Kuwait receive more spillovers than they transmit. Global financial uncertainty measures (GVZ, USEPU and UKEPU) positively influence financial market interconnectedness, with EVZ exhibiting a negative impact while VIX and OVX remain statistically insignificant.Practical implicationsInvestors and portfolio managers in Oman, Bahrain and Kuwait should carefully evaluate the UAE and KSA markets before making investment decisions due to the latter's role as net transmitters in the region. Additionally, it is emphasized that Islamic and conventional stocks should not be considered interchangeable asset classes for risk hedging.Social implicationsInvestors must be aware that Islamic and conventional stocks cannot be used as an alternative asset class to hedge risk.Originality/valueThe present article offers valuable insights for practitioners and researchers delving into the comparative analysis of Islamic and conventional stock markets within the GCC context. It enhances our comprehension of the dynamic interdependence between Islamic and conventional stock markets in the GCC economies and the impact of global financial uncertainties on this intricate relationship.

How does inflation propagate among CPI components? Evidence from the euro area

This study employs a time-varying parameter vector autoregressive (TVP-VAR) model to investigate infla- tion propagation and spillovers among twelve major Consumer Price Index (CPI) components across eleven euro area countries from 1999 to 2023. We uncover empirical regularities in the transmission channels of inflation, noting Transport and Clothing and Footwear as primary shock transmitters, whereas Communi- cation, Alcohol and Tobacco consistently absorb shocks. These insights, consistent across various contexts and countries, contribute to understanding inflation persistence and households’ inflation experiences in the Euro area.

Blessings or curse: How do media climate change concerns affect commodity tail risk spillovers?

In this paper, we examine the time-varying tail risks transmission among the agricultural, precious metals, and energy commodities markets, and explore how climate change concerns affect this connectedness. Using the Conditional Autoregressive Value-at-Risk (CAViaR) model and the time-varying parameter vector autoregressive (TVP-VAR) connectedness model, our empirical analysis reveals several key findings. First, our tail risk-based approach shows that tail risks transmission rises during crisis periods such as the GFC of 2007 and the Covid period of 2020. Second, climate risks, in particular climate transitions risks, play an important role in commodity tail risk connectedness. These findings are important for investors, practitioners, and policymakers. Our results are robust to a number of robustness tests.

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.

Understanding the spillover effects of ethanol production and energy prices on African food markets: A time-varying approach

Amidst recent global uncertainty caused by events such as the COVID-19 pandemic and wars, African countries are finding it even more challenging than before to achieve food security. This study utilizes the time-varying parameter vector autoregression (TVP-VAR)–based extended joint connectedness model to analyze the impact of external factors, such as U.S. ethanol production, grain speculation, international energy prices, international food prices, and international corn prices, on domestic food prices in four African countries (Ethiopia, Ghana, South Africa, and Tanzania). The key results of the study can be summarized as follows: (1) The production of ethanol in the United States has an influence on the food prices in all four sample countries, with the extent of this impact differing from one nation to another. Notably, the effects of ethanol production were particularly pronounced until the years 2011–2012. (2) In Ethiopia and Tanzania, the domestic food market shows only a slight impact from speculative activities, whereas in Ghana and South Africa, these activities have a notable effect. (3) International energy prices play a significant role in shaping the domestic food prices across all four countries.

Do macroprudential policies reduce risk spillovers between energy markets?: Evidence from time-frequency domain and mixed-frequency methods

Preventing volatility spillovers between energy markets is essential for maintaining financial stability. Macroprudential policies play an invaluable tool in financial risk monitoring and help to reduce the incidence of systemic risk. However, the effectiveness of macroprudential policies on risk spreads in the energy system is still unclear. This article applies the TVP-VAR (time-varying parameter vector autoregression) spillover methods to capture the risk connectedness between energy markets in the time and frequency domains, and constructs an asymmetric GARCH-MIDAS-MPP model to investigate the influence of macroprudential policies on volatility overflows of the energy system. The results suggest that there are strong risk links between global energy markets and long-term risk resonances play a dominant role. Time-varying risk overflows occur throughout the energy sector, and market turbulence amplifies these risk communications. Whether on a time or frequency horizon, the oil market represents the largest emitter of fluctuation transmissions, while the fuel oil market stands out as the biggest recipient of risk diffusion. We do not observe that macroprudential policies can significantly reduce the average and long-run risk premiums of the energy system. Conversely, our results reveal that macroprudential policies intensify short- and medium-term risk spreads between global energy markets. This paper provides some insight into energy market risk contagion, which is conducive to the improvement of macroprudential policies for energy market risk management.

Volatility spillovers and carbon price in the Nordic wholesale electricity markets

This paper investigates price volatility and spillovers in the Nordic electricity wholesale markets. We use the Time-Varying Parameter Vector Autoregressive (TVP-VAR), Rolling Window-based VAR (RW-VAR), and high dimensional VAR with common factors (VAR-CF) methods and analyze the integration dynamics among these markets and impact of carbon prices on volatility spillovers. We use 107,352 hourly price data from January 2010 to March 2022. The novelty of this research is four-fold. First, we adopt a connectedness approach to explore volatility interactions among the four Nordic markets, contributing to the scarce literature on volatility in this market. Second, we segment the Norwegian market into southern and northern regions, revealing differences in volatility spillover patterns. Third, we investigate the effect of carbon prices on volatility spillovers and market dynamics. Last, we show significant contribution of covariances to interdependence among markets. We find significant connectedness between the Nordic markets, with an average Total Connectedness Index of between 50% (with a system of variance) and 90% (with a system of both variance and covariance). Sweden is the sole net volatility spillover transmitter, while Denmark experiences the largest shocks from the system. We further find that carbon prices exert a 5% significant impact on the volatility spillover index.

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.

Dynamic connectedness, portfolio performance, and hedging effectiveness of the hydrogen economy, renewable energy, equity, and commodity markets: Insights from the COVID-19 pandemic and the Russia-Ukraine war

This study employs a Time-Varying Parameter Vector Auto Regressive (TVP-VAR) connectedness approach to investigate the dynamic interconnections, portfolio performance, and hedging effectiveness across hydrogen economy, renewable energy markets, equities, and energy commodities amidst the backdrop of the COVID-19 pandemic and the Russia-Ukraine conflict. Our findings reveal a consistent level of average connectedness during both black swan events, with dynamic connectedness reaching its peak during the COVID-19 crisis. While some assets (hydrogen economy index, renewable, solar and clean technology index) persistently function as net transmitters, the others (geothermal index, crude oil, natural gas, biofuel, and gold) consistently act as net receivers. We also find that the role of equity indices (wind energy, fuel cell, FTSE_100, and S&P 500) as net transmitters or receivers changes over time. The network plot results show that the hydrogen economy typically functions as shock transmitters for equity market indices and commodities. Portfolio analysis underscores the superior risk-adjusted performance of the minimum variance (connectedness) portfolio during the COVID-19 crisis in comparison to the Russia-Ukraine conflict. Furthermore, our examination of effective hedge and portfolio strategies demonstrates that managed collateralized portfolio weights of hydrogen economy and renewable energy assets (equities) in the minimum variance portfolio increase (decrease) from 7% to 12% (53%–41%) during the COVID-19 crisis, and subsequently decline (rise) further to 6% (53%) amidst the Russia-Ukraine conflict. These findings suggest a preference for hydrogen economy and renewable energy assets as hedging instruments in health-related financial crises over crises precipitated by military conflicts.

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.

Can carbon emission trading markets reduce the risks in traditional energy markets?

As the largest energy consumer in the world, China maintains a tightly interconnected energy market in the international arena. Therefore, it is necessary to explore the interdependence and spillover effects between the carbon emissions trading markets, traditional energy markets in China, and International markets. We explore changes in risk in the Chinese energy market with the introduction of the Chinese carbon market by employing the Vine Copula function to construct a vine structure and analyse the complex interdependence among markets. Moreover, we utilise the Time-Varying Parameter Vector Auto Regression and Diebold-Yilmaz Spillover Index (TVP-VAR-DY) method to examine the dynamic spillover effects of risk across various markets. Empirical analyses reveal a strong interdependence between the carbon emissions trading market and the traditional energy market. The Brent crude oil market occupies a central position within the system and significantly influences other markets. The carbon emissions trading market on the periphery has little impact on the system. Additionally, significant volatility spillover effects—observed between the carbon emissions trading market and traditional energy market systems—are closely correlated with economic conditions and extreme events, demonstrating time-varying characteristics. Furthermore, the Chinese carbon emissions trading market effectively reduces risk spillover in traditional Chinese energy markets from international markets during periods of risk contagion, particularly during times of high volatility, which can offer policymakers a novel path for risk management and provide investors with new avenues for investment hedging strategies.