Time-varying Parameter Vector Autoregressive Model Research Articles

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.

Dynamic interlinkages between geopolitical stress and agricultural commodity market: Novel findings in the wake of the Russian Ukrainian conflict

This study examines time-varying connectedness between agricultural commodities and geopolitical risk in terms of volatility. In this context, we employ the time- and frequency-based network connectedness approaches based on a time-varying parameter vector autoregression (TVP-VAR) model and use data from January 1, 2020, to January 4, 2023. Our findings indicate that (1) overall time-varying connectedness indexes are sharply amplified around geopolitical stress episodes; (2) wheat and the daily geopolitical risk index (GPRD) transmit notable volatility shocks starting in March 2022 because of the Russian invasion of Ukraine (RIU) on February 24, 2022; (3) persistent connectedness is sharply amplified around the RIU; and (4) temporary linkages dominate most of the period studied. Our findings have implications for investors, stakeholders, and policymakers in terms of their investment strategies and risk monitoring.

Are real interest rates a monetary phenomenon? Evidence from 700 years of data

In this paper we examine the effect of permanent inflation shocks on real interest rates, based on a structural Time-Varying Parameter Vector Autoregression (TVP-VAR) model that accounts for parameter instability, using the most extensive annual dataset that accounts for the entire economic history that dates back to 1310 for France, Germany, Holland (the Netherlands), Italy, Japan, Spain, the United Kingdom (U.K.) and the United States (U.S.). The Fisherian hypothesis of a one-to-one movement of inflation to nominal interest rates can only be rejected episodically, in favour of a Mundell-Tobin effect of less than proportional increase in the nominal interest rate to an inflation shock. Our findings suggest that long-run real interest rates of advanced economies have historically remain unaffected by inflation shocks due to a corresponding one-to-one increase in the nominal interest rate. Hence, the conclusions drawn by the majority of the existing literature based on post World War II samples should be treated with caution, due to sample selection bias.

Analysis of Dynamic Connectedness among Sovereign CDS Premia

This paper studies the dynamics of spillovers between sovereign Credit Default Swap (CDS) premia of nine countries, including Turkey, Russia, Brazil, South Africa, China, Germany, France, Italy and Spain. Weekly CDS data spans from July 2012 through June 2022. Adopting the methodology developed by Diebold & Yılmaz (2014), several connectedness measures are computed based on generalized forecast error variance decompositions generated through a time-varying parameter vector autoregressive model (TVP-VAR). The results show that the network’s connectedness level increased significantly during the COVID-19 outbreak and the Ukrainian war. Higher connectedness levels among European markets and developing countries are observed. Especially the connectedness levels between South Africa and other developing countries are remarkably high. The results reveal both fundamental-based and pure contagion channels and provide insight into the dynamic network of risk spillovers. A thorough understanding of international risk transmission channels is crucial for policy-makers and global investors regarding risk mitigation.

The Time-Varying Effects of Oil Shocks on the Trade Balance of Saudi Arabia

This study aims to analyze the impact of oil shocks on the external balance of Saudi Arabia, as one of the largest net oil-exporting countries. To this end, a time-varying parameter vector autoregression model (TVP-VAR) is estimated by using quarterly data covering the period between 1991: Q1 and 2021: Q4. We find that identifying the source of shocks plays an important role in understanding the time-varying impact of shocks on its economy. Our findings indicate that the global oil production shocks excluding Saudi Arabia have a negative and significant impact on the trade balance and are greater than the impact of the Saudi oil production shocks, which is not significant for most of the period. In addition, we found that oil price shocks have more profound and much greater impacts than global and domestic oil supply shocks. This may be attributed to the fact that oil price shocks are more than oil supply shocks, and supply shocks are linked to oil price shocks. However, impulse responses show that the effects of oil shocks are volatile over time and their effects are generally more pronounced during and immediately after global shocks. Our findings have serious implications for the trade balance of Saudi Arabia, particularly in the low and volatile oil price environment.

The time-varying impact of uncertainty on oil market fear: Does climate policy uncertainty matter?

Implied volatility index is a popular proxy for market fear. This paper uses the oil implied volatility index (OVX) to investigate the impact of different uncertainty measures on oil market fear. Our uncertainty measures consider multiple perspectives, specifically including climate policy uncertainty (CPU), geopolitical risk (GPR), economic policy uncertainty (EPU), and equity market volatility (EMV). Based on the time-varying parameter vector autoregression (TVP-VAR) model, our empirical results show that the impact of CPU, GPR, EPU, and EMV on OVX is time-varying and heterogeneous due to these uncertainty measures containing different information content. In particular, the CPU has become increasingly important for triggering oil market fear since the recent Paris Agreement. During the COVID-19 pandemic, CPU, EPU, and EMV, rather than GPR, play a prominent role in increasing oil market fear.

Analysis of Dynamic Connectedness Relationships among Clean Energy, Carbon Emission Allowance, and BIST Indexes

Understanding and examining energy markets correctly is crucial for stakeholders to attain maximum benefit and avoid risks. As a matter of fact, the volatility that occurred in energy markets and recent crises had major impacts on national economies. Dynamic connectedness relationships (DCRs) can make quite powerful predictions for both low-frequency data and limited time-series data. The objective of this study is to explicate the dynamic connectedness relationships among the BIST sustainability index, BIST 100 index, S&P Global Clean Energy index (S&P GCEI), and S&P GSCI carbon emission allowances (EUA). The daily data obtained over the period 11 April 2014–11 November 2022 were used for the research study. The DCRs among the variables used in the study were investigated by employing the time-varying parameter vector autoregressive (TVP-VAR) model. As a result of the study, the volatility from carbon emission allowances was determined to spill over to S&P GCEI, BIST 100, and BIST sustainability indexes. During the COVID-19 pandemic, significant reductions were detected in the volatility spillover (VS) from carbon emission allowances to S&P GCEI, BIST 100, and BIST sustainability indexes. Moreover, it was revealed that a weak VS existed from S&P GCEI to BIST sustainability and BIST 100 indexes. The findings reveal the importance of policymakers taking some incentive measures in EUA prices and also its role in portfolio diversification.

Time-Varying Relation between Oil Shocks and European Stock Market Returns

This paper considers a time-varying parameter vector autoregression model to analyze the varying impact of three types of structural oil shocks (the supply-side shock, the aggregate demand shock, and the oil-specific demand shock) on the European stock market since the 1990s. Our findings show that the three types of oil shocks heterogeneously influence stock market returns in the euro area, and that this influence considerably changes over time during the period considered. First, an unexpected increase in oil supply appears to exert a positive but generally declining effect in the period before the Global Financial Crisis (GFC) of 2007–2009, which descends into negative values after the GFC. Second, an unanticipated increase in aggregate demand triggers a generally positive effect on stock market returns in the euro area. However, in the period from 2003 to 2005, stock market returns responded negatively, which could be attributed to the so-called growth-retarding effect. Third, an unexpected increase in oil-specific demand instigates a negative response in the pre-GFC period (considering the response 4–5 months after the shock), although this changes to a positive effect thereafter. Interestingly, irrespective of the origin of oil price fluctuations, oil price increases are associated with positive European stock market returns after the GFC. This signals a greater degree of oil market financialization.

Media coverage of COVID-19 and its relationship with climate change indices: A dynamic connectedness analysis of four pandemic waves

This study explores the impact of the COVID-19 media coverage index (MCI) on the return and volatility connectedness of five MSCI Climate Changes Indices (the USA, Emerging Markets (EMU), Japan, Europe, and the Asia Pacific). The sample period was from 11 March 2020–19 January 2022, divided into sub-samples based on four waves of the COVID-19 pandemic. Thus, we use the time-varying parameter vector autoregression (TVP-VAR) model besides the frequency-dependent connectedness network approach. The key findings are as follows. First, the results demonstrate that the MCI is a net receiver of shocks in all waves, and the highest level of connectedness occurs in the first wave. The findings concerning volatility are similar, with the majority of MSCI Climate Change Indices being net transmitters, potentially indicating the severity of the pandemic. Second, estimating the short-, medium-, and long-term return network connectedness indicates the dominance of strong-term connectedness suggesting the spread of shocks within a week. Our results are robust by replacing MCI with Panic Index (PI). These results have implications for investors and policymakers.

Patterns of unconventional monetary policy spillovers during a systemic crisis

ABSTRACT We examine whether the COVID-19 pandemic-induced systemic shocks cause a change in the dynamics of monetary policy spillovers among developed economies. Results from our analysis under the time-varying parameter vector autoregressive model indicate that: (i) variations in monetary policy actions are explained by monetary policy spillovers; (ii) shocks from the COVID-19 pandemic rocketed monetary policy spillovers; (iii) the Euro area and the US chiefly propagate monetary policy shocks to their counterpart developed economies; and (iv) New Zealand and Japan endure the highest monetary policy shocks. Our results evidence the need for synchronized monetary policy actions during systemic crises.

The spillover of media sentiment on the sukuk bonds during COVID-19 pandemic

ABSTRACT This study focuses on the examination of the spillover impact of media sentiments (media coverage index, MCI), on the returns and volatility of the different investment graded sukuk bonds during the COVID-19 pandemic using the Time-varying Parameter Vector Autoregressive (TVP-VAR) model. We find that media sentiments have a stronger spillover impact on the bonds’ returns than their volatilities. The impacts are also found to be higher around the first few months of 2020 and 2021. The lower investment grade sukuk bonds are net transmitters of spillovers to their higher investment grade sukuk bonds counterparts. Sukuk bond BBB becomes a net recipient of spillovers and its net directional spillover relationship with sukuk bond AAA becomes zero. The spillover relationship is generally time-varying, with exceptional spillovers occurring during the early periods of the outbreak of the pandemic and the rise of its second wave.

Dynamic relationship among climate policy uncertainty, oil price and renewable energy consumption—findings from TVP-SV-VAR approach

This paper collects monthly data for the United States from January 2005 to April 2021 and develops a time-varying parameter vector autoregressive model with stochastic volatility (TVP-SV-VAR) model to investigate the time-varying relationship between climate policy uncertainty, oil prices, and renewable energy consumption. In addition, this paper discusses the dynamic impact of climate policy uncertainty on five types of renewable energy consumption. Our results show that the relationship between them is time-varying. In most periods, climate policy uncertainty positively affects oil prices in the short and medium term, and positively affects total renewable energy consumption in the short and long term. In addition, the impact of climate policy uncertainty on the consumption of the five types of renewable energy is heterogeneous. The results of the robustness tests indicate that our results are robust and our study has important implications for the development of relevant policies.

Are life insurance futures a safe haven during COVID-19?

This study aims to examine whether life insurance futures can serve as a hedge against the COVID-19 pandemic and whether they have the characteristics of a safe haven under the impact of the health shocks of the COVID-19 pandemic. We chose three life insurance stock futures in India and one in Taiwan as samples, including the market index of the two countries and the number of confirmed COVID-19 cases as sample variables. We used the growth rate of COVID-19 cases as the threshold variable, estimated the asymmetric threshold vector autoregression model, and found that insurance futures in the regime with a significant growth rate of confirmed COVID-19 cases can hedge against COVID-19 risks; therefore, insurance futures are a safe haven for the market. We further estimated the time-varying parameter vector autoregression model, and the impulse response results showed that insurance futures are a safe haven for COVID-19 pandemic risks.

The Dynamic Impacts of Monetary Policy Uncertainty Shocks

This paper assesses whether the impact of monetary policy uncertainty on the U.S. economy has changed over time. Estimating a Time-Varying Parameter Vector Autoregressive model on U.S. data from 1985Q1 to 2022Q3, we find that uncertainty shocks have larger negative effects on output during the COVID-19 recession than during other periods. However, financial market variables, such as stock prices and dividends, responded more significantly to uncertainty shocks during the Asian crisis of the late 1990s, the IT bubble of the 2000s, and the Great Recession. We then develop a Dynamic Stochastic General Equilibrium model with monetary policy uncertainty. Based on the calibrated model, we conduct several counterfactual exercises to demonstrate that the effects of uncertainty shocks depend on the state of the economy, which is consistent with the empirical evidence. These findings provide new insights into the time-varying nature of the impact of economic uncertainty.

The Time-Lag Effect of Climate Factors on the Forest Enhanced Vegetation Index for Subtropical Humid Areas in China.

Forests represent the greatest carbon reservoir in terrestrial ecosystems. Climate change drives the changes in forest vegetation growth, which in turn influences carbon sequestration capability. Exploring the dynamic response of forest vegetation to climate change is thus one of the most important scientific questions to be addressed in the precise monitoring of forest resources. This paper explores the relationship between climate factors and vegetation growth in typical forest ecosystems in China from 2007 to 2019 based on long-term meteorological monitoring data from six forest field stations in different subtropical ecological zones in China. The time-varying parameter vector autoregressive model (TVP-VAR) was used to analyze the temporal and spatial differences of the time-lag effects of climate factors, and the impact of climate change on vegetation was predicted. The enhanced vegetation index (EVI) was used to measure vegetation growth. Monthly meteorological observations and solar radiation data, including precipitation, air temperature, relative humidity, and photosynthetic effective radiation, were provided by the resource sharing service platform of the national ecological research data center. It was revealed that the time-lag effect of climate factors on the EVI vanished after a half year, and the lag accumulation tended to be steady over time. The TVP-VAR model was found to be more suitable than the vector autoregressive model (VAR). The predicted EVI values using the TVP-VAR model were close to the true values with the root mean squares error (RMSE) < 0.05. On average, each site improved its prediction accuracy by 14.81%. Therefore, the TVP-VAR model can be used to analyze the relationship of climate factors and forest EVI as well as the time-lag effect of climate factors on vegetation growth in subtropical China. The results can be used to improve the predictability of the EVI for forests and to encourage the development of intensive forest management.

Effect of the monetary policy in the United States on the international share of the U.S. Dollar: 1914-1945

The United States Dollar (USD) replacement of the sterling as the dominant currency is not only the result of the "invisible hand," but also the "visible hand." This study analyzes the effect of the monetary policy in the United States (U.S.) on the international share of the USD from 1914 to 1945 using the Bayesian technique, to estimate the time-varying parameter vector autoregressive (TVP-VAR) model. The study posits two main findings. First, the time-point impulse response shows that the increase in the U.S. interest rate results in an increase in the international share of the USD, implying that this increase has an expansion effect on the USD, and the effect has no time-varying characteristics. Second, the equal time interval impulse response shows that the effect of the monetary policy on the share of the USD is greater in the short term.

VOLATILITY SPILLOVER BETWEEN GERMANY, FRANCE, AND CEE STOCK MARKETS

The CEE stock markets are more and more integrated in the European financial markets. The growth of the integration of financial markets favours the volatility and return spillover between them. The current study analyses the volatility spillover among the stock markets in the countries from Central and East Europe (CEE) and Germany and France with the aim to identify the possibilities of reduction of a portfolio risk. A special attention is granted to the analysis during the pandemic caused by COVID-19. The time-varying parameter vector autoregressive (TVP-VAR) model on which is based the methodology proposed by Antonakakis and Gabauer (2017) is used to estimate the evolution in time of volatility spillover. The empirical results obtained for the period January 2001 – September 2021 highlight the increase in volatility spillover between the countries analysed when the pandemic caused by COVID-19 was confirmed. The lack of volatility integration of the markets analysed enables the making of arbitrages in order to reduce the risk of a portfolio. The results obtained are important in the management of financial asset portfolios.

The Time-Varying Effect of Interest Rates on Housing Prices

This study analyzes the time-varying effect of interest rates on housing prices. As housing prices are too high for most consumers to afford with income alone, they use bank loans. Consequently, when interest rates fall, the demand for housing increases, causing prices to rise. This effect of interest rates was common in countries that implemented low-interest rates in response to the COVID-19 pandemic. Using Korean data from March 1991 to March 2022, this study examined the impact of interest rate shocks on housing prices by employing a time-varying parameter vector autoregressive model. According to the analysis, in Korea, while the impact of the interest rate shocks on housing prices was not significant before the global financial crisis, it increased dramatically afterward. Particularly, the impact of interest rate shocks was strongest relative to the past during the period of the increase in house prices from 2020 to 2021. The rise in the effects of interest rate shocks on housing prices is attributed to the increased dependence on loans for housing purchases. The results suggest that given the recent substantial increments in interest rates due to inflation, an interest rate shock would likely cause a global housing market recession.

On the volatility of WTI crude oil prices: A time-varying approach with stochastic volatility

This study investigates the impacts of crude oil-market-specific fundamental factors and financial indicators on the realized volatility of West Texas Intermediate (WTI) crude oil price. A time-varying parameter vector autoregression model with stochastic volatility (TVP-VAR-SV) is applied to weekly data series spanning January 2008 to October 2021. It is found that the WTI oil price volatility responds positively to a shock in oil production, oil inventories, the US dollar index, and VIX but negatively to a shock in the US economic activity. The response to the EPU index was initially positive and then turned slightly negative before fading away. The VIX index has the most significant effect. Furthermore, the time-varying nature of the response of the WTI realized oil price volatility is evident. Extreme effects materialize during economic recessions and crises, especially during the COVID-19 pandemic. The findings can improve our understanding of the time-varying nature and determinants of WTI oil price volatility.