Time-varying Parameter Vector Autoregressive Model Research Articles

Spillover Effects between Oil, Gold, Stock, and Exchange Rate Returns in Thailand: An Extended Joint Connected TVP-VAR Approach

The objective of this study is to analyze the spillover effects among the returns of oil, gold, the stock market, and exchange rates in Thailand. Using the time-varying parameter vector autoregression model (TVP-VAR) with extended joint connectedness and data from April 2002 to March 2024, our analysis reveals a moderate level of the dynamic linkage among these returns. We observe that the dynamic connectedness among all returns varies over time, influenced by global economic events and country situations. Notably, in Thailand's landscape, stock market and gold returns act as net shock transmitters, with the stock market exhibiting the highest volatility among all variables, while oil and exchange rate markets function as net recipients. These insights significantly contribute to understanding asset and commodity markets and offer valuable policy implications for effectively managing these markets.

Cross-border ESG rating dynamics: An in-depth connectedness analysis of portfolio returns and volatilities in the USA and Canada

This study uses a time-varying parameter vector autoregression (TVP-VAR) model to examine the dynamic relationship between rating changes and portfolio returns in the US and Canada across the environmental (E), social (S), governance (G) and total ESG assessment pillars. The analysis includes both return and volatility spillovers and covers the period from March 2009 to October 2022. The study reveals a fluctuating pattern of connectedness, influenced by global financial events, such as the 2008 financial crisis. In particular, the US shows higher levels of connectedness. Rating changes, particularly in the ESG dimension, show stronger spillovers than returns, highlighting their importance in portfolio construction. The study further explores net connectedness profiles, identifying ESG rating changes as net transmitters. The results suggest that investors should prioritize rating changes over returns, highlighting the importance of considering ESG factors in portfolio management, especially the social criterion, to mitigate investment risks. The research contributes to the understanding of ESG dynamics in international equity markets and provides valuable insights for investors and market regulators.

Sparse-structured time-varying parameter vector autoregression for high-dimensional network connectedness measurement

This paper proposes a new sparse-structured time-varying parameter vector autoregression (SS-TVP-VAR) model to effectively measure high-dimensional network connectedness of financial enterprises. Previous VAR based network connectedness methods require high computational cost and are not feasible for high-dimensional networks of a large number of enterprises. The proposed SS-TVP-VAR not only reduces computational cost but also selects key coefficients when building the VAR model. As a result, it produces a more adaptive and effective connectedness to measure systemic risk caused by extreme events. Studies on 57 listed major financial enterprises in China mainland during a 15-year period show that the proposed SS-TVP-VAR achieves reliable and useful results, and it successfully identifies all the major market events during this time.

Risk spillover effect of the new energy market and its hedging effectiveness: New evidence from industry chain

During China’s industrial transformation, studying risk spillover effects and clarifying risk contagion pathways within the new energy industry chain is critical for promoting high-quality development in the new energy. The risk transmission mechanism of the new energy industry chain is explored by constructing a time–frequency index using a time-varying parameter vector autoregressive model in this paper. Additionally, we also further analyze the portfolio and hedging effectiveness of the new energy market. The findings reveal that, first, the downstream market serves as the primary risk source, propagating risk through the industry chain to the midstream and upstream sectors within the new energy industry chain contagion system. Second, the risk spillover effect of the new energy market has significant heterogeneity and time-varying due to the impact of short-term frequency domain drivers and extreme events. Third, the portfolio significantly reduces the investment risk of a single asset. With the extension of the investment horizon, investors’ portfolio weights in the upstream, midstream and downstream industries gradually tend to be even.

Mitigating digital market risk with conventional, green, and Islamic bonds: Fresh insights from new hybrid deep learning models

We examine the impact of conventional, green, and Islamic bonds on the long-term memory of cryptocurrency market risk. Utilizing a time-varying parameter vector autoregressive deep learning model, we integrate time-varying parameter vector autoregressive methods with advanced deep learning sequence modeling architectures, including temporal convolutional network, gated recurrent unit, and long short-term memory for December 18, 2017, to April 19, 2024. Results indicate that incorporating all fixed-income securities reduces digital market risk. However, conventional and green bonds have a particularly strong impact on improving the long-term memory of digital market risk, while this is not the case for Sukuk.

Contagion effect between fuel fossil energies and agricultural commodity markets and portfolio management implications

This study investigates the dynamic spillover effects between fossil fuels (Brent oil and natural gas) and major agricultural markets (maize, soybeans, and wheat) using a time-varying parameter vector autoregression (TVP-VAR) model. Additionally, we quantify the cumulative return of the investment portfolio, optimal weights, time-varying risk coverage, and hedging effectiveness of bivariate commodity portfolios using the Minimum Variance Portfolio (MVP), Minimum Correlation Portfolio (MCP), Minimum Connectedness Portfolio (MCoP) under bearish, tranquil, and bullish market conditions. The results show that maize and natural gas are the main net receivers of spillovers, whereas wheat and Brent oil are the main net transmitters of shocks to the network, respectively. Our findings of optimal weights in multivariate portfolio suggest more weights for agri-commodities against fossil fuels in all three modes of market. In addition, the optimal weights of bivariate portfolio analysis show that the most optimal portfolio which has the highest HE value is related to natural gas pairs with agri-commodities. Moreover, depending on the status of the market; in an average and bearish modes, the highest optimal weight is related to maize/natural gas. However, in the bullish market, the highest optimal weights are related to natural gas/Brent oil pair. Furthermore, the results of optimal bivariate hedge ratios show that natural gas/wheat (wheat/maize and vice versa) portfolio is the cheapest (the most expensive) investment strategy in all modes of market and based on MVP and MCoP approaches.

China’s GDP-at-Risk: Real-Time Monitoring, Risk Tracing, and Macroeconomic Policy Effects

Timely monitoring GDP-at-risk and tracing economic downside risk sources can help establish effective risk warning and prevention systems. This study constructs a probability distribution for China’s economic growth with skewness determined by a multidimensional predictor information set of macro fundamentals. Such a treatment allows us to identify changing drivers of economic downside risks during monitoring GDP-at-risk’s dynamic evolutionary path. We also employ a time-varying parameter vector autoregression model with random volatility to explore the heterogeneous impacts of different macroeconomic policy instruments on economic slowdowns. Our results provide empirical support for macroeconomic management and policy formulation in emerging markets. We reach three conclusions. First, the dynamics of GDP-at-risk exhibit significant event-driven characteristics, and economic downside risk increases significantly under the influence of extreme events. Moreover, the probability distribution of economic growth is asymmetric--as the downside risk of the economy increases, its upside potential increases disproportionately. Second, the time-varying risk trace of GDP-at-risk shows that the contribution of financial conditions and local government debt to economic downside risk declines. The importance of the risk-driving role of housing price growth gradually increases, suggesting that China’s property prices can provide more valuable early warning information about future growth risk, allowing time for precise preventive measures. Nevertheless, interest rates and inflation as risk divers have consistently minimal impacts. Third, the heterogeneity impulse response function of GDP-at-risk suggests that quantity-based monetary policy and fiscal policy can manage economic downside risks in the short run. In contrast, price-based monetary policy can curb economic overheating and reduce downside risks in the medium to long term. Therefore, the effect of price-based monetary policy is more sustainable in China.

Leadership shift in the global soybean market: Dynamic connectedness approach (TVP-VAR)

The price transmission in international soybean market has been extensively examined. However, recent econometric advancements have enabled the application of dynamic connectedness methodology as outlined by Antonakakis and Gabauer (2017) [1], which is based on a Time-varying Parameter Vector Autoregressive (TVP-VAR) model. This approach captures the time-varying connectedness of time series, considering potential risk shock emitters and receivers. The connectedness index (Diebold and Yilmaz, Jan. 2012) [2] was developed using Generalized Forecast Error Variance Decomposition (GFEVD) and the Generalized Impulse Response Function (GIRF) (Koop et al., Sep. 1996; Pesaran and Shin, Jan. 1998) [3,4]. This study aims to understand the dynamic connectedness and price leadership. The research examined markets including the US Soybean futures market (Chicago Board of Trade), Rotterdam Port spot market representing the European soybean market, Paranaguá port representing Brazil, Argentina represented by Rosario Futures and Spot, and the Chinese domestic spot market and the Dalian futures on behalf of China. The research spanned approximately ten years, from September 2009 to May 2019. The findings suggest that the soybean market has reached a high level of maturity, able to withstand exogenous shocks for at least the past seven years. The net pairwise directional connectedness revealed dynamic and bidirectional causality. The dynamic connectedness index showed a highly connected and developed market in the West (Chicago Futures, Rotterdam, Paranaguá, and Rosario Futures and Spot). However, the connectedness between Western and Eastern markets was relatively low, indicating some level of market isolation. Furthermore, the pairwise connectedness index between Eastern market (China spot and Dalian Futures) was also considerable low. Lastly, Paranaguá and Rosario overtook Rotterdam as price-leading markets and were identified as the primary net transmitters of shocks, indirectly implying causality. Chicago, Rosario, and Paranaguá formed a triumvirate leading international prices, while Rotterdam adopted a secondary leading position, deviating from its historical role as the price leader.

Time-Varying Role of House Prices on Monetary Policy in Türkiye

The main focus of this study is examining the time-varying effects of house prices on monetary policy in Türkiye from February 2010 to November 2021. We analyse these effects using a time-varying parameter vector autoregressive model with stochastic volatility (TVP VAR-SV). The evidence indicates that responses of inflation to positive house price shocks decrease over time. The response of credit and interest rates to positive house price shocks increases after 2018. The results reveal that the responses of the Central Bank of the Republic of Türkiye (CBRT) to the changes in the housing market have changed over time.

The impact of COVID-19 on global financial markets: A multiscale volatility spillover analysis

This paper presents a comprehensive multiscale analysis of volatility spillover mechanisms among diverse financial markets utilizing a novel combination of Normalized Adaptive Multivariate Empirical Mode Decomposition (NA-MEMD) and Time-Varying Parameter Vector Autoregressive (TVP-VAR) model. Our analysis spans Bitcoin, crude oil, gold, foreign exchange, emerging stock markets, and developed stock markets, examining their respective roles in global volatility transmission. Utilizing the NA-MEMD methodology and a time-varying volatility spillover index, we find that in the short-term, Bitcoin and crude oil primarily act as volatility receivers, while other markets serve as volatility transmitters. Over the long-term, gold and Bitcoin consistently act as volatility receivers, with Bitcoin and gold showing evident safe-haven effects, while the remaining markets primarily function as volatility transmitters. Particularly post-COVID-19, developed stock markets emerge as significant transmitters of volatility, largely directed towards the crude oil market. By evaluating and comparing risk transmission patterns among financial markets before and during the COVID-19 pandemic, our findings offer valuable insights that can assist policymakers and other stakeholders in managing financial uncertainty in times of global crises.

A new method for measuring financial resilience

This paper proposes a new model to measure financial resilience from the perspective of external risk shocks, which composes of three submodels, namely, the dynamic factor model, the time-varying parameter vector autoregression (TVP-VAR) model, and the resilience characteristic measurement model with two dimensions: absorption intensity and absorption duration. At the theoretical level, we simulate and analyze the changing paths of financial resilience under different scenarios. At the empirical level, we apply the model to study the resilience of the UK financial market. The results indicate that the UK financial resilience fluctuations exhibit phased characteristics and there is a clear inverse relationship between absorption intensity and absorption duration. Notably, periods of low resilience often coincide with specific risk events.

Asymmetric TVP-VAR connectedness between highly traded commodities and hedging strategies: Evidence from major contagions

The objective of this study is to examine the return interconnectedness and asymmetric spillover effects in global commodity futures markets, with a focus on the impact of contagion. A competent asymmetric time-varying parameter vector autoregressive (TVP-VAR) model was employed for highly traded commodity futures (cocoa, coffee, corn, cotton, soy, sugar, wheat, and oil) between January 1, 2000, and March 31, 2024. This study investigates the connectedness of commodities in three dimensions: asymmetric spillovers, the influence of oil and oil substitutes on the network, and the impact of major contagions. The average total connectedness index (TCI) indicates that the connectedness is significant throughout the period and increases during the invasion. The findings imply that contagion effects trigger a potential alteration in the structure of the network integration level of the commodities, amplifying system-wide dynamic connectivity due to disurptions caused by oil and oil substitutes. The net plot depicts corn and soy as the net transmitters, with their magnitude increasing during the contagions. The pairwise connectedness index (PCI) revealed that corn-soy, corn‒wheat, and soy-wheat were the primary interactors, while oil became a significant interactor, particularly during the oil crash and the COVID-19 outbreak. Additionally, compared with other contagions, GFC had a potential asymmetric effect on the network. Positive returns dominate the interaction between the primary transmitter and receivers, whereas negative returns do not significantly dominate the total network. These investigations contribute to the literature on the food-fuel nexus in terms of asymmetries and the impact of contagions on the futures market. It also identified the optimal portfolio allocation based on the hedging effectiveness of three portfolio construction strategies.

The trend of digital finance: unveiling the multidimensional network of cryptocurrency risk propagation

ABSTRACT This study primarily explores the mechanisms of risk propagation among cryptocurrencies, unveiling for the first time the frequency dimension of risk propagation within the cryptocurrency market and identifying the role of oscillation frequency in this process. By employing Variational Mode Decomposition (VMD) and the DY spillover matrix to construct a complex network, the paper analyzes the frequency dimension risk propagation mechanisms of nine major cryptocurrencies from 2017 to 2023. Key findings include the significant risk propagation capabilities of Ethereum (ETH) and Bitcoin (BTC) during periods of high market volatility, while stablecoins such as Tether and USD Coin exhibit minimal risk propagation ability. Additionally, the characteristics of cryptocurrency risk propagation have been enhanced following the COVID-19 pandemic. Overall, the risk propagation of most cryptocurrencies is primarily realized through high-frequency oscillations. The robustness of the conclusions is verified using the Time-Varying Parameter Vector Autoregressive (TVP-VAR) model. The results are significant for understanding the dynamic characteristics of the cryptocurrency market, predicting future market risks, and formulating risk management strategies. Furthermore, the methodology and findings of this study provide new perspectives and tools for exploring the risk propagation relationships among cryptocurrencies.

The time-varying effects of geopolitical risk on mutual fund risk taking.

Based on a time-varying parameter vector autoregression model with stochastic volatility (TVP-VAR-SV), this paper investigates the dynamic effects of geopolitical risk on mutual fund risk taking in China across three-time horizons and at three selected time points. Overall, the impulse responses are time-varying and we find a negative effect of geopolitical risk on mutual fund risk taking until 2015, with the short-term effect being the most pronounced, suggesting that when professional investors such as mutual fund managers are faced with the stock valuation uncertainty due to a geopolitical shock, they choose to reduce market risk exposures. After 2015, the short-term effect begins to diminish and gradually turns positive, which could be explained by the fact that with the increasing abundance and diversification of investment instruments, fund managers have more effective investment tools and more sophisticated trading strategies to hedge against geopolitical risk, rather than reducing market risk exposure. Further, we explore the heterogeneous effects of eight types of geopolitical risk and three types of mutual fund. The results indicate that the effect of geopolitical actions is stronger than that of geopolitical threats, while the effect of narrow geopolitical risk is stronger than that of broad geopolitical risk. Moreover, we find that the response of the risk taking of growth funds to the geopolitical risk is weaker than that of balanced and income funds.

Partisan conflict, trade policy uncertainty, and the energy market

This paper investigates the time-varying effects of partisan conflict (PC) and trade policy uncertainty (TPU) on the energy market from January 2000 to July 2021 by employing a time-varying parameter vector autoregressive model with stochastic volatility. The results show that the pass-through effects of the PC and TPU shocks on energy return and volatility varied over time and are different across energy sub-sectors. PC and TPU had a greater impact on energy market return in the short-term, whereas the impact on energy market volatility was spread across relatively long periods. Generally, the impact of TPU on energy return and volatility was greater than that of PC at different lag periods and different points. Further, under different energy frameworks, the trend of the time-varying relationship between PC and TPU was roughly the same, indicating the robustness of our results.

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.

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.

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.