Time-varying Autoregressive Model Research Articles

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.

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.

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 Multivariate Autoregressive Index model

Many economic variables are characterized by changes in their conditional mean and volatility, and time-varying Vector Autoregressive Models are often used to handle such complexity. Unfortunately, as the number of series grows, they present increasing estimation and interpretation issues. This paper tries to address this problem by proposing a Multivariate Autoregressive Index model that features time-varying mean and volatility. Technically, we develop a new estimation methodology that mixes switching algorithms with the forgetting factors strategy of Koop and Korobilis (2012). This substantially reduces the computational burden and allows one to select or weigh the number of common components, and other data features, in real-time without additional computational costs. Using US macroeconomic data, we provide a forecast exercise that shows the feasibility and usefulness of this model.

ESG practices mitigating geopolitical risks: Implications for sustainable environmental management

As climate change and geopolitical conflicts intensify, understanding how geopolitical risks affect companies prioritizing Environmental, Social, and Governance (ESG) practices is crucial. This study investigates the dynamic relationship between global geopolitical risks and the performance of Environmental, Social, and Governance (ESG) and non-ESG companies, particularly their influence on green markets. Utilizing a robust methodological framework, including the dynamic time-varying parameters vector autoregression (TVP-VAR) model, and causal impact modeling, we analyze daily financial data from 2021 to 2024. The results reveal a substantial negative impact of geopolitical risks on non-ESG companies, contrasting with the resilience of ESG-committed counterparts. This suggests that ESG-committed companies demonstrate better resilience against geopolitical risks, emphasizing the protective role of ESG practices amid uncertainties. Additionally, the inclusion of ESG companies in green markets diminishes the severity of the negative impact of geopolitical risks, underlining the transformative role of ESG commitment in shaping investor behavior towards sustainable investments. Our findings offer insights for policymakers and investors navigating geopolitical risks and ESG performance, with a focus on environmental management, and provide guidance for effective risk mitigation and investment policies to enhance environmental sustainability.

Breakdown of effective information flow in disorders of consciousness: Insights from TMS-EEG

BackgroundThe complexity of the neurophysiological mechanisms underlying human consciousness is widely acknowledged, with information processing and flow originating in cortex conceived as a core mechanism of consciousness emergence. Combination of transcranial magnetic stimulation and electroencephalography (TMS-EEG) is considered as a promising technique to understand the effective information flow associated with consciousness. ObjectivesTo investigate information flow with TMS-EEG and its relationship to different consciousness states. MethodsWe applied an effective information flow analysis by combining time-varying multivariate adaptive autoregressive model and adaptive directed transfer function on TMS-EEG data of frontal, motor and parietal cortex in patients with disorder of consciousness (DOC), including 14 vegetative state/unresponsive wakefulness syndrome (VS/UWS) patients, 21 minimally conscious state (MCS) patients, and 22 healthy subjects. ResultsTMS in DOC patients, particularly VS/UWS, induced a significantly weaker effective information flow compared to healthy subjects. The bidirectional directed information flow was lost in DOC patients with TMS of frontal, motor and parietal cortex. The interactive ROI rate of the information flow network induced by TMS of frontal and parietal cortex was significantly lower in VS/UWS than in MCS. The interactive ROI rate correlated with DOC clinical scales. ConclusionsTMS-EEG revealed a physiologically relevant correlation between TMS-induced information flow and levels of consciousness. This suggests that breakdown of effective cortical information flow serves as a viable marker of human consciousness. SignificanceFindings offer a unique perspective on the relevance of information flow in DOC, thus providing a novel way of understanding the physiological basis of human consciousness.

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

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

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

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

Volatility Connectedness of MOVE Index and Bond Returns

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

What time-varying network models based on functional analysis tell us about the course of a patient's problem

Background: Relations among psychological variables are assumed to be complex and to vary over time. Personalized networks can model multivariate complex interactions. The development of time-varying networks allows to model the variation of parameters over time. Objectives: We aimed to determine the value of time-varying networks for clinical practice. Methods: We applied time-varying mixed graphical models (TV-MGM) and time-varying vector autoregressive models (TV-VAR) to intensive longitudinal data of nine participants with depressive symptoms (n = 6) or anxiety (n = 3). Results: Most of the participants showed temporal changes in network topology within the assessment period of 30 days. Time-varying networks of participants with small, medium, and large time variability in edge parameters clearly show the different temporal evolvements of dynamic interactions between variables. The case example indicates clinical utility but also limitations to the application of time-varying networks in clinical practice. Conclusion: Time-varying network models provide a data-driven and exploratory approach that could complement current diagnostic standards by reflecting interacting, often mutually reinforcing processes of mental health problems and by accounting for variation over time. They can be used to generate hypotheses for further confirmatory and clinical testing.

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

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

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

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

Insights into the dynamics of market efficiency spillover of financial assets in different equity markets

This paper provides multidisciplinary insights into the dynamics of market efficiency spillover of financial assets, considering Bitcoin, DXY (U.S. Dollar Index), S&P 500, and crude oil in different equity markets. Given this, we employ the multifractal detrended fluctuation analysis (MF-DFA) and the time-varying parameter vector autoregressive models (TVP-VAR) to illuminate these complex interplays between these assets and other equity markets. Our findings display that Bitcoin acts as a net transmitter, whereas DXY functions as a net receiver. Additionally, Bitcoin, DXY, and S&P 500 clearly exhibit dynamics in information transmission, whereas crude oil does not. It means that the low connectedness of the oil market in terms of market efficiency spillover, suggesting that the oil market may play an effective role as a hedging instrument for other assets.

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

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

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

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

Variation in emotion dynamics over time is associated with future relationship outcomes.

Romantic relationships are defined by emotion dynamics, or how the emotions of one partner at a single timepoint can affect their own emotions and the emotions of their partner at the next timepoint. Previous research has shown that the level of these emotion dynamics plays a role in determining the state and quality of the relationship. However, this research has not examined whether the estimated emotion dynamics change over time, and how the change in these dynamics might relate to relationship outcomes, despite changes in dynamics being likely to occur. We examined whether the magnitude of variation in emotion dynamics over time was associated with relationship outcomes in a sample of 148 couples. Time-varying vector autoregressive models were used to estimate the emotion dynamics for each couple, and the average and standard deviation of the dynamics over time was related to relationship quality and relationship dissolution 1-2 years later. Our results demonstrate that certain autoregressive and cross-lagged parameters do show significant variation over time, and that this variation is associated with relationship outcomes. Overall, this study demonstrates the importance of accounting for change in emotion dynamics over time, and the relevance of this change to the prediction of future outcomes.

Dynamic connectedness of inflation around the world: A time-varying approach from G7 and E7 countries

Using advanced econometric techniques of the Time-Varying Parameters Vector Autoregression model with stochastic volatility (TVP-SV-VAR) and the Quantile Vector Autoregression (QVAR) model, this research investigates the dynamics of the Consumer Price Index (CPI) across G7 and E7 countries from January 1993 to January 2023, aiming to comprehensively analyze the international transmission of inflation. The study yields several key findings. Firstly, we ascertain that approximately 64% of inflation volatility can be attributed to international transmission. Secondly, our analysis consistently identifies Turkey and Russia as net exporters of inflation over the study period, while G7 countries and Mexico emerge as persistent net importers. Thirdly, we note a gradual increase in China's inflation connectivity with developing countries following the Asian financial crisis, coupled with a diminishing impact of Chinese inflation transmission to developed nations. Fourthly, we observe a heightened complexity in the inflation transmission network during periods marked by high Total Connectivity Index (TCI) values, compared to those with low TCI values. Lastly, our study underscores that the transmission of international inflation intensifies during periods characterized by extremely high or low international inflation rates.

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

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

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

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

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

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