Time-varying VAR Research Articles

WITHDRAWN: the Impact of Monetary Policy on the Liquidity of Bond Market——Based on Chinese Local Government Bond Market

The bond market serves dual roles in fiscal and financial spheres, playing a crucial role in coordinating monetary policy. This paper investigates the impact of quantitative and price-based monetary policies on the liquidity level of China’s bond market. A comprehensive index measuring the liquidity of the local bond market is constructed using a combination weighting method that integrates the entropy method and the coefficient of variation. Employing the time-varying stochastic volatility structure vector autoregression (TVP-SV-SVAR) model on data spanning from 2013 to 2021, the study empirically compares the impulse response of local bond market liquidity to monetary policy shocks. Findings reveal that both types of monetary policy operations exhibit asymmetric, nonlinear, and time-varying impacts on bond market liquidity. Quantitative monetary instruments induce deeper impulse responses, with longer-lasting effects. These conclusions offer insights for monetary policy reforms and bond market development in China.

Good vs. bad volatility in major cryptocurrencies: The dichotomy and drivers of connectedness

Cryptocurrencies exhibit unique statistical and dynamic properties compared to those of traditional financial assets, making the study of their volatility crucial for portfolio managers and traders. We investigate the volatility connectedness dynamics of a representative set of eight major crypto assets. Methodologically, we decompose the measured volatility into positive and negative components and employ the time-varying parameters vector autoregression (TVP-VAR) framework to show distinct dynamics associated with market booms and downturns. Our findings indicate that crypto connectedness reflects important events and oscillates substantially while reaching lower limit values when compared to traditional financial markets. Periods of extremely high or low connectedness are clearly linked to specific events in the crypto market and macroeconomic or monetary history. Furthermore, existing asymmetry from good and bad volatility indicates that market downturns spill over substantially faster than comparable market surges. Overall, the connectedness dynamics are driven by a combination of both crypto (momentum, on-chain activity, off-chain activity) and legacy financial and economic (financial and economic uncertainty, and financial market performance) factors, while the asymmetry is more connected to the off-chain crypto activity and the combination of economic, financial, and monetary factors. In both the total connectedness and asymmetry modeling, these can serve as hands-on indicators to be further translated into specific portfolio re-balancing decisions, risk management, and regulatory frameworks.

Can stronger governance and institutional quality drive growth through inward foreign direct investments in BRICS nations?

Background: This research focusses on the significance of institutional quality (INSQ) and governance in shaping the relationship between foreign direct investments (FDI) and growth. It specifically examines the BRICS nations (Brazil, Russia, India, China and South Africa) because of their economic significance in the global economy.Aim: This study aims to analyse the impact of INSQ and governance on sustainable growth, with a particular focus on its effects through the channels of FDI in the BRICS countries.Setting: Annual panel data from the Organisation for Economic Cooperation and Development (OECD) statistics, the United Nations Conference on Trade and Development (UNCTAD) statistics and the World Bank indicators spanning two decades (2000–2022) are used to analyse BRICS nations.Methods: The research study employed the Bayesian time-varying coefficient vector autoregression (BTVC-VAR) model to achieve the objective of the study.Results: The findings indicate that there is no long-term relationship between FDI, INSQ and economic growth in the BRICS countries. However, there is a noticeable co-movement among these variables in the short run.Conclusion: Given the obtained results, the policymakers should prioritise efforts to strengthen institutional capacity in the short term while focussing on the Sustainable Development Goals (SDG-8 and SDG-16).Contribution: The existing studies have assumed static economic, social and political conditions, potentially failing to accurately depict the complexities of an actual economy. This study offers methodological innovation by employing Bayesian time-varying coefficient vector autoregression (BTVC-VAR), enabling coefficients to adapt to evolving economic conditions over time. This effectively captures the dynamic nature of variables and provides reliable estimates.

Connectedness and frequency connection among green bond, cryptocurrency and green energy-related metals around the COVID-19 outbreak

We investigate the return interdependence among green bonds, cryptocurrency indices and green energy-related metals. We apply time-varying parametric vector autoregression (TVP-VAR) conenctedness, wavelet coherence, Wavelet Quantile Correlation （WQC) and Quantile on Quantile (QQR) Connectedness Methods. Our empirical findings show that return connectedness has become even stronger after the outbreak of COVID-19, with both green bonds and cryptocurrency indices acting as net receivers of return spillovers. Surprisingly, Copper functioned as a net sender of return spillovers over the entire observation period. Findings revealed that the cryptocurrency index exhibited a consistent positive correlation with the green energy-related metals market at medium to short-term frequencies, whereas green bonds showed a negative correlation with metals market at short-term frequencies and a positive correlation at long-term frequencies.

Impact of Globalization on Macroeconomic Dynamics Using a Time-varying Bayesian VAR

Impact of Globalization on Macroeconomic Dynamics Using a Time-varying Bayesian VAR

A novel method to select time-varying multivariate time series models for the surveillance of infectious diseases

BackgroundDescribing the transmission dynamics of infectious diseases across different regions is crucial for effective disease surveillance. The multivariate time series (MTS) model has been widely adopted for constructing cross-regional infectious disease transmission networks due to its strengths in interpretability and predictive performance. Nevertheless, the assumption of constant parameters frequently disregards the dynamic shifts in disease transmission rates, thereby compromising the accuracy of early warnings. This study investigated the applicability of time-varying MTS models in multi-regional infectious disease monitoring and explored strategies for model selection.MethodsThis study focused on two prominent time-varying MTS models: the time-varying parameter-stochastic volatility-vector autoregression (TVP-SV-VAR) model and the time-varying VAR model using the generalized additive framework (tvvarGAM), and intended to explore and verify their applicable conditions for the surveillance of infectious diseases. For the first time, this study proposed the time delay coefficient and spatial sparsity indicators for model selection. These indicators quantify the temporal lags and spatial distribution of infectious disease data, respectively. Simulation study adopted from real-world infectious disease surveillance was carried out to compare model performances under various scenarios of spatio-temporal variation as well as random volatility. Meanwhile, we illustrated how the modelling process could help the surveillance of infectious diseases with an application to the influenza-like case in Sichuan Province, China.ResultsWhen the spatio-temporal variation was small (time delay coefficient: 0.1–0.2, spatial sparsity:0.1–0.3), the TVP-SV-VAR model was superior with smaller fitting residuals and standard errors of parameter estimation than those of the tvvarGAM model. In contrast, the tvvarGAM model was preferable when the spatio-temporal variation increased (time delay coefficient: 0.2–0.3, spatial sparsity: 0.6–0.9).ConclusionThis study emphasized the importance of considering spatio-temporal variations when selecting appropriate models for infectious disease surveillance. By incorporating our novel indicators—the time delay coefficient and spatial sparsity—into the model selection process, the study could enhance the accuracy and effectiveness of infectious disease monitoring efforts. This approach was not only valuable in the context of this study, but also has broader implications for improving time-varying MTS analyses in various applications.

Tracking trend output using expectations data

Abstract This article proposes a new approach to measuring trend output that exploits survey data on expectations to distinguish the effects of permanent and transitory shocks and to track the time-variation in the processes underlying the determination of output. The approach is illustrated using measures of output expectations and output uncertainties based on a business survey conducted for UK manufacturing. The measures are employed in a time-varying vector autoregression (VAR) to track trend output and to provide a compelling characterization of the output fluctuations in UK manufacturing over the last 20 years.

Extant linkages between Shanghai crude oil and US energy futures: Insights from spillovers of higher-order moments

This study is epicentral to analyzing the impact of futures volatility on portfolio and risk management, as extant literature indicates the challenges of using economic variables that fall short of forecasting volatility beyond lagged values. Further, higher moments may be better adaptive to signaling distress during market upheavals. This paper sources data from Bloomberg from March 26, 2018–April 28, 2023, to examine the dynamic spillovers of higher moments among Shanghai International Energy Exchange and US energy futures contracts by constructing realized skewness and kurtosis. Using nonlinear techniques of mutual information and time-varying vector autoregression (TVP-VAR), we show that realized skewness and kurtosis offer significant information on spillover transmission between the two futures markets, primarily through the crises of COVID-19 and the Russia and Ukraine war. Further, we identify that the risks embedded in these future contracts have increased significantly. Our results have important implications for policymakers, investors, and risk managers.

International macroeconomic vulnerability

Small open economies are known to be impacted by shocks to larger economies. This phenomenon is known as macroeconomic vulnerability. We propose and implement a novel index of macroeconomic vulnerability to foreign shocks for a given pair of a large economy and a small open economy. It uses a structural time-varying Bayesian VAR with a block-exogeneity hypothesis. The index is based on the sum of the responses of the small open economy to shocks in the large economy over time, thus allowing us to disentangle and measure the source of the shock, the impact variables, and the duration of impact. We highlight two results out of the many that our index unveils. First, we do not find a difference between the international impact of U.S. shocks during periods of crises versus stability. Second, we find that there is a growing decouple between emerging markets (EM) and developed markets (DM) on how their domestic inflation is affected by U.S. output shocks. Our approach can also be used to elucidate previously unknown transmission channels or unmeasured theoretical mechanisms. Finally, using a sample of developed and developing countries, we find that global banks do not increase the macroeconomic vulnerability of a country.

Trade Openness, Financial Development and Economic Growth in Lesotho: BVAR and Time-varying VAR Analysis

This research paper explores the dynamic relationship between trade openness, financial development, and economic growth in Lesotho. The study utilizes quarterly time series data spanning from 1970 to 2021, obtained from the Central Bank of Lesotho. This paper makes a significant contribution to the methodological discourse by utilizing a time-varying VAR approach, in addition to the Bayesian VAR estimation approach. The empirical results indicate that there is no cointegration relationship between economic growth, financial development, and trade openness. However, the evidence shows a unidirectional causality from economic growth to trade openness in Lesotho. The Bayesian VAR and time-varying VAR estimations reveal that the variables dynamically react and adjust to shocks among the variables. The study recommends that to stimulate the opening up of the economy to the world, growth-enhancing policies such as the provision of world-class infrastructure facilities, improved healthcare facilities, and a conducive macroeconomic environment should be implemented, considering the insufficient international demand in Lesotho.

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.

An Analysis of Travel-Tourism Stock Returns in the Wake of the COVID-19 Pandemic: Evidence From the Five Most Visited Countries

This article examines the effects of the COVID-19 pandemic on the travel-tourism stock markets. Specifically, it considers the five most visited countries: France, Spain, the US, China, and Italy. Unlike previous studies, it estimates time-varying VAR (TVP-VAR) models, including daily observations of confirmed COVID-19 cases, economic activity, CDS spreads, and the returns of travel???tourism sectors. In brief, our findings indicate that the effects of COVID-19 vary across countries, as well as over time. Specifically, increasing numbers of cases initially had a negative and significant impact on the travel-tourism stock returns of all countries. However, this effect had become insignificant by early April 2020. The travel-tourism markets of the European countries were seen to be more heavily affected by COVID-19 when compared to China and the US, with China seeming to have been the least affected country of all. Overall, our results are essential in understanding the impact of the COVID-19 pandemic on the travel???tourism stock markets, and are of particular importance to policymakers, portfolio managers, and investors.

MSCI全球指数与韩国股指波动对韩国贸易的影响

This study aims to analyse the impact of the stock market on Korea’s import and export trade, mainly through the MSCI Global and KOSPI indices as stock market proxies, and Korea’s total imports, total exports, import price indices, economic sentiment indices, and business surveys indices as proxies for import and export trade. Using a time-varying vector autoregression (TVP-VAR) model, it was found that the MSCI Global and KOSPI indices have a short-term positive effect on Korea’s total imports and total exports, but the KOSPI has a negative effect on total exports. For price indices, the MSCI Global Index has a positive effect on the import price index, while the KOSPI has a negative effect at a 1-month lag. For the export price index, the MSCI Global Index has a negative impact at 1-month lag but a positive impact at 3-month lag. For the Economic Sentiment Index, both indices have a negative impact at the 1-month lag. For the business survey index, both have a positive impact. In addition, the study analyses data from three different points in time: the US subprime crisis in 2008, the European credit crisis in 2015 and the New Crown epidemic in 2020. The results show that in all three financial events, the MSCI Global Index had a positive impact on Korea’s total exports and imports in the short term, while the KOSPI had a positive impact on total imports and a negative impact on total exports. For the import price index, the MSCI Global Index has a short-term positive impact while the KOSPI has a negative impact. For the export price index, both indices have a negative impact during financial events. For the economic sentiment index, the MSCI Global Index has a negative impact in the first lag and turns to a positive impact in the second lag, while the opposite is true for the KOSPI. Finally, in all three financial events, both indices had a short-term positive impact on the business survey index.

Exploring the asymmetric influence of economic policy uncertainty on the nonlinear relationship between exchange rate and carbon prices in China

Our objective is to investigate the impact of economic policy uncertainty (EPU) on the risk spillover effect between exchange rates and carbon prices. First, we employ the time-varying parametric vector autoregression (TVP-VAR) method to assess the dynamic nonlinear risk spillover effect between four exchange rates and China's carbon price for the period from September 2, 2013, to April 29, 2022. Our findings reveal a significant time-varying spillover effect between exchange rates and carbon prices. Among them, carbon prices and the EUR/CNY exchange rate predominantly act as risk contributors, and a pronounced correlation is observed among various exchange rates. Furthermore, the magnitude of risk spillover and interdependence intensifies significantly in response to major events. Second, we employ quantile-to-quantile regression (QQR) to examine the impact of EPU on risk spillover. Our results indicate that the influence of EPU on risk spillover exhibits is characterized by marked asymmetry and heterogeneity. At high levels of EPU, EPU has a substantial positive impact on risk spillover. However, when the risk spillover is already elevated, the effect of EPU tends to diminish. Based on these findings, we propose several policy recommendations, including prudent treatment of exchange rate adjustments, fostering stability in carbon prices, and the enactment of robust economic policies.

How do psychological and physiological performance determinants interact within individual athletes? An analytical network approach

ABSTRACT Previous research on sports performance has mostly been conducted: (a) at a single point, or at most, a few points in time, (b) at the group level, and (c) as a causal chain of monodisciplinary predictor and outcome variables. In the present research, we argue and demonstrate that the next important step should be to monitor, analyze, and visualise the dynamic and individual-specific interactions of multidisciplinary determinants of sports performance. Hence, we apply a recently developed analytical approach, that is, (Time-Varying) Vector-AutoRegressive ((TV)-VAR) modelling, which captures the intra-individual interactions and changes of multidisciplinary determinants. We first measured critical psychological (e.g., self-efficacy) and physiological (e.g., heart rate) factors among youth male football (soccer) players at a professional club, on a daily basis across one season. Next, we assessed the temporal dynamics of the factors with (TV-)VAR models and visualised the findings in network graphs. We present the results of two show-cases that demonstrate how multidisciplinary key determinants of sports performance can dynamically evolve across a sports season, that is, interact and change in individual-specific ways over time. Specifically, the results of Player 1 revealed a stable network across the season in which self-efficacy was the strongest predictor of other determinants, whereas this was not the case for Player 2. These new insights improve our understanding of how key determinants of sports performance are dynamically related within individual athletes and may allow practitioners to develop and implement person-specific, targeted, and timely interventions.

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.

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.

Volatility interconnectedness among financial and geopolitical markets: Evidence from COVID-19 and Ukraine-Russia crises

This study investigates the interconnectedness of volatility and spillovers among various financial and geopolitical markets during the COVID-19 pandemic and the Ukraine-Russia crises. Utilizing a Time-Varying Parameter Frequency Vector Autoregression (TVP-VAR) approach, we analyze daily data spanning from January 2015 to June 2022. Our empirical findings reveal substantial interconnectedness and spillovers among geopolitical risk (GPR), oil & gas, gold, conventional stock, Islamic stocks, and sukuk markets, particularly heightened during the crises periods. Interestingly, we observe a lower level of risk spillover during the Ukraine-Russia conflict compared to the COVID-19 pandemic. Short-frequency risk spillovers dominate over long-frequency ones. Moreover, our results indicate that conventional equity, Islamic equity, oil, and gas sectors predominantly act as net-risk transmitters, while sukuk, gold, and GPR serve as net-risk receivers across both short- and long-term frequencies. Specifically, during the COVID-19 crisis, conventional equity, Islamic equity, oil, and gas sectors emerge as net-risk transmitters, while sukuk, gold, and GPR act as net informational receivers in total and long-term frequencies. Conversely, during the Ukraine-Russia conflict, the oil and gas sector, sukuk, gold, and GPR assume net-risk receiver roles, with conventional equity and Islamic equity acting as net transmitters in total and short-term spillovers. Notably, oil & gas, gold, conventional stocks, Islamic stocks, and sukuk markets exhibit lower sensitivity to geopolitical risk during both crises, suggesting their potential utility in hedging against such risk. These findings offer valuable insights for investment risk management strategies.

Green finance under stress: Unraveling the spillover effects of tail risk

The study was undertaken in direct response to escalating concerns regarding the environmental implications of green assets, with the primary objective of determining the potential of green financial markets to serve as a mitigating force against the risks associated with green stocks, bonds, cryptocurrencies, and carbon markets. To evaluate the risk, the study employed tail risk at the 5% value-at-risk level using the conditional autoregressive value-at-risk (CAViaR) and time-varying parameters vector auto regressions (TVP-VAR) techniques. From the analysis it was found that there was a strong intra-class connectedness between the assets but limited interconnectedness between the markets. Furthermore, we conducted an analysis of dynamic NET spillovers to discern the markets responsible for transmitting and receiving risk spillovers across diverse markets, with the aim of comprehending their behavior both before and after the onset of the COVID-19 pandemic. It was also found that extreme risk spillovers were observed during the study period. These findings have important implications for policymakers, regulators, investors, and other financial market participants. Green assets are designed to combat climate risk by promoting sustainable energy practices and reducing carbon emissions and to maintain the stability of the financial markets under both extreme and typical market conditions.

Tracing the dynamic impact of energy transitions on equity market volatility in an era of financial turbulence

This study investigates the dynamic relationship between the shift towards clean energy and stock market fluctuations, covering the period from January 2004 to August 2021 by applying time-varying parameters vector autoregressions (TVP-VAR). The results reported strong spillovers between clean energy markets and stocks, while dirty ones demonstrated disconnected spillovers. Asymmetry in the spillovers detected dominance of negative (bad) spillovers due to various uncertain economic and financial circumstances. The most significant negative (bad) events tracked in time-varying attributes are the global financial crisis, European Debt Crisis, Shale Oil Crisis, Brexit, US interest rate agenda, and the COVID-19 pandemic. Meanwhile, negative asymmetries prevailed in time-varying analysis, echoing the pre-eminence of negative crisis periods. We identified clean energy markets and stocks as net spillover emitters and dirty energy markets as net recipients. These findings have profound implications for a range of stakeholders, including investors, policymakers, and market participants, offering valuable insights for portfolio diversification and risk management strategies amidst the ongoing energy paradigm shift.