Time-varying Vector Research Articles

Motion planning and stabilization of nonholonomic systems using gradient flow approximations

Nonlinear control-affine systems described by ordinary differential equations with time-varying vector fields are considered in the paper. We propose a unified control design scheme with oscillating inputs for solving the trajectory tracking and stabilization problems under the bracket-generating condition. This methodology is based on the approximation of a gradient-like dynamics by trajectories of the designed closed-loop system. As an intermediate outcome, we characterize the asymptotic behavior of solutions of the considered class of nonlinear control systems with oscillating inputs under rather general assumptions on the generating potential function. These results are applied to examples of nonholonomic trajectory tracking and obstacle avoidance.

An investigation of the potential clinical utility of critical slowing down as an early warning sign for recurrence of depression

Background and objectivesMuch of the burden of depressive illness is due to relapses that occur after treatment into remission. Prediction of an individual's imminent depressive relapse could lead to just-in-time interventions to prevent relapse, reducing depression's substantial burden of disability, costs, and suicide risk. Increasingly strong relationships in the form of autocorrelations between depressive symptoms, a signal of a phenomenon described as critical slowing down (CSD), have been proposed as a means of predicting relapse. MethodsIn the current study, four participants in remission from depression, one of whom relapsed, responded to daily smartphone surveys with depression symptoms. We used p-technique factor analysis to identify depression factors from over 100 survey responses. We then tested for the presence of CSD using time-varying vector autoregression and detrended fluctuation analysis. ResultsWe found evidence that CSD provided an early warning sign for depression in the participant who relapsed, but we also detected false positive indications of CSD in participants who did not relapse. Results from time-varying vector autoregression and detrended fluctuation analysis were not in agreement. LimitationsLimitations include use of secondary data and a small number of participants with daily responding to a subset of depression symptoms. ConclusionsCSD provides a compelling framework for predicting depressive relapse and future research should focus on improving detection of early warning signs reliably. Improving early detection methods for depression is clinically significant, as it would allow for the development of just-in-time interventions.

Connectedness Among Geopolitical Risk, Inflation, Currency Values, and Exports by TVP-VAR Analysis: A Worldwide Perspective

Abstract The article discusses the spillover impacts between geopolitical uncertainty, inflation, exchange rates, and exports worldwide by employing the Time-Varying Parameter Vector Autoregressive (TVP-VAR) methodology. The net directional pairwise and net total directional causality among these variables is examined in major advanced economies (US, UK, Germany, France, Israel, South Korea and Japan) and rising economies (Russia, Türkiye, China, India, Brazil, and South Africa). Normal market conditions (since early 2000) and stressed ones since the latest crises (the Covid-19 disease and the Russia–Ukraine conflict) are covered. It is revealed that the national currency value constitutes the most influential determinant of exports and inflation but also in the system examined. Currency values exert direct impacts on inflation but also indirect, affecting exports and enhancing geopolitical risk as this is found to increase inflationary pressures. Intriguingly, this gives credence to the emergence of a new channel of inflation-creation that works through geopolitical risk. Such linkages are more pronounced in the US, South Korea, and Brazil while Germany and France present the weakest relations. Inflation, the currency value, and exports turned out to be very decisive regarding the geopolitical risk in Russia during the Russia–Ukraine war.

Retracted: Empirical Study on the Relationship between Agricultural Economic Structure Growth and Environmental Pollution Based on Time-Varying Parameter Vector Autoregressive Model.

[This retracts the article DOI: 10.1155/2022/5684178.].

Fast estimation of a large TVP-VAR model with score-driven volatilities

This paper proposes a fast approach to estimating a large time-varying parameter vector autoregressive (TVP-VAR) model. Based on a score-driven modeling framework, we first assume that the time-varying variances of random errors in each equation of the TVP-VAR are score-driven, and then propose filtering and smoothing procedures to estimate time-varying parameters and time-varying volatilities. We show that under the forgetting factors, the filtering estimation of time-varying parameters is equivalent to an equation-by-equation estimator, significantly reducing the dimension of state space and thus delivering fast estimation. Moreover, a fast smoothing estimation can be derived, avoiding the inverse of the super-high dimensional state equation covariance matrix. We provide dynamic model averaging (selection) and maximum likelihood estimates for forecasting and inference. Our simulation study shows that the proposed method is more accurate than the popular methods and enjoys tremendous computational gain from the equation-by-equation estimator. Finally, we conduct an empirical study on the dynamic connectedness of global stock markets, demonstrating the merits of our methods in real-time and ex-post analysis.

Deciphering the cryptocurrency conundrum: Investigating speculative characteristics and volatility

This study employs Time-Varying Parameter Vector Autoregression (TVP-VAR) to analyze spillover effects among ten financial instruments spanning 395 weeks, encompassing significant events such as the 2016/17 cryptocurrency surge, the COVID-19 pandemic, and the Ukraine conflict. The research highlights Bitcoin's influence on equities and commodities, influenced by evolving factors, regulatory uncertainties, and market sentiment. Equity indices both transmit and absorb shocks, while commodities intensify market volatility. Overall, Bitcoin notably impacts speculative assets such as equity indices, with a moderated influence on less speculative assets, emphasizing its pivotal role in dynamic financial markets subject to changing regulations.

Assessing the US financial sector post three bank collapses: Signals from fintech and financial sector ETFs

We investigate the effects of the collapses of Silicon Valley Bank, Signature Bank, and First Republic Bank on the US financial sector by analysing returns and second moments of traditional financial and fintech ETFs. Using a network model, we examine high-frequency data sampled at one-hour intervals for seventeen ETFs encompassing pre- and crisis periods. We find, using a time-varying parametric vector autoregressive (TVP-VAR) and volatility impulse response analysis, that traditional financial ETFs are net transmitters of returns and volatility spillovers in the network, and that this impact is more pronounced in volatility in the period coinciding with the collapse of the three big banks. We identify effects persisting through the medium term. This study is among the first to comprehensively analyze the recent crisis in the US banking sector, covering a full range of the fall of three big banks.

Precious metals and currency markets during the Russia-Ukraine conflict’s inflationary periods

The contemporary conflict between two countries Russia and Ukraine has caused intensification in the increasing prices of commodities and energy owing to the supply chain disruption and the sanctions imposed on both countries. The previous literature has validated that Russia-Ukraine conflict substantially affects financial and economic operations of other countries. Therefore, the key aim of conducting this study is to inspect the dynamic spillover connectedness between the four leading precious metals (such as Gold, Palladium, Platinum, and Silver) and seven major currency markets (including Euro, British Pound, Australian Dollar, Swiss Franc, Japanese Yen, Canadian Dollar, and Chinese Yuan). The study includes the dataset from 24 January 2022–31 May 2022 to cover the inflationary period. Accordingly, the Time-Varying Parameter Vector Autoregression framework is applied. Granger linear causality and BDS nonlinear causality techniques are also employed to examine the causal effect of geopolitical risk in aforementioned connectedness. The findings reveal that Platinum has a strong spillover towards all the considered currencies except the Japanese Yen. The increasing demand for Platinum during the conflict secured it to receive the spillover. While the Japanese Yen is a net transmitter of spillover indicating the Japanese Yen as sturdiest currency during the conflict. The causality test reveals that geopolitical risk causes spillover connectedness across the precious metals and the currency markets during inflationary period. This study is significant for policymakers, investors, and portfolio managers while developing strategies or making critical investment decisions.

Dynamic and asymmetric connectedness in the global “Carbon-Energy-Stock” system under shocks from exogenous events

In this paper, by using the time-varying parameter vector autoregression model (TVP-VAR) with the asymmetric connectedness indicator and network diagrams, we investigate the dynamic and asymmetric return connectedness in the global “Carbon-Energy-Stock” system, including carbon markets and stock markets of the three largest economies, namely the United States, European Union and China, and fossil energies of crude oil and natural gas under exogenous shocks. Our study shows that (i) the risk spillover level of the global system has significantly increased after the outbreak of two exogenous events, the COVID-19 pandemic and the Russo-Ukrainian war, and the global shock from the COVID-19 pandemic has more widespread and greater impact on the system than the geopolitical shock from the Russo-Ukrainian war, (ii) the global “Carbon-Energy-Stock” system is more sensitive to negative information on price returns than positive information, and the asymmetry of the connectedness is much larger when the system is active and in the presence of exogenous shocks, (iii) risks in the global “Carbon-Energy-Stock” system usually transformed from stock markets, especially the stock markets of the United States and European Union, to the carbon markets. These findings provide valuable guidance and have economic implications for both investors and policymakers worldwide.

Do clean and dirty cryptocurrencies connect with financial assets differently? The role of economic policy uncertainty

This paper analyzes time-varying networks of clean and dirty cryptocurrencies with green and traditional assets through a dynamic connectedness approach established by the time-varying parameter vector autoregressive (TVP-VAR) model. The underlying asymmetry of the dynamic pairwise connectedness when facing uncertainty shocks is further studied through a non-parametric quantile causality method. Our results demonstrate a limited information transmission of volatility from cryptocurrencies to both traditional and green assets, while the connection of clean cryptocurrencies (CI) with the financial system is even weaker compared to that of dirty cryptocurrencies (DI), especially after the COVID-19 pandemic. In contrast, connection within the financial system is found to be relatively closer. Moreover, causal relationships between economic policy uncertainty (EPU) and cryptocurrency-financial asset linkages are generally enhanced after the pandemic onset, while such the causality of uncertainty with DI related asset linkages tends to be even stronger. Most of the above causalities are shown to be negligible during market depression, further implying the sheltering role of the market linkages against uncertainty.

Extreme risk dependence and time-varying spillover between crude oil, commodity market and inflation in China

The fluctuation of general commodity prices, particularly crude oil prices, exerts a substantial influence on the economic development of all countries. Based on the mean and quantile spillover index of time-varying parameter vector autoregressive (TVP-VAR), we measure the spillover effects among crude oil, commodity market, and inflation in China under normal and extreme conditions from April 2018 to January 2023. The results show the interdependence among crude oil, commodity markets, and inflation; however, the degree of correlation varies at different quantiles. Notably, the spillover index is significantly higher at the extreme quantile, indicating that extreme risks can easily affect the spillover effect among crude oil, commodity markets, and inflation. Meanwhile, the tail risk spillover effect among crude oil, commodity markets, and inflation exhibits asymmetry, indicating that the spillover effect is greater during downward market conditions than upward ones. Furthermore, oil prices serve as a significant provider of systemic risk, both in normal market conditions and during times of extreme risk. In most cases, oil prices transfer more risks to inflation and commodity markets, commodity markets transfer more risks to inflation, and the paired spillover effects between commodity markets fluctuate over time. Finally, our results hold significant implications for investors, market regulators, and policymakers.

Higher-order moment risk spillovers and optimal portfolio strategies in global oil markets

Investigating the dynamic interdependencies and risk connectedness in global oil markets is paramount for investors and regulatory bodies. Nevertheless, prevailing research has predominantly focused on the examination of lower-order moments, thereby imposing limitations on the extent of inquiry. This paper undertakes an analysis of higher-order moment risk spillovers in global oil markets employing the innovative time-varying parameter vector autoregression (TVP-VAR) extended joint connectedness approach. Moreover, this study pioneers the construction of minimum connectedness portfolio strategies encompassing oil markets worldwide, incorporating the outcomes of higher-order moment risk connectedness into multivariate portfolio construction. Our empirical investigation unequivocally highlights the ascendance of OPEC oil as the preeminent net transmitter of connectedness across all moments, reinforcing its pivotal role in the transmission of risk. Furthermore, our findings disclose significant heterogeneity in the connectedness outcomes, with discernible variations across diverse moment orders. Remarkably, the oil benchmarks of WTI and Brent undergo a discernible transformation, transitioning into net recipients when subjected to higher-order moment risk spillovers. Additionally, the dynamics of equicorrelations, pairwise correlations, as well as the time-varying total, net, and net-pairwise connectedness indices, exhibit a high degree of responsiveness to major crisis events. Finally, the incorporation of higher-order moment risk spillovers into oil portfolio strategies manifests enhanced efficacy in risk management. The policy implications derived from our findings bear practical significance for an array of stakeholders, including cross-market investors, portfolio managers, regulatory entities, and policymakers.

Dynamic connectedness across energy and metal futures markets during the COVID-19 pandemic: New evidence from a time-varying spillover index

The COVID-19 outbreak has greatly increased economic uncertainty worldwide and significantly impacted the markets for energy and metal futures. In order to capture the dynamic connectedness between the energy and metal futures markets for the period of January 3, 2013, to June 30, 2022, and to validate the mechanism by which COVID-19 affects spillover effects, this study combines a time-varying parametric vector autoregressive model with spillover effects. It is clear that there is a clear spillover effect in the energy and metal futures markets, showing time-varying characteristics, with fluctuations in the range of 35%–65%. The findings demonstrate that there are considerable spillover effects in the energy and metal futures markets, which fluctuate between 35% and 65% over time and are subject to unexpected events. Especially, the spillover network and dynamic spillover results indicate that during the COVID-19 outbreak, the spillover effect between the energy futures market and the metal futures market significantly increased. The mechanism analysis results indicate that COVID-19 influences the spillover effects between the energy and metal futures markets mostly through uncertainty and financial channel. Further research found that compared to normal market conditions, bullish and bearish market conditions had a greater impact on spillovers in the energy and metal futures markets.

Fluctuations in gold prices in Vietnam during the COVID-19 pandemic: Insights from a time-varying parameter autoregression model

Vietnam ranks 37th in the world's ranking of national economies but has the largest gold consumption in Southeast Asia and the 7th largest gold consumption in the world. The price of gold in Vietnam often increases substantially in an abnormal way. Therefore, we employ a time-varying parameter vector autoregression approach based on extended joint connectedness to explain Vietnam's high gold price. We analyze the connectedness between five markets—the foreign exchange, the Vietnamese stock, gold, crude oil, and cryptocurrency markets—and the global gold traded in Vietnam from January 1, 2018, to August 1, 2021. Our results show heterogeneous connections at the system level. Vietnam's gold market appears to act as a net receiver of shocks, while other markets, such as cryptocurrencies, foreign exchanges, and stocks, act as net transmitters. One of the most critical transmitters among these channels is gold. The gold market in Vietnam has behaved differently over the course of the COVID-19 pandemic than it did in the past. The Vietnamese gold market appears to be a net receiver during uncertain periods, whereas it is a net transmitter in the normal course of events. Crude oil and world gold act as long-term transmitters of shock during the pandemic period. By contrast, cryptocurrency accounts for an insignificant portion of the fluctuations in Vietnam's gold market, and its role is limited to the short term.

Interconnectedness of Cryptocurrency Uncertainty Indices with Returns and Volatility in Financial Assets during COVID-19

This paper investigates the dynamic relationship between cryptocurrency uncertainty indices and the movements in returns and volatility across spectrum of financial assets, comprising cryptocurrencies, precious metals, green bonds, and soft commodities. It employs a Time-Varying Parameter Vector Autoregressive (TVP-VAR) connectedness approach; the analysis covers both the entire sample period spanning August 2015 to 31 December 2021 and the distinct phase of COVID-19 pandemic. The findings of the study reveal the interconnectedness of returns within these asset classes during the COVID-19 pandemic. In this context, cryptocurrency uncertainty indices emerge as influential transmitters of shocks to other financial asset categories and it significantly escalates throughout the crisis period. Additionally, the outcomes of the study imply that during times of heightened uncertainty, exemplified by events such as the COVID-19 pandemic, the feasibility of portfolio diversification for investors might be constrained. Consequently, the amplified linkages between financial assets through both forward and backward connections could potentially compromise financial stability. This research sheds light on the impact of cryptocurrency uncertainty on the broader financial market, particularly during periods of crisis. The findings have implications for investors and policymakers, emphasizing the need for a comprehensive understanding of the interconnectedness of financial assets and the potential risks associated with increased interdependence. By recognizing these dynamics, stakeholders can make informed decisions to enhance financial stability and manage portfolio risk effectively.

Dynamic volatility connectedness in the European electricity market

Since the 1990s, one of the energy policy objectives of the European Union (EU) has been to create a single, integrated European electricity market. This study uses a broad set of European electricity markets to answer the question whether volatility connectedness is higher in geographically closer markets or whether all markets are integrated. This question is particularly relevant now, at a time of high volatility of energy commodity prices, which can affect energy security in Europe. Our empirical analysis examines volatility connectedness in 26 European electricity markets between 1 August 2007 and 23 February 2022 using the time-varying parameter vector autoregressive model (TVP-VAR) based on the extended joint connectedness method (Balcilar et al., 2021). Furthermore, the impact of volatility of determinants of electricity prices on volatility connectedness in European electricity markets since 2016 is assessed.The results reveal that volatility connectedness between markets, i.e., the integration of European electricity markets, increases. Connectedness within geographically closer markets is observed mainly in the initial periods. However, since 2016, regional connections have increased, which is accompanied by interregional connections. This is particularly evident in the recent period –characterised by high volatility of electricity and energy commodity prices. However, when the impact of volatility of electricity price determinants on volatility connectedness is removed, especially since 2021, the results are slightly altered and indicate lower volatility connectedness in the European electricity markets. The role of the main net transmitters or net receivers does not change, while the role of the other markets changes or strengthens/weakens. Inter-regional connectedness is not as strong, which means that volatility connectedness occurs mainly in the markets from the same region.

Do climate policy uncertainty and investor sentiment drive the dynamic spillovers among green finance markets?

Green finance is an essential instrument for improving the environment and addressing climate change. This study investigates the dynamic spillovers among green finance markets using time-varying parameter vector autoregression (TVP-VAR) spillover indices, and further investigates the impact of climate policy uncertainty and investor sentiment on spillovers based on the generalised autoregressive conditional heteroscedasticity mixed data sampling (GARCH-MIDAS) model. The results indicate that: (i) environmental, social and governance (ESG), clean energy and water markets are information transmitters in the green finance system, whereas green building, green transportation, green bond and carbon markets are mainly information receivers; (ii) green stock markets including clean energy, non-energy and ESG markets transmit and receive greater information in the green finance system, while green bond and carbon markets do less; (iii) the green bond market is more interconnected with other green finance markets after the COVID-19 outbreak; (iv) investor sentiment contributes more to the net total directional spillovers of green resource markets (water and clean energy), while climate policy uncertainty contributes more to total spillovers and the net total directional spillovers of other green finance markets. These findings offer invaluable guidance for both policymakers and environmental investors.

Global geopolitical risk and volatility connectedness among China's sectoral stock markets

Employing the time-varying parameter vector autoregressive (TVP-VAR) connectedness index and nonparametric causality-in-quantiles test, this paper investigates the causal effect of global geopolitical risk on the dynamic volatility connectedness within China's sectoral stock markets. The TVP-VAR connectedness index results reveal significant volatility connectedness among China's stock market sectors, and the industrial, consumer discretionary, and raw material sectors play a critical systemic role throughout the sample period. The Granger causality test results show that the causality-in-quantiles test demonstrates superior performance compared to the linear Granger causality test, and it is evident that global geopolitical risk exhibits significant nonlinear causality effects on both the overall volatility connectedness among sectors and the net volatility connectedness across different sectors.

Connectedness between monetary policy uncertainty and sectoral stock market returns: Evidence from asymmetric TVP-VAR approach

In this study, we have examined the asymmetric dissemination of spillover returns among monetary policy uncertainty and returns of sectoral stocks of different sectors. The essential contribution of this study is the analysis of deviating levels of spillover returns among uncertainty of monetary policy and stocks returns for diverse segments. Additionally, we have applied an advance technique named “TVP-VAR” for data analysis to measure dynamic connectedness and execute dynamic portfolio exercise via different hedging methods in order to apprehend asymmetry; as this technique is based on a time-varying vector autoregressive approach. The dataset used in this study employs a sample of more than 30 years of daily observations obtained from October 9, 1989 to October 10, 2022 to closely examine the connection among the series. The sample covers most of the financial and economic catastrophes to study the connection among Monetary Policy Uncertainty-MPU and returns of sectoral stocks. The findings highlight that negative connectedness dominates the whole duration; thus, signifying that financiers and investors react more intensely to negative information or news. The analysis further points that the Minimum-Connected Portfolio (MCP) and Minimum-Correlation Portfolio (MPC) techniques are efficacious in apprehending asymmetry and deliver significant understanding for portfolio management. Moreover, the results imply that these approaches can assist financiers and investors to attain effective and efficient financing. Lastly, the findings of this analysis offer valuable implications for financiers and policymakers for portfolio management and observing financial stability.

Interoperability of the revolutionary blockchain architectures and Islamic and conventional technology markets: Case of Metaverse, HPB, and Bloknet

This study examines how Islamic and conventional technology stock indices interact with blockchain technology assets including Metaverse, High-Performance Blockchain, and Blocknet, throughout different market conditions and horizons. A three-pronged approach is employed, namely the quantile cross-spectral coherency approach, the time-varying parameter vector autoregressions (TVP-VAR), and the Causality in quantiles methodology. The quantile coherency results show that connectivity between new generation blockchains and conventional and Islamic markets varies. It was found that the connectedness of the network of variables rises in the medium run and reaches its peak in the long run as a result of the TVP-VAR approach. It is evident from the results that the application of sharia screening only impacts the relationship between the technology stock index and digital assets significantly, but does not affect the connectedness and causality between the two. Technology managers, policymakers, and blockchain designers are able to gain new insights from these research findings.