Time-varying VAR Research Articles

Transmission pathways between foreign aid and renewable energy consumption in Vietnam

Our paper is the first attempt to analyze the environmental consequences and energy security of foreign aid in Vietnam during the 1987–2019 period. We apply various modern econometric techniques, including the multivariate Wavelet model and an extended time-varying vector autoregression (TVP-VAR). By using the multivariate wavelet model, we find that there are cycles of period 3–4 years in the 1988–1992 sample and 18–25 years in the 1985–2001 and 1989–2009 sample for the nexus between foreign aid growth and nonrenewable, renewable energy consumption. The region (from 1989 to 1992) implies that changes in foreign aid adversely led to changes in renewable energy consumption. In those regions, the phase (1993–2005) suggests the anti-phase relation with renewable consumption leading. The phase (2006–2011) implies that changes in foreign aid adversely led to changes in renewable energy consumption. By using an extended TVP-VAR, we reveal that nearly 61% of error variance within the system stems from idiosyncratic effects. We demonstrate that there are changes in the roles of foreign aid over time. Foreign aid plays a role as a net transmitter of shocks from 1988 to 2003 and net transmitter of shocks from 1988 to 2003 and transforms into a net receiver of shocks from 2004 to 2019. In 1989, foreign aid also transmits the greatest shocks to the use of renewable energy. Furthermore, from 1988 to 1995, nonrenewable energy shocks affect the inflows of foreign aid, which then acts as a shock transmitter of these shock to the economy since 1995.

Transmission of risks between energy and agricultural commodities: Frequency time-varying VAR, asymmetry and portfolio management

This paper examines energy and agricultural commodities' short-run and long-run connectedness by using the Time-varying parameter vector autoregressions (TVP-VAR). It applies the frequency version of the TVP-VAR model, which is a modified version of the dynamic TVP-VAR model. The frequency decomposition definition also decomposes into short-run and long-run connectedness. We further the analysis by investigating the effect of asymmetry in returns on connectedness. It also examines how portfolio management strategies would lead to a maximization of profits with minimal risks. Empirical evidence indicates that only 32.52% and 31.38% of connectedness in oil and gas, respectively, are transmitted to agricultural commodities, which suggests their weak tendencies in influencing agricultural commodities; the total connectedness index hovers around 40–60% in the 2018–2019 period; however, it dropped below 40% in 2020–2021 when the COVID-19 pandemic contributed to disintegrate the connectedness between energy and agricultural commodities but increased further during the 2022 Russia-Ukraine saga. The findings also indicate that corn, wheat, and flour are net transmitters of risks to oil and natural gas in the long and short-run, and wheat-flour pairwise connectedness is the strongest in the connectedness. Asymmetry is also pronounced in the network of connectedness. Portfolio analyses indicate that investors require a low proportion of energy in a portfolio of energy-agricultural commodities to achieve an optimum profit. The findings will offer exciting insights into the connectedness of agricultural and energy commodities, particularly during periods of high price uncertainty.

Bayesian circular lattice filters for computationally efficient estimation of multivariate time-varying autoregressive models

Nonstationary time series data exist in various scientific disciplines, including environmental science, biology, signal processing, econometrics, among others. Many Bayesian models have been developed to handle nonstationary time series. The time-varying vector autoregressive (TV-VAR) model is a well-established model for multivariate nonstationary time series. Nevertheless, in most cases, the large number of parameters presented by the model results in a high computational burden, ultimately limiting its usage. To address this issue, a computationally efficient multivariate Bayesian Circular Lattice Filter is developed, extending the usage of the TV-VAR model to a broader class of high-dimensional problems. The fully Bayesian framework allows both the autoregressive (AR) coefficients and innovation covariance to vary over time. The proposed estimation method is based on the Bayesian lattice filter (BLF), which is extremely computationally efficient and stable in univariate cases. To illustrate the effectiveness of the proposed approach, a comprehensive comparison with other competing methods is conducted through simulation studies and finds that, in most cases, the proposed approach performs superior in terms of the average squared error between the estimated and true time-varying spectral density. Finally, the methodology is demonstrated through applications to quarterly Gross Domestic Product (GDP) data and Northern California wind data.

Identifying the true nature of price discovery and cross-market informational flow in the investment grade CDS and equity markets

We examine the comparative efficiency of systematic investment grade credit default swap (CDS) and equity markets using a time-varying coefficient vector autoregression. This modeling framework enables a view of cross-market informational flow along each point in the time-period under investigation by taking into account parameter instability. We obtain smoothing estimates of parameters capturing such flow between CDS and equity markets using daily data from 2004 to 2019, and measure the strength of flow via relative predictive gains. In contrast to prior studies, we find a two-way interactive effect in which certain types of information are captured more efficiently in prices by each market. We also find that the time-varying coefficient vector autoregression results in superior forecasting gains relative to models not accounting for price discovery. These results have implications for systematic investors, arbitrageurs and stakeholders who monitor systematic markets for their informational content.

Examining the Time Varying Spillover Dynamics of Indian Financial Indictors from Global and Local Economic Uncertainty.

The research aims to excavate the role of global (Fed Rate, Crude, Real Dollar Index) and endogenous economic variables (GDP and Consumer Price Index) in shaping the spillover amongst the major Indian Financial indicators, viz. Nifty Index, MCX Gold, USDINR, Govt. Bond 10Y maturity and agricultural index N-Krishi. To facilitate cross-comparison decomposition of time-varying spillover output generated from Time-Varying Vector Autoregression (TVP-VAR) with aggregation at three layers is performed. The research finds that Indian Financial Indicators are vulnerable to spillover shocks from global variables predominantly driven by Fed Rate and Real Dollar Index. USDINR turns out to be most sensitive to global shocks and transgresses the shock to other financial indicators. Importantly, persistently high inflation has brought volatility spikes in the directional spillover to financial indicators. Though spillover subsidence is observed post-2014, with an all-time high during GFC, a sudden spurt in all financial indicators has been observed post-Covid-19, with Govt. bonds showing a sporadic rise. An important observation relates to staunch spillover from GDP during GFC with reoccurrence post-Covid. Additionally, a closely knit spillover tie is observed among USDINR, N-Krishi, and Crude. The study is beneficial to RBI to proactively monitor the weakening rupee along with Fed tapering to manage the rising spillover post-Covid-19. The effort of RBI has to be reciprocated by the government in inflation targeting to reinforce the curbing efforts of rising shock spillover.

Connectivity between the Latin American and U.S. stock markets in the presence of the Covid-19 pandemic

Objective - This paper aims to revisit the relationship between the Latin American and U.S. stock markets during the Covid-19 pandemic. Methodology- The dynamic connectivity between these markets was estimated by implementing the time-varying VAR model (TVP-VAR) for daily data from January 2010 to June 2021. Results - There are three main results. First, although spillovers from each market accounted for most of their variance, exchanges were not entirely independent of each other. Second, the U.S. and Brazil were net transmitters of spillovers, while Argentina and Chile were net receivers. Finally, the magnitude of net spillovers is not high enough to characterize a contagion effect. Practical implications - The inclusion of Latin American stock markets in the scope of Covid-19 studies is an important contribution to the literature that often neglects Latin American markets in its samples. Originality - The study of the connection between markets in the presence of Covid-19 differs from most that focus exclusively on the impact of Covid-19. Understanding the relationship between Latin American and U.S. markets in the presence of a crisis unlike any that has occurred before can help investors in their investment strategies and policymakers, governments, and monetary authorities interested in the integration or disintegration between markets.

The dynamic spillover effects of climate policy uncertainty and coal price on carbon price: Evidence from China

This paper applies the time-varying parameters vector autoregression (TVP-VAR) model to investigate the dynamic effects of climate policy uncertainty and coal price on carbon price in China. Based on news from China's mainstream newspapers and websites, a tailor-made climate policy uncertainty index is constructed. The VAR-BEKK-GARCH model is utilized as robustness check. The results indicate that both the climate policy uncertainty and coal price have significant time-varying effects on the carbon price. Additional dynamic connectedness analysis reveals that coal price is the main shock transmitter while climate policy uncertainty and carbon price are mostly net shock receivers.

Risk connectedness between energy and stock markets: Evidence from oil importing and exporting countries

The surmounted environmental and energy challenges have motivated this study to explore the connectedness nexus between oil/renewable energy and stock markets for oil-exporting (importing) countries. We utilize the dynamic conditional correlation (DCC-GARCH) connectedness framework to compare the connectedness of oil/renewable energy with stock markets. Our results showcase higher total connectedness between renewable energy and stock markets. We find increased connectedness during three major pandemics (Swine Flu, EBOLA, and COVID-19). We performed a regression analysis that highlighted the impact of economic and financial uncertainties on connectedness as an additional analysis. The addition of dummy variables for three major pandemics indicates that COVID-19 significantly impacted the connectedness between oil/renewable energy and stock markets. For the robustness of our results, we employed time-varying vector autoregressions (TVP-VAR) connectedness framework to showcase that our results remain qualitatively similar and robust to different specifications. We draw useful implications for oil exporting and oil importing countries in particular, and we draft ramifications for investors, portfolio managers, policymakers, and macroprudential bodies in general.

Are European natural gas markets connected? A time-varying spillovers analysis

This paper examines the European natural gas market connectedness. Four main markets from the North-Western and South European regions are investigated with the Diebold-Yilmaz connectedness approach derived from the time-varying parameters VAR model with stochastic volatility. We show that the European natural gas market becomes increasingly integrated. Our results update previous findings, which pointed to the leading role of the British NBP as a source of shocks to natural gas price and volatility. Instead, we demonstrate that the two leading markets in Europe are the Dutch TTF and the German NCG. In recent years, however, the observed dominance of the indicated markets weakens, while spillovers from the Italian PSV gain importance. This could indicate the growing role of the supply of liquefied natural gas (LNG) and the launch of the pipeline from the Caspian Sea to Italy.

Revisiting Exchange Rate Shocks on Macroeconomic Variables in China using Time-Varying VAR Model

In this study we analyze exchange rate shocks in China on its macroeconomic variables by apply TVP-VAR modeling approach. Three-dimensional impulse response functions reveal that GDP, CPI and interest rate are affected in the short run and long run. Our findings have important policy implications for the Chinese government conducting exchange rate policy.

Tail Dependency and Risk Spillover between Oil Market and Chinese Sectoral Stock Markets—An Assessment of the 2013 Refined Oil Pricing Reform

The Chinese refined oil pricing reform in 2013 has brought its refined oil price to be more aligned with the international oil price, helping to mitigate prior distorted pricing mechanisms. Its impact on the correlation, tail risks, and spillover effects between the international crude oil market and Chinese sectoral stock markets warrants empirical assessments. Time-varying copula models and conditional VaR (CoVaR) are employed to examine the correlation between the international oil market and Chinese sectoral stock indexes before and after the 2013 pricing reform, as well as the tail risk and spillover effects of the extreme and moderate oil markets. The results show that: (1) the correlation between the oil market and all 11 Chinese stock sectors is positive both before and after the reform, but the correlation is weaker after the reform than before; (2) The downside tail risk of the extreme and moderate oil markets to most Chinese stock market sectors, and the upside tail risk of the moderate oil market to most stock sectors are lower after the reform; (3) Tail risk spillover effects of extreme oil market on all sectors exist before and after the reform; (4) The upside tail risk spillover effects of moderate oil market exist in most sectors before the reform, but they almost all disappear after the reform. The downside risk spillover effects of the moderate oil market do not exist before or after the reform. The findings provide valuable references for portfolio management and future policy update.

Do financial volatilities mitigate the risk of cryptocurrency indexes?

Cryptocurrencies are renowned for their extreme volatilities, while financial volatilities exhibit inherent uncertainty in stock, oil, gold, and currency markets. Given these concerns, the current study endeavors to answer the question of whether financial volatilities can mitigate the risk of cryptocurrency indexes. We employ the time-varying parameters vector autoregression approach and find distinct spillover patterns between financial volatilities and cryptocurrency indexes. In this way, we stress the diversification benefits of financial volatilities to mitigate the risk of cryptocurrency indexes. Time-varying trends demonstrated extreme risk spillovers during turmoil periods. We outlined useful implications for policymakers, financial market participants, investors, and portfolio managers.

The Dynamic Effects of Urban–Rural Income Inequality on Sustainable Economic Growth under Urbanization and Monetary Policy in China

Income inequality in China has become increasingly serious since the beginning of the economic reform period in the 1970s, with urban–rural income inequality playing a large role. Urbanization policy and monetary policy are currently important economic policy tools for the Chinese government. In order to investigate the influence of inequality on the economy and to provide recommendations for ensuring the sustainability of growth, we study the effect of urban–rural income inequality on economic growth in the context of urbanization and monetary policy in China between 2002 and 2021. Using a flexible time-varying parametric structural vector auto-regression (TVP-VAR) model and a robust Markov chain Monte Carlo (MCMC) algorithm, our empirical results show that the effect is time-varying, with inequality promoting growth in the early years but affecting it adversely at later stages. Currently, urbanization mitigates inequality and promotes growth simultaneously, while easy monetary policy worsens inequality and affects growth adversely in the long term. We suggest that the authorities need to consider the implementation of policy rebalancing to ensure that the sustainability of economic development is not jeopardized because of worsening income disparity. Proactive urbanization policy and prudent monetary policy are viable rebalancing options.

An Alternative Estimation Method for Time-Varying Parameter Models

A multivariate, non-Bayesian, regression-based, or feasible generalized least squares (GLS)-based approach is proposed to estimate time-varying VAR parameter models. Although it has been known that the Kalman-smoothed estimate can be alternatively estimated using GLS for univariate models, we assess the accuracy of the feasible GLS estimator compared with commonly used Bayesian estimators. Unlike the maximum likelihood estimator often used together with the Kalman filter, it is shown that the possibility of the pile-up problem occurring is negligible. In addition, this approach enables us to deal with stochastic volatility models, models with a time-dependent variance–covariance matrix, and models with non-Gaussian errors that allow us to deal with abrupt changes or structural breaks in time-varying parameters.

The exposure of the US tourism subsector stocks to global volatility and uncertainty factors

ABSTRACT In the face of uncertain circumstances and volatilities across tourism stocks, the current study attempts to investigate the connectedness of US tourism subsectors using time-varying vector autoregressions (TVP-VAR) technique for the period spanning 1 August 2008–31 August 2021. Moreover, we examined the influence of various financial and economic indicators to observe their impact on the connectedness of US tourism subsectors. Further, we employed Diebold and Yilmaz ([2012]. Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57–66) connectedness approach to justify the robustness of our results. We find that hotels, transportation, and recreational are net transmitters of spillovers to the remaining US subsectors. We reported multiple time-varying attributes where the connectedness of US tourism subsectors was remarkably higher. In addition, the regression results reveal a positive and significant impact of financial and economic uncertainties on the connectedness. Finally, our findings were robust to the methodology employed, which substantially justified our estimator. We proposed numerous useful implications for policymakers, US government, tourism industry, investors, market players, and portfolio managers to benefit from the findings obtained.

Estimation of large dimensional time varying VARs using copulas

This paper provides a simple, yet reliable, alternative to the (Bayesian) estimation of large multivariate VARs with time variation in the conditional mean equations and/or in the covariance structure. The original multivariate, n-dimensional model is treated as a set of n univariate estimation problems, and cross-dependence is handled through the use of a copula. This makes it possible to run the estimation of each univariate equation in parallel. Thus, only univariate distribution functions are needed when estimating the individual equations, which are often available in closed form, and easy to handle with MCMC (or other techniques). Thereafter, the individual posteriors are combined with the copula, so obtaining a joint posterior which can be easily resampled. We illustrate our approach using various examples of large time-varying parameter VARs with 129 and even 215 macroeconomic variables.

Modeling brain connectivity dynamics in functional magnetic resonance imaging via particle filtering

Interest in the studying of functional connections in the brain has grown considerably in the last decades, as many studies have pointed out that alterations in the interaction among brain areas can play a role as markers of neurological diseases. Most studies in this field treat the brain network as a system of connections stationary in time, but dynamic features of brain connectivity can provide useful information, both on physiology and pathological conditions of the brain. In this paper, we propose the application of a computational methodology, named Particle Filter (PF), to study non-stationarities in brain connectivity in functional Magnetic Resonance Imaging (fMRI). The PF algorithm estimates time-varying hidden parameters of a first-order linear time-varying Vector Autoregressive model (VAR) through a Sequential Monte Carlo strategy. On simulated time series, the PF approach effectively detected and enabled to follow time-varying hidden parameters and it captured causal relationships among signals. The method was also applied to real fMRI data, acquired in presence of periodic tactile or visual stimulations, in different sessions. On these data, the PF estimates were consistent with current knowledge on brain functioning. Most importantly, the approach enabled to detect statistically significant modulations in the cause-effect relationship between brain areas, which correlated with the underlying visual stimulation pattern presented during the acquisition.

Responses of China’s shadow banking system to exogenous shocks

ABSTRACT The rapid expansion of China’s shadow banking system in recent years has led to continuous accumulation of risks and posed threats to the security of China’s real economy. According to the specific mechanism of China’s economic operation, this paper constructs a dynamic stochastic general equilibrium model to analyse the responses of China’s shadow banking system to the exogenous shocks of benchmark interest rate adjustment, high-risk enterprise return rate fluctuation and shadow bank return rate fluctuation. Results indicate that (1) the shadow bank loan interest rate reacts intensively to the benchmark interest rate adjustment; (2) the shadow bank loan interest rate has immediate and short-term response to the fluctuation of high-risk enterprise return rate; (3) the fluctuation of shadow bank return rate indirectly affects the shadow bank loan interest rate. An empirical study using the time-varying VAR model provides further empirical evidence. The shadow banking risks caused by the impacts on the shadow bank loan interest would increase the fragility of China’s financial system and endanger the security of China’s real economy.

Fluctuation in the global oil market, stock market volatility, and economic policy uncertainty: A study of the US and China

This paper investigates the relationships between the global oil market, stock markets, and economic policy, focusing on China and the US. We use a time-varying parameter stochastic volatility vector autoregression model to examine the transmission mechanisms between fluctuation in the global oil price, the volatility of the Chinese and US stock markets, and economic policy uncertainty in China and the US from 2003 to 2020. We observe significant correlations between the volatility of the two countries’ stock markets and fluctuation in the global oil price. Compared with the Chinese stock market, the US stock market has a greater impact on the global oil market. We also find that an increase in economic policy uncertainty in the two countries exacerbates fluctuation in the global oil price, particularly during times of crisis. In turn, greater fluctuation in the global oil price increases stock market volatility and economic policy uncertainty in both countries.

The risk spillover effect of the COVID-19 pandemic on energy sector: Evidence from China

Detecting the adverse effects of major emergencies on financial markets and real economy is of great importance not only for short-term policy reactions but also for economic and financial stability. This is the lesson we learnt from the COVID-19 pandemic. This paper focuses on the risk spillover effect of the COVID-19 on Chinese energy industry using a high-dimensional and time-varying factor-augmented VAR model. The results show that the net volatility spillovers of the pandemic remain positive to all underlying energy sectors during January to June of 2020 and February to April of 2021. For the former sub-period, the volatility spillover of the COVID-19 is not only the highest, but also lasts longest for oil exploitation sector, followed by the power and gas sectors. While for the latter sub-period, the COVID-19 has relatively higher volatility spillovers to the power, coal mining and petrochemical sectors. These findings suggest that the COVID-19 has significant risk spillover effects on Chinese energy sectors, and the effects vary among different energy sub-sectors and across different periods of time.