Time-varying VAR Model Research Articles

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.

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.

The dynamic connectedness between macroeconomic uncertainty and commodity volatility: evidence from China

ABSTRACT We investigate the dynamic connectedness between Chinese macroeconomic uncertainty and volatility in Chinese commodity markets, controlling for international macroeconomic uncertainty, international commodities and Economic Policy Uncertainty Index (EPU), and adopting an approach that combines the Diebold – Yilmaz connectedness measure with the time-varying VAR model. Our empirical results indicate that Chinese macroeconomic uncertainty was the main net transmitter of the commodities commonly used in manufacturing, such as chemical engineering materials and non-ferrous metal, over the whole sample period, and had a considerable impact on these commodities due to the production shutdown caused by COVID-19 containment measures. This indicates the important role of Chinese macroeconomic uncertainty on the supply and demand sides. As China is the largest manufacturing country, macroeconomic uncertainty in this nation has had a significant and increasing influence on international commodities, especially when there occur events that severely affect global production activity. However, the impacts of EPUs on commodity volatilities were limited compared with the effects of Chinese macroeconomic uncertainty, especially during the COVID-19 pandemic, demonstrating that macroeconomic uncertainty, which reflects uncertainty about future aggregate supply and demand, plays a more crucial role in driving the volatility of commodities with stronger production attributes.

The effect of oil price and macroeconomic factors on inflation rate in Azerbaijan: The application of time-varying coefficient VAR model and time-varying Granger causality analysis

This study analyzes inflation dynamics in Azerbaijan, focusing on the impact of oil price fluctuations and macroeconomic variables using advanced econometric methods, specifically a time-varying coefficient vector autoregressive model and time-varying Granger causality analysis. The research identifies significant inflation inertia, where past inflation rates shape future expectations. The relationship between oil prices and inflation has evolved, with contractionary policies mitigating the inflationary effects of rising oil prices after 2011, but oil price volatility and external inflationary pressures resurfacing post-2021. Azerbaijan's exchange rate policy reduced inflation by lowering import prices before 2017, but its effectiveness has since weakened. Fiscal policy, particularly changes in M2, has played a key role in inflation dynamics, with contractionary measures after 2015 reducing inflation, while expansionary policies in 2022 reignited inflation. The study also shows that growth in the non-oil sector helps alleviate inflation, though its influence has diminished over time. The study concludes that effective policy measures are essential to manage inflation inertia, reduce dependence on oil revenues, and strengthen economic resilience in a rapidly changing global landscape.

House Bubbles, global imbalances and monetary policy in the US

This paper examines the factors driving housing price exuberance in the United States, specifically the influence of expansionary monetary policies and the global saving glut. We employ medium scale Bayesian VAR and time-varying VAR models to estimate the effects of monetary policy and global saving glut shocks on US housing bubbles. We find that, prior to the Global Financial Crisis, the impact of the saving glut shock is more enduring, powerful, and rapid in generating housing bubbles compared to monetary policy shocks. However, the recent housing boom that commenced in 2019 demonstrates a different pattern. Our results suggest that both monetary policy and the global saving glut contribute to the increase in house prices. Counterfactual policy experiments validate this conclusion.

A tool to simulate and visualize dyadic interaction dynamics.

ynamic models are becoming increasingly popular to study the dynamic processes of dyadic interactions. In this article, we present a Dyadic Interaction Dynamics (DID) Shiny app which provides simulations and visualizations of data from several models that have been proposed for the analysis of dyadic data. We propose data generation as a tool to inspire and guide theory development and elaborate on how to connect substantive ideas to specific features of these models. We begin by discussing the basics of dynamic models with dyadic interactions. Then we present several models and illustrate model-implied behavior through generated data, accompanied by the DID Shiny app which allows researchers to generate and visualize their own data. Specifically, we consider: (a) the first-order vector autoregressive (VAR(1)) model; (b) the latent VAR(1) model; (c) the time-varying VAR(1) model; (d) the threshold VAR(1) model; (e) the hidden Markov model; and (f) the Markov-switching VAR(1) model. Finally, we demonstrate these models using empirical examples. We aim to give researchers more insight into what dynamic modeling approach fits their research question and data best. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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.

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.

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.

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.

Forecasting crude oil real prices with averaging time-varying VAR models

The aim of this research is to discuss the ability to forecast real crude oil price by the use of Time-Varying Vector Autoregression (TVP-VAR) models. In particular, model averaging and model selection schemes over several TVP-VAR models are performed. These methods address the problem of variable uncertainty. Indeed, several previous studies indicate that explanatory variables for crude oil prices can be different in different periods of time. Further, the strength of the relationship between crude oil price and its determinants can vary in time. The applied model combination schemes are an extension of Dynamic Model Averaging, which has already been found viable. Moreover, geopolitical risk is included in each model as an endogenous variable, and the model combination scheme is constructed in a way to tackle the joint forecasting ability (with respect to crude oil real price and geopolitical risk). It is found that, indeed, the Vector Autoregression approach results in more accurate forecasts than single equation a Time-Varying Regression or the standard Dynamic Model Averaging. Also, the model combination scheme of several Vector Autoregression models outperforms a single Vector Autoregression model approach. However, forecast accuracy is tested with some novel tools, such as Giacomini and Rossi fluctuation test and Murphy diagrams, which are able to capture time-varying predictive ability significances and several scoring functions.

Fossil energy and clean energy stock markets under COVID-19 pandemic

ABSTRACT This paper analyses the dynamic impact of fossil energy shocks (crude oil and Natural gas) on renewable clean energy stock markets during the COVID-19 pandemic. Using a time-varying VAR model with stochastic volatilities, we conduct distinct analyses over the period from 10th March to 15th June 2020, while distinguishing between three sub-periods. Our key findings show a significant increase in the returns of clean energy stocks after the sharp collapse of crude oil prices. However, we find the opposite effect on renewable energy stock returns after the OPEC+ agreement. The results also indicate that the announcement of COVID-19 as a global pandemic caused the prices of both Natural gas and renewables to rise after a decrease. By contrast, after the crude oil shocks, there was no response of renewables to Natural gas shocks. Furthermore, the stochastic volatility reveals that after the OPEC-Russia agreements, the volatility of crude oil returns gets back to normal and steady fluctuation. Our results provide useful implications for policy-makers to mitigate the negative effects on renewable energy investments due the uncertainty related to the ongoing pandemic.

BETTER SAFE HAVENS DURING COVID-19: A COMPARISON BETWEEN ISLAMIC AND SELECTED FINANCIAL ASSETS

This study examines the safe haven properties of six assets (the S&P Technology Index, S&P GSCI Commodity Index, bitcoin, the Dow Jones Islamic Equity Index, the Dow Jones Global Sukuk Index and US Treasury bonds) during contiguous infectious diseases, employing the equity index returns of three regional markets (S&P500,S&P Europe, and S&P Asia-Pacific) over the period 2010 - 2020 Q2. In the research, information-rich methodological tools such as the Markov switching approach and the DCC-GARCH model are used. Our results suggest that Sukuk and bonds act as safe havens for different types of investors during the ongoing COVID-19 crisis. This property is, however, is not confirmed for the S&P Technology Index, Commodity Index, bitcoin or the DJ Islamic Equity Index. Moreover, using the time-varying VAR model and the new measure of pandemic uncertainty proposed by Baker et al. (2020), the results demonstrate that the COVID-19 pandemic has led to uncertainty and heightened volatility spillovers among regional equities and the safe haven assets examined. The key results of the study are robust and useful for portfolio managers and investors.

Dynamic connectedness between stock markets in the presence of the COVID-19 pandemic: does economic policy uncertainty matter?

This study investigates the dynamic connectedness between stock indices and the effect of economic policy uncertainty (EPU) in eight countries where COVID-19 was most widespread (China, Italy, France, Germany, Spain, Russia, the US, and the UK) by implementing the time-varying VAR (TVP-VAR) model for daily data over the period spanning from 01/01/2015 to 05/18/2020. Results showed that stock markets were highly connected during the entire period, but the dynamic spillovers reached unprecedented heights during the COVID-19 pandemic in the first quarter of 2020. Moreover, we found that the European stock markets (except Italy) transmitted more spillovers to all other stock markets than they received, primarily during the COVID-19 outbreak. Further analysis using a nonlinear framework showed that the dynamic connectedness was more pronounced for negative than for positive returns. Also, findings showed that the direction of the EPU effect on net connectedness changed during the pandemic onset, indicating that information spillovers from a given market may signal either good or bad news for other markets, depending on the prevailing economic situation. These results have important implications for individual investors, portfolio managers, policymakers, investment banks, and central banks.

Oil price change and Economy relationship :A global review using a nonlinear dynamic model for MENA Countries

This paper studies the macroeconomic effects of oil price shocks in a selection of MENA countries. The oil price shock is identified by assuming that an individual country's performance does not affect world oil prices. We put particular emphasis on the time-varying relationship between oil prices and macroeconomic variables and implement their approach in a Time-Varying Structural VAR model (TV-SVAR) framework.
 The main findings are that the macroeconomic effects of oil price shocks have evolved over time in MENA countries. Interestingly, however, we do not find a lot of heterogeneity among the MENA countries they consider, even though their list includes both net oil exporters (Algeria, Bahrain, Iran, Kuwait, Saudia Arabia) and net oil importers (Turkey and Tunisia)

The effects of the ECB’s expanded asset purchase programme

Abstract This paper provides empirical evidence on the financial and macroeconomic impact of the news associated to the announcement launching the ECB’s expanded asset purchase programme (APP). We proxy the APP announcement shock with an announcement variable derived from the institutional features of the APP and survey-based evidence. The variable is used in a time-varying coefficient VAR model with stochastic volatility to estimate the effects of the announcement shock. The evidence suggests that this shock had a significant upward effect on both real economic activity and HICP inflation in the euro area. The response of long-term interest rates gave rise to a flattening of the yield curve in the short term, followed by a steepening in the medium term. Several channels of transmission appear to have been activated, including the portfolio rebalancing channel, the exchange rate channel and the credit channel.

Trade policy uncertainty and its impact on the stock market -evidence from China-US trade conflict

We study the impact of international trade policy uncertainty (TPU) on equity markets. To capture the current tension in the relationship between China and the US, we use monthly stock market volatility and TPU indices of China and US from 2000 to 2019. By adopting a time-varying VAR model (TVP-SV-VAR), we find that TPU has heterogeneous effects on U.S. and China stock market. U.S. TPU has stronger impact on both U.S. and Chinese stock market. Time-varying characteristics show that U.S. - China trade conflicts have positive effect on U.S. stock market but a negative effect on Chinese stock market.

Study on stock market volatility spillover effect based on TVP-VAR model

Utilizing time-varying VAR model and impulse response analysis, we examine the dynamic impact of volatility from foreign companies listed and their contribution to the main stock market in Japanese, Korean, Hong Kong and Singapore markets during the period of 20002018. We find that the host markets have a positive response to volatility effects of foreign companies, more specifically, the increase of the foreign company volatility will cause the increase of the host market volatility. Results show that volatility of foreign companies from mature markets did not cause more volatility in host markets. Interestingly, foreign companies from emerging markets will bring stronger volatility spillover to host markets.

Economic and business cycles with time varying in India: evidence from ICT sectors

The purposes of this paper are two main sections. The former is to study the relationship between Indian ICT industries and GDP by applying Bayesian inference. Yearly predominant indexes collected during 2000 to 2015, including Indian GDP, fixed phone usages, mobile phone distributions, internet servers, and broadband suppliers are analysed by employing the Markov-switching model (MS-model) and Bayesian vector autoregressive model (BVAR). The latter is the time-varying parametric VAR model with stochastic volatilities (TVP-VAR). With Bayes statistics, this time-varying analysis can more clearly provide the extended perception to the underlying flexible structure in the economy. Additionally, the Bayesian regression model is used to investigate the ICT multiplier related to Indian economic growth. Empirically, results indicate IT sectors are now becoming the importance of Indian economic expansion, compared with telecommunication sectors. The ICT multiplier also confirms high-technological industrial zones should be systematically enhanced, especially, researches and developments in cyberspace.