Time-varying Autoregressive Model Research Articles

The dynamic connectedness and hedging opportunities of implied and realized volatility: Evidence from clean energy ETFs

This paper aims to examine dynamic connectedness and hedging opportunities between the realized volatilities of clean energy ETFs and energy implied volatilities through Time-Varying Parameter Vector Autoregression Model (TVP-VAR) and Asymmetric Dynamic Conditional Correlation (ADCC) GARCH models. TVP-VAR analysis results show that dynamic connectedness increases during turbulence periods. We also determine that clean energy ETFs such as PBW, QCLN, SMOG, and TAN are net volatility transmitters. Surprisingly, OVX is a net volatility receiver, especially with the developments after the Paris Agreement in 2016.As a result of the ADCC GARCH analysis, we determine that the conditional correlation between clean energy ETFs and implied volatility ETFs is asymmetric, and negative information shocks increase the conditional correlation. Although OVX is a cheap alternative for hedging long position risks in clean energy ETFs, VXXLE is more effective than OVX in terms of hedging effectiveness. These findings provide insight for individual and institutional investors, and portfolio managers on how negative and positive shocks change the conditional correlation between assets at different levels.

The time-varying effects of trade policy uncertainty and geopolitical risks shocks on the commodity market prices: Evidence from the TVP-VAR-SV approach

The paper focuses on investigating the time-varying influence of geopolitical risks (GPR) and trade policy uncertainty (TPU) on commodity prices by using time-varying parameter vector autoregressive model with stochastic volatility (TVP-VAR-SV). We find that (i) TPU and GPR have significant time-varying effects on the aggregate (classified) commodity market, and the former is a short-term effect before 2006, and it becomes a medium-to-long-term effect after 2006, while the latter is mainly a short-term effect; (ii) TPU shock has a significant positive and time-varying impact on GPR, and the relatively long-term impact is more obvious before 2017, while the short-term impact will dominate after 2017. Additionally, the GPR shock has a short-term negative impact on TPU, and a medium- and long-term positive impact except for the period from 2002 to 2006; (iii) the impact of geopolitical threats (GPT) and geopolitical acts (GPA) on aggregate commodity market have positive and negative alternating shock effects with time variability and a significant short periods impact; (iv) there is a certain degree of heterogeneity in the response of different commodity prices, and the response of individual commodities is related to specific external shocks, such as the COVID-19 pandemic.

Estimating scale-free dynamic effective connectivity networks from fMRI using group-wise spatial–temporal regularizations

Estimating dynamic effective connectivity (dEC) networks is crucial to understand the time-varying directional interconnections among brain regions. It is now widely understood that brain networks have the property of being scale-free. However, this property has seldom been considered and is often inadequately preserved using conventional dEC estimation methods. As a result, important hubs and network graphical characteristics cannot be accurately obtained. In this work, we develop a new method to use a group-wise penalty together with spatial sparsity and temporal smoothness regularizations (namely Group-wise Spatial–Temporal Regularizations, GSTR) for the inference of scale-free dEC networks from functional magnetic resonance imaging (fMRI). The method employs a time-varying vector autoregressive (VAR) model, where the model coefficients can be formed as adjacency matrices of the dEC networks. Meanwhile, the proposed group-wise regularization is able to preserve the connectivities of potential hubs in scale-free networks by grouping them as an entire set. To deal with the complexity of optimization with multiple regularizations, we propose an effective algorithm based on the augmented Lagrangian multiplier. The accuracy of the GSTR method is validated using a variety of synthetic datasets with the scale-free property. Furthermore, we apply the GSTR method to an open fMRI dataset recorded from a block design visual task-related experiment containing 255 healthy participants to estimate visual-induced dEC networks and find GSTR can achieve reasonable and interpretable dEC estimates. Results from both synthetic and real-world datasets suggest that the proposed GSTR method could serve as a powerful analytical tool to accurately infer scale-free dEC patterns.

Risk transmissions between bitcoin and traditional financial assets during the COVID-19 era: The role of global uncertainties

This paper examines return and volatility connectedness between Bitcoin, traditional financial assets (Crude Oil, Gold, Stocks, Bonds, and the United States Dollar-USD), and major global uncertainty measures (the Economic Policy Uncertainty-EPU, the Twitter-based Economic Uncertainty-TEU, and the Volatility Index-VIX) from April 29, 2013, to June 30, 2020. To this end, the Time-Varying Parameter Vector Autoregression (TVP-VAR) model, dynamic connectedness approaches, and network analyses are used. The results indicate that total spillover indices reached unprecedented levels during COVID-19 and have remained high since then. The evidence also confirms the high return and volatility spillovers across markets during the COVID-19 era. Regarding the return spillovers, Gold is the centre of the system and demonstrates the safe heaven properties. Bitcoin is a net transmitter of volatility spillovers to other markets, particularly during the COVID-19 period. Furthermore, the causality-in-variance Lagrange Multiplier (LM) and the Fourier LM tests' results confirm a unidirectional volatility transmission from Bitcoin to Gold, Stocks, Bonds, the VIX and Crude Oil. Interestingly the EPU is the only global factor that causes higher volatility in Bitcoin. Several potential implications of the results are also discussed.

Time-varying spillovers between trade policy uncertainty and precious metal markets: Evidence from China-US trade conflict

Using a time-varying vector autoregressive (TVP-VAR) model combined with a spillover index, we study the dynamic spillovers between trade policy uncertainty (TPU) and precious metal markets during the Sino-US trade war. The results show obvious spillover effects between the Chinese TPU and American TPU and the precious metal markets, and the strength and direction of the spillover effects are time-varying and asymmetrical. The uncertainty of the Sino-US trade policy has a heterogeneous impact on the precious metal markets. American TPU dominates the markets, followed by Chinese TPU. In the face of trade war conflict, the spillover fluctuation of American TPU to Chinese TPU is very significant. In addition, in the face of trade policy uncertainty, gold and silver have strong self-adjustment abilities and stabilities, making them highly suitable for hedging investments. International investors and policymakers should consider the impacts of international trade policy uncertainty when conducting risk monitoring and building portfolios in precious metal markets.

Bitcoin speculation, investor attention and major events. Are they connected?

ABSTRACT We use a time-varying vector autoregressive model to investigate the dynamic effect of investor attention on Bitcoin speculation and then examine the association of this effect with five types of events in the Bitcoin market. The results indicate that investor attention has a positive effect on Bitcoin speculation and this effect changes with time and decays as lag phases increase. Policy-related events are the key factors that make this effect time-varying, while safety events have no obvious impact. Besides, our results find the existence of contrarian strategy in the Bitcoin market.

Volatility spillovers between fine wine and major global markets during COVID-19: A portfolio hedging strategy for investors

Motivated by the growing necessity of portfolio diversification, this paper investigates the dynamic connectedness among fine wine, equities, bonds, crude oil, commodities, gold, copper, shipping and real estate by applying the Diebold and Yilmaz (2012) approach, based on the time-varying parameter vector autoregressive (TVP-VAR) model of Antonakakis et al. (2020), for the period 1/1/2010-5/31/2021. Our results indicate moderate volatility spillovers among the markets over time, whereas total connectedness is prone to exogenous shocks, reaching its peak during stress periods. Equities, crude oil, gold and fine wine are the net contributors of spillovers, whereas real estate, commodities, copper, bonds and shipping constitute the net receivers of the diffused shocks. Furthermore, we estimate and compare the hedging ability of fine wine, before and after the emergence of the coronavirus pandemic, to instruct investors in rebalancing their portfolio strategies during COVID-19. The empirical findings suggest that fine wine can form an effective hedging tool to reduce the risk deriving from adverse movements of the markets and its hedging ability was enhanced during COVID-19, with few exceptions. Regardless time period, the highest hedging effectiveness can be achieved by taking a long position in the volatility of crude oil and a short position in the volatility of fine wine.

Evolution of Monetary Policy in Peru: An Empirical Application Using a Mixture Innovation TVP-VAR-SV Model

Abstract This article discusses the evolution of monetary policy (MP) in Peru in 1996Q1–2019Q4 using a mixture innovation time-varying parameter vector autoregressive (VAR) model with stochastic volatility (TVP-VAR-SV) as proposed by Koop, Leon-Gonzales and Strachan. The main empirical results are: (i) the VAR coefficients and volatilities change more gradually than the contemporaneous coefficients over time; (ii) the volatility of MP shocks was higher under the pre-Inflation Targeting (IT) regime; (iii) a surprise increase in the interest rate produces gross domestic product (GDP) growth falls and reduces inflation in the long run; (iv) the interest rate reacts more quickly to aggregate supply shocks than to aggregate demand shocks; (v) MP shocks explain a high percentage of domestic variables behavior under the pre-IT regime but their contribution decreases under the IT regime. Overall, these results show that MP has contributed in Peru to lower macroeconomic volatility by (i) reducing average long-term inflation, (ii) increasing the response of GDP growth rate to interest rate, and (iii) by becoming more predictable. (JEL codes: C11, C32, and E52).

Dynamic nonlinear connectedness between the green bonds, clean energy, and stock price: the impact of the COVID-19 pandemic.

This paper uses weekly data from July 01, 2011 to July 09, 2021 to examine the dynamic nonlinear connectedness between the green bonds, clean energy, and stock price around the COVID-19 outbreak in the global markets. By building a time-varying parameter vector autoregression model (TVP-VAR), the comparison analyses of pre- and during the COVID-19 sample groups verify the existence of nonlinear and dynamic correlation among the three variables. First, prior to the COVID-19 pandemic, the simultaneous impacts of clean energy on stock price increased over time. Second, the results of impulse responses at different horizons indicate that green bonds lead to a short-term increase of clean energy, and it exerts an increasingly positive impacts after the COVID-19 outbreak. The COVID-19 has weakened the negative impacts of green bonds on stock price in the medium term. Finally, through the analysis of impulse responses at different points, we find that stock prices will rise when clean energy is subjected to a positive shock, and this positive effect is stronger during economic recovery period than in the other two periods.

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

In order to better demonstrate the relationship between agricultural economic structure growth and environmental pollution, an autoregressive model based on time-varying parameter vector was proposed. In the process of developing the research, this paper introduces the LDMI method, based on the time-varying parameter vector autoregression model, with the help of sampling formula calculation and other methods. Efforts were made to obtain credible conclusions. The experiment result shows that in this study, a total of 10,000 samples were taken. According to this value, 10000/116.15 = 86, which means that at least 86 unrelated samples can be obtained. Therefore, we can determine that each indicator mentioned in this paper has valid samples when it is introduced into the time-varying parameter vector autoregression (TVP-VAR) model for parameter estimation. After sampling detection image analysis and data calculation, the effect of energy structure, energy intensity industrial structure, and scale effect on the emission scale of environmental pollutants was obtained. It is proved that through the research of this paper, two main conclusions are finally obtained, and the influence of the five factors mentioned above is summarized.

Identifying the volatility spillover risks between crude oil prices and China's clean energy market

<abstract><p>Since the COVID-19 outbreak, the global economy has been hit hard, and the development of renewable energy and energy transitions has become a common choice for all countries. The development of clean energy firms has become a hot topic of discussion among scholars, and the relationship between the stock prices of clean energy firms and the international crude oil market has attracted more attention. In this paper, we analyze the volatility connectedness between crude oil and Chinese clean energy firms from 2016 to 2022 by building time-varying vector autoregressive models with stochastic volatility components and time-varying spillover index and dynamic conditional correlation GARCH models. The results of the shock effects analysis show that international crude oil volatility had a significant short-term positive impact on Chinese clean energy firms during the COVID-19 outbreak period. Regarding spillover analysis, firms with large total market capitalization tended to be the senders of volatility spillovers, while smaller firms were likely to be the recipients. In terms of dynamic correlation analysis, the correlation between international crude oil and each clean energy firm was found to be volatile, and the dynamic correlation coefficient tended to reach its highest point during the COVID-19 outbreak. Meanwhile, from the optimal portfolio weighting analysis, it is clear that all optimal weights of international crude oil and medium clean energy firms will increase during an epidemic outbreak, and that more assets should be invested in clean energy firms.</p></abstract>

THE CONNECTEDNESS BETWEEN THE SENTIMENT INDEX AND STOCK RETURN VOLATILITY UNDER COVID-19: A TIME-VARYING PARAMETER VECTOR AUTOREGRESSION APPROACH

We analyze the connectedness between the sentiment index and the return and volatility of the crude oil, stock and gold markets by employing the time-varying parameter vector autoregression model vis-à-vis the coronavirus disease (COVID-19) epidemic. Our sentiment index is constructed via text mining technology. We also employ a network to visualize and better understand the structure of the connectedness. The results confirm that the sentiment index is the net pairwise directional connectedness receiver, while the infectious disease equity market volatility tracker is the transmitter. Furthermore, the impact of the COVID-19 pandemic on the total connectedness of volatility is unprecedented.

Time-Varying Network Connectedness of G-7 Economic Policy Uncertainties: A Locally Stationary TVP-VAR Approach

This work analyzes the frequency-dependent network structure of Economic Policy Uncertainties (EPU) across G-7 countries between January 1998 and April 2021. We implement an approach that builds dynamic networks relying on a locally stationary Time-Varying Parameter-Vector Autoregressive model using Quasi-Bayesian Local Likelihood methods. We compute short-, medium-, and long-term network connectedness of G-7 EPUs over a period covering several economic/financial turmoils. Furthermore, we structure short-term network topologies for the Global Financial Crisis (GFC) and the COVID-19 pandemic periods. Findings of the study indicate amplified interdependencies between G-7 EPUs around well-known economic/geopolitical incidents, frequency-dependent connectedness networks among them, and stronger interdependencies than the medium-, and long-term linkages. Finally, we find that short-term spillovers are not persistent in the long-term for both turmoil periods.

Time-varying impact of economic policy uncertainty and geopolitical risk on tourist arrivals: Evidence from a developing country

Although the relationship between uncertainties and tourism has been widely discussed, the time-varying impacts of uncertainties on tourism have been ignored. This paper investigates the time-varying relationship between two global and domestic uncertain shocks (namely, economic policy uncertainty (EPU), geopolitical risks (GPR)) with Chinese inbound tourism using a time-varying parameter vector autoregression (TVP-VAR) model. The empirical results show that uncertain shocks on tourist arrivals have significantly time-varying features, and the direction of the effect changes. The impact gradually weakens with the increase in the lag period. However, the impacts are heterogeneous depending on the uncertain shocks. Additionally, the effects of major event shocks on tourism differ. The uncertainty of the 9/11 terrorist attack had the shortest impact on tourism, while the impact on tourism was greater for geopolitical risks than for economic policy uncertainty during the global financial crisis. These findings provide new insights for policy holders and relevant investors into the mentioned relationships.

Financial cycle and the effect of monetary policy

Using a time-varying parameter vector vector autoregressive model with stochastic volatility (TVP-SV-VAR), this study evaluates the effect of quantitative and price-based monetary policies on China's financial cycle, which is represented by a dynamic financial condition index (FCI) constructed with a time-varying parameter factor-augmented vector autoregressive (TVP-FAVAR) model. Results reveal that quantitative monetary policy boosts the financial cycle slightly and swiftly, whereas price-based monetary policy has slower but stronger long-term negative effect to restrain boom and control financial risk. Moreover, the effects of both monetary policies are compromised in periods of strong financial fluctuation.

The Time-Varying Effect of Asset Prices on Turkey’s Circular Economy

The study aims to examine the effects of spillovers from stock prices on consumption and interest rates in Turkey. From the circular economy viewpoint, there should be sustainable consumption to achieve sustainable development with the help of consumers and other stakeholders. A time-varying vector autoregressive (TVP-VAR) model with stochastic volatility is used in the study. The aim is to obtain dynamics that stimulate growth, development, recession or change within the Turkish economy according to the emphases on circular economy. In order to analyze the relationship between real consumption, nominal interest rate and real stock prices, the TVP-VAR model is specified as a three-variable, time-varying model. The sample data that have been gathered from the Central Bank of the Republic of Turkey cover the period between Q1 1987 and Q3 2013. Overall, this study provides significant evidence of spillovers on consumption and interest rate during financial crises in Turkey, and the implications of monetary policy. In addition, the TVP model with stochastic volatility offers remarkable results regarding the influence of price shock on consumption in Turkey. However, we do not find any significant effect from interest rate to real consumption.

The Post-COVID-19 Economic Policy Uncertainty and the Effectiveness of Monetary Policy: Evidence From China.

The outbreak of the COVID-19 pandemic has caused an upsurge economic policy uncertainty (EPU). Study on the time-varying effect of EPU is of substantial implication for the central bank in implementation of monetary policy. To empirically investigate the time-varying effect of EPU, the paper considers the shock of the monetary policy implemented by China's central bank on different economic variables including interest rate, output gap, and inflationary gap using the latent threshold time-varying parameter vector autoregressive model (LT-TVP-VAR Model). Data period is chosen to be January 2015 through April 2021. Our findings show that (i) EPU has a significant threshold effect on the shock of quantitative monetary policy instrument and the shock of price-based monetary policy, and that the two types of policy are positively correlated; (ii) the price-based monetary policy instrument has a significant counter-cyclical effect on both output gap and inflationary gap; (iii) relative to the quantitative monetary policy instrument, the price-based monetary policy instrument has a more significant counter-cyclical effect on output gap; and (iv) a higher level of EPU is associated with a more significant monetary policy effect on output gap and inflationary gap.

Monetary Policy’s Regulation and Control of Housing Prices under the Novel Coronavirus Pneumonia Epidemic

In recent years, the downward pressure and uncertain factors of my country’s economic development have gradually increased, especially due to the greater impact of the novel coronavirus pneumonia epidemic in early 2020, it is necessary to adjust the monetary policy to make the economy operate stably. Monetary policy is the main means of controlling housing prices, so this article uses the sample data of my country from January 2011 to September 2020, and uses the time-varying parameter vector autoregressive (TVP-SV-VAR) model under the background of economic policy uncertainty (EPU) for the first time to analyze the price-based and quantitative monetary policies are very necessary to regulate and control housing prices. The study found that the increase in economic policy uncertainty will weaken the regulatory role of quantitative monetary policy but will strengthen the regulatory role of price-based monetary policy; the current economic policy uncertainty has increased, especially since the outbreak of the novel coronavirus pneumonia, the quantitative monetary policy is unstable, and the price-based monetary policy is the best choice for controlling housing prices.

The dynamics and determinants of liquidity connectedness across financial asset markets

We quantify the degree of liquidity connectedness across stock, bond, money and foreign exchange markets in Malaysia. The liquidity connectedness index from the time-varying parameter vector autoregression model reveals sensitivity to extreme market events but low cross-asset liquidity contagion on average, implying negligible risk of a systemic liquidity dry-up in the Malaysian financial markets. Further analysis finds that cross-asset liquidity connectedness is explained by global market uncertainty, perceived credit risk as well as international crude oil prices. The influence of external factors underscores the needs for small open economy like Malaysia to expand market surveillance to monitor cross-border liquidity shocks.

FINANCIAL CYCLES IN THE ECONOMY AND IN ECONOMIC RESEARCH: A CASE STUDY IN CHINA

This work explores the relationship between financial cycles in the economy and in economic research. To this aim, we take China as an empirical example, and an intuitive bibliometric analysis of selected terms concerning financial cycles in economic research is performed first. Both in the economy and in economic research, we then conduct singular spectrum analysis to further isolate and describe the specific length and amplitude of financial cycles for China based on quarterly time-series data. Finally, according to the estimated cycles that detrended by Hodrick-Prescott filter for financial and bibliometric variables, the Granger causality test scrutinizes the results of the first two steps. Moreover, a time-varying parameter vector autoregression model is estimated to quantitatively investigate the time-varying interaction between financial and bibliometric variables. Our study shows that financial cycles have a strong effect on the developments in the financial-related literature. In particular, the 2008 global financial crisis’s impulse intensity is significantly higher than in other periods. Surprisingly, discussions on financial cycles in the literature also have an impact on financial activities in real life. These findings contribute to nascent work on the patterns in financial cycles, thus providing a new and effective insight on the interpretation of financial activities.