Multivariate Vector Autoregressive Model Research Articles

Drivers of the dynamics of the spread of cholera in the Democratic Republic of the Congo, 2000-2018: An eco-epidemiological study.

The dynamics of the spread of cholera epidemics in the Democratic Republic of the Congo (DRC), from east to west and within western DRC, have been extensively studied. However, the drivers of these spread processes remain unclear. We therefore sought to better understand the factors associated with these spread dynamics and their potential underlying mechanisms. In this eco-epidemiological study, we focused on the spread processes of cholera epidemics originating from the shores of Lake Kivu, involving the areas bordering Lake Kivu, the areas surrounding the lake areas, and the areas out of endemic eastern DRC (eastern and western non-endemic provinces). Over the period 2000-2018, we collected data on suspected cholera cases, and a set of several variables including types of conflicts, the number of internally displaced persons (IDPs), population density, transportation network density, and accessibility indicators. Using multivariate ordinal logistic regression models, we identified factors associated with the spread of cholera outside the endemic eastern DRC. We performed multivariate Vector Auto Regressive models to analyze potential underlying mechanisms involving the factors associated with these spread dynamics. Finally, we classified the affected health zones using hierarchical ascendant classification based on principal component analysis (PCA). The increase in the number of suspected cholera cases, the exacerbation of conflict events, and the number of IDPs in eastern endemic areas were associated with an increased risk of cholera spreading outside the endemic eastern provinces. We found that the increase in suspected cholera cases was influenced by the increase in battles at lag of 4 weeks, which were influenced by the violence against civilians with a 1-week lag. The violent conflict events influenced the increase in the number of IDPs 4 to 6 weeks later. Other influences and uni- or bidirectional causal links were observed between violent and non-violent conflicts, and between conflicts and IDPs. Hierarchical clustering on PCA identified three categories of affected health zones: densely populated urban areas with few but large and longer epidemics; moderately and accessible areas with more but small epidemics; less populated and less accessible areas with more and larger epidemics. Our findings argue for monitoring conflict dynamics to predict the risk of geographic expansion of cholera in the DRC. They also suggest areas where interventions should be appropriately focused to build their resilience to the disease.

Identifying Central Negative Thoughts Using Experience Sampling and Network Analysis

PurposeNetwork analysis has promised to inform clinical practice about what should be prioritised in treating different psychological disorders. However, the pure phenomenological approach adopted by network analysis did not help advance this goal considerably. We propose a theoretical approach based on the cognitive model of psychopathology.MethodWe used a multivariate vector-autoregression model to analyse the networks of anxious symptoms, depressive symptoms, and negative automatic thoughts. In a preliminary study, we identified the most common negative thoughts and then monitored them alongside symptoms of anxiety and depression in a sample of undergraduate students three times per day for three weeks.ResultsResults revealed that negative thoughts have a high bridge outdegree in the temporal network (predict the occurrence of symptoms), while symptoms have a high bridge outdegree (are predicted by thoughts). Thoughts related to self-criticism, like “There is something wrong with me”, were the most central for both anxiety and depression and could be considered priority targets for cognitive interventions.ConclusionsAdopting a theoretical approach has proven helpful in providing concrete targets for therapy instead of just identifying central symptoms, as it is typically done in network studies. Future network studies could also consider adopting an approach based on a psychotherapeutic theory about the aetiology of psychopathology.

Anaysis of Interest Rate Spread, Financial Development and Foreign Capital Inflow Nexus in Nigeria

ABSTRACT: The world has gone through various economic crises in the past century, and their effects have had varying implications on the economies of countries all over the world. In a bid to help stabilise economies all over the world, various measures have been put in place. Some of these measures include regulations and policies by the Bretton Woods institutions (World Bank and International Monetary Fund) and their agencies. The idea behind these measures is to ensure sustainable growth and development among member states and other countries of the world. Some of these policies include the privatization and liberalization policies of the Bretton Woods institutions as well as other policies bordering on such economic and social benefits for people all over the world. The Nigerian economy in our contemporary time can be described as passing through a rather 'volatile' phase with respect to interest rate, financial development and foreign capital inflow. Thus, this study investigated the impact of interest rate spread and financial development on foreign capital inflow in Nigeria. Several studies have been carried out using different methodologies, such as; co-integration equation, multivariate vector auto regressive (VAR) model and vector error correction technique. Each methodology used was in line with the objective of the research in question. However, to achieve its objective, this paper adopted the Classical Linear Regression Model. The results of the study showed that financial development has a positive impact on foreign capital inflow, while interest rate spread on the other hand, was also found to have a positive impact on foreign capital inflow. Other control variables such as market capitalization and treasury bills rate were also found to positively affect foreign capital inflow, while the all share index had a negative impact on foreign capital inflow. Some economists have opined that the Nigerian economy is currently going through a phase of recession. In order for large scale investments to be made within the country, there is a need for foreign participants to be attracted to the Nigerian economy and pull their resources, both financial and non-financial into the country (but, not without regulation). However, for these investors to have confidence to make investments in the Nigerian economy, there has to be a high degree of economic stability in the country. This can be supported by enacting and implementing policies that can enhance the quality of interest rates spread and financial development within the country. The policy implication here involves enhancing financial development through sound policies and adopting competitive interest rates, could increase the rate of foreign inflow into the economy. In view of this, it is also recommended that government policies should target interest rate competitiveness, financial and economic development as well as sustainability.

156 Major Depression is Linked to Increased Information Flow From the Orbitofrontal Cortex

INTRODUCTION: Major depression is associated with widespread dysfunction throughout the limbic system and neocortical regions such as the orbitofrontal cortex (OFC) and the dorsolateral prefrontal cortex (dlPFC) (4–7). The brain regions comprising the limbic system, including the amygdala, ventral striatum, and anterior cingulate cortex (ACC), show altered connectivity in MDD patients (5, 7). As part of an ongoing NIH-funded trial of DBS for depression, we implanted 3 patients with both DBS leads and stereo-EEG (sEEG) electrodes to measure neural oscillations in the dlPFC, OFC, and ACC. METHODS: Three depression patients were implanted with sEEG electrodes in areas including OFC, dlPFC, and ACC. We frequently measured depression severity throughout a 9-day inpatient monitoring period using a validated adaptive severity scale (8). We measured directed connectivity using multivariate vector autoregressive models that measure information flow between the right dlPFC, OFC, and ACC during resting state). Then we examined the relationship between depression severity and GC within the delta band (1-3 Hz). RESULTS: Information flow within the delta band was positively correlated with depression severity in each patient. Each showed distinct patterns of pathophysiological connectivity. Across all patients, directed connectivity from the OFC to the ACC predicted depression severity (p < 0.05). CONCLUSIONS: Information flow within the prefrontal cortex correlates closely with depression severity. Increased OFC -> ACC connectivity may relate to increased self-appraisals of mood and diminished control of emotional state during depressive episodes.

Ricardian equivalence or twin deficits hypothesis? Evidence from Serbia

This study investigates empirically whether higher budget deficits worsen the current account balance in Serbia, which is in line with the twin deficit hypothesis.This prediction is very different from Ricardian equivalence theory, which implies that a decrease in public savings is always anticipated via increased private savings. Hence, budget deficits have no impact on the current account balance. Based on quarterly data for the period between 2005 and 2020 and using a multivariate vector autoregression (VAR) model and a short-run structural VAR model, this paper confirms the twin deficit hypothesis in Serbia. More precisely, a 1 percentage point increase in the budget deficit (as a percentage of GDP) generates a 0.31 percentage point increase in the current account deficit (as a percentage of GDP). Moreover, the result is also confirmed using alternative estimation techniques (GMM and OLS method). According to these results, macroeconomic policymakers in Serbia should resort to policies that encourage fiscal consolidation to rectify or at least mitigate deterioration of the current account balance.

Modeling and Forecasting Electricity Demand and Prices: A Comparison of Alternative Approaches

Electricity demand and price forecasting are key components for the market participants and system operators as precise forecasts are necessary to manage power systems effectively. However, forecasting electricity demand and prices are challenging due to their specific features, such as high frequency, volatility, long trend, nonconstant mean and variance, mean reversion, multiple seasonalities, calendar effects, and spikes/jumps. Thus, the main aim of this study is to propose models that can efficiently forecast electricity demand and prices. To this end, the time series (demand/price) is divided into two components. The first component is considered a deterministic component that includes a trend, yearly, seasonal, and weekly periodicities, calendar effects, and lagged exogenous information and is modeled by parametric and nonparametric approaches. The second component is known as a stochastic (residual) component that is estimated using univariate autoregressive (AR) and multivariate vector autoregressive (VAR) models. The estimation of these models is carried out by four different estimation methods, including ordinary least squares (O), Lasso (L), Ridge (R), and Elastic-net (E). The proposed modeling scheme is applied to Nordic electricity demand and price time series, and one-day-ahead out-of-sample forecasts are obtained for a whole year. Besides descriptive statistics, a statistical significance test is also used to evaluate the models’ forecasting accuracy. The results suggest that the proposed methodology effectively forecasts the price and demand for electricity. In addition, the choice of the estimation procedure used for both deterministic and stochastic components has a significant effect on the forecasting results. Furthermore, multivariate vector autoregressive gives superior performance compared to univariate autoregressive models.

Using Econometric Models to Manage the Price Risk of Cocoa Beans: A Case from India

This study aims at developing econometric models to manage the price risk of Dry and Wet Cocoa beans with the help of ARIMA (Autoregressive Integrated Moving Average) and VAR (Vector Auto Regressive). The monthly price of Cocoa beans is collected for the period starting from April 2009 to March 2020 from the office of CAMPCO Limited, Mangalore, and the ICE Cocoa futures price from the website of investing.com. The augmented dickey fuller test is used to test the stationarity of the series. The ACF and PACF correlograms are used to identify the tentative ARIMA model. Akaike information criterion (AIC) and Schwarz criterion (SBIC), Sigma square, and adjusted R2 are used to decide on the optional AR and MA terms for the models. Durbin–Watson statistics and correlograms of the residuals are used to decide on the model’s goodness of fit. Identified optimal models were ARIMA (1, 1, 0) for the Dry Cocoa beans price series and ARIMA (1, 1, 2) for the Wet Cocoa beans price series. The multivariate VAR (1) model found that the US and London Cocoa futures prices traded on the ICE platform will influence the price of Dry Cocoa in India. This study will be helpful to forecast the price of Cocoa beans to manage the price risk, precisely for Cocoa traders, Chocolate manufacturers, Cocoa growers, and the government for planning and decision-making purposes.

Forecasting day-ahead electricity prices: A comparison of time series and neural network models taking external regressors into account

The amount of renewable energies in electricity production has increased significantly in the last decade, resulting in more variability of the day-ahead electricity spot price. The Electricity Price Forecast (EPF) has to adapt to the new situation by applying flexible models. However, the numerous available forecasting methods differ widely, with no distinct candidate offering the best solution. Against this background, we conduct a comparative study of four different approaches to forecasting the German day-ahead electricity spot price. In addition to the prominent Seasonal Integrated Auto-Regressive Moving Average model ((S)ARIMA(X)) and the Long-Short Term Memory (LSTM) neural network models, we employ a Convolutional Neural Network LSTM (CNN-LSTM) and an extended two-stage multivariate Vector Auto-Regressive model (VAR) approach as hybrid models. For better performance, we include common external influences such as the consumer load, fuel and CO2 emission prices, average solar radiation and wind speed in our analysis. We analyse hourly data for twelve samples from October 2017 to September 2018. Each model is implemented to deliver price forecasts at three horizons, i.e., one day, seven days and thirty days ahead. While the LSTM model achieves the best forecasting performance on average, the two-stage VAR follows closely behind and performs exceedingly well for shorter prediction horizons. Further, we provide evidence that a combination of both forecasting methods outperforms each of the single models. This indicates that combining advanced methods could lead to further improvements in electricity spot price forecasts.

Estimating Impulse-Response Functions for Macroeconomic Models using Directional Quantiles

Abstract A multivariate vector autoregressive model is used to construct the distribution of the impulse-response functions of macroeconomics shocks. In particular, the paper studies the distribution of the short-, medium-, and long-term effects after a shock. Structural and reduced form quantile vector autoregressive models are developed where heterogeneity in conditional effects can be evaluated through multivariate quantile processes. The distribution of the responses can then be obtained by using uniformly distributed random vectors. An empirical example of exchange rate pass-through in Argentina is presented.

Analysis of the impact of electric vehicle policy on supply and demand of U.S. states bioethanol market

This study aims to construct a quantitative model of the dynamic influence of electric vehicle sales in the United States on bioethanol consumption and bioethanol production. The final research results have shown that there is a long-term equilibrium relationship in our multivariate vector autoregression (VAR) model between bioethanol consumption and bioethanol production, gasoline price, corn price, and electric vehicle sales. It can be seen from the impulse response analysis that ethanol demand will strongly respond to one standard deviation of electric vehicle sales, and our new model fits the real data of ethanol supply and demand deviation well. Based on this newly long-term equilibrium relationship, we have constructed a policy simulation model to describe the quantitative relationship between the changes in the electric vehicle policy of each state and the temporal and spatial pattern of the national bioethanol resource allocation among US states. It can help and serve as a reference tool for policy makers to conduct quantitative policy evaluation.

An Empirical Relationship Between Real Effective Exchange Rate Volatility and Current Account Balances in Kenya

Deficits in the current account balances have been a common phenomenon in Kenya. Some scholars have attributed this trend to the volatility of the Kenyan shilling. Previous literatures on this issue have generated mixed results on the relationship between Kenya’s real effective exchange rate (REER) volatility and current account balances, and as such this study sought to resolve this controversy. This study investigated and mapped out the profile of Kenya’s REER volatility for the period 1972 to 2015 and interrogated its impact on current account balances (CAB) to fill the gap. The study was guided by Mundell-Fleming-Dornbusch model to measure REER volatility and the elasticity approach to assess the relationship between REER volatility and CAB. The study employed GARCH technique to measure REER volatility and Vector Autoregressive (VAR) model to examine the impact of REER on Current Account Balances. The study adopted Quantitative Research Design.  It relied on secondary data sourced from World Bank and International Monetary Fund. Unit root tests reported stationarity when variables were first differenced, while Cointegration test results based on Johansen’s maximum-likelihood procedure indicated that there is no evidence of cointegration among variables under consideration. The results of GARCH revealed that REER was indeed volatile (t-statistic 32.90 > 1.96). The results of multivariate VAR model analysis indicated that REER (-1) volatility impacted positively and insignificantly on CAB, while REER(-2) had a negative and insignificant effect on CAB for the sampled period. Findings of this study will help in the formulation of fiscal and monetary policies to address macro-economic shocks associated with REER volatility in the Kenyan economy. In light of these results, there is need to apply appropriate macroeconomic policy mix in the short run to mitigate the cyclical and short-term shocks that arise from REER volatility and correct the country’s unsustainable external imbalances in the long run.  

Predicting total electron content in ionosphere using vector autoregression model during geomagnetic storm

Abstract The ionospheric total electron content (TEC) severely impacts the positional accuracy of a single frequency Global Positioning System (GPS) receiver at the equatorial latitudes. The ionosphere causes a frequency-dependent group delay in the GPS-ranging signals, which reduces the receiver’s accuracy. Further, the variations in TEC due to various space weather phenomena make the ionosphere’s behaviour nonhomogeneous and complex. Hence, developing an accurate forecast model that can track the dynamic behaviour of the ionosphere remains a challenge. However, advances in emerging data-driven algorithms have been found helpful in tracking non-stationary behavior in TEC. These models help forecast the delays in advance. The multivariate Vector Autoregression model (VAR) predicts the Ionospheric TEC in the proposed model. The prediction model uses input data compiled in real-time from the lag values of incoming TEC data and features extracted from TEC. The TEC is predicted in real-time and tested for different prediction intervals. The metrics – Mean Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) are used for testing and validating the accuracy of the model statistically. Testing the predicted output accuracy is also done with the dynamic time warping (DTW) algorithm by comparing it with the actual value obtained from the dual-frequency receiver. The model is tested for storm days of the year 2015 for Bangalore and Hyderabad stations and found to be reliable and accurate. A prediction interval of twenty-minute shows the highest accuracy with an error within 10 TECU for all the storm days.

Twin deficits hypothesis in Bangladesh: an empirical investigation

PurposeThis study attempts to examine the twin deficits hypothesis for Bangladesh. Along with the traditional twin deficits hypothesis associated with the current account and fiscal deficit, the paper also explores the causal relationship between the trade deficit and fiscal deficit.Design/methodology/approachWe start with the investigation of the conventional twin deficit hypothesis employing autoregressive distributed lag (ARDL) bounds testing approach in a multivariate framework. Due to the absence of cointegration between the budget deficit and trade deficit, the study adopts a multivariate vector autoregressive (VAR) model to analyze the nexus.FindingsThe study supports the presence of the twin deficits hypothesis in Bangladesh, both in the short run and long run. Unidirectional causation running from the budget deficit to the current account deficit in the long run. The trade model also supports the twin deficit hypothesis, like the aforementioned current account model.Practical implicationsTherefore, the sustainable fiscal deficit is the key to maintain a stable current account deficit and trade deficit in Bangladesh.Originality/valueThe study incorporates the country risk indicators to address the governance issue while analyzing the models' deficit scenarios because good governance is an integral part of explaining the development outcome and failure of a country like Bangladesh.

Inflation forecasting in an emerging economy: selecting variables with machine learning algorithms

PurposeThe main purpose of this study is to forecast inflation rates in the case of the Turkish economy with shrinkage methods of machine learning algorithms.Design/methodology/approachThis paper compares the predictive ability of a set of machine learning techniques (ridge, lasso, ada lasso and elastic net) and a group of benchmark specifications (autoregressive integrated moving average (ARIMA) and multivariate vector autoregression (VAR) models) on the extensive dataset.FindingsResults suggest that shrinkage methods perform better for variable selection. It is also seen that lasso and elastic net algorithms outperform conventional econometric methods in the case of Turkish inflation. These algorithms choose the energy production variables, construction-sector measure, reel effective exchange rate and money market indicators as the most relevant variables for inflation forecasting.Originality/valueTurkish economy that is a typical emerging country has experienced two digit and high volatile inflation regime starting with the year 2017. This study contributes to the literature by introducing the machine learning techniques to forecast inflation in the Turkish economy. The study also compares the relative performance of machine learning techniques and different conventional methods to predict inflation in the Turkish economy and provide the empirical methodology offering the best predictive performance among their counterparts.

An empirical analysis of the FDI and economic growth relations in Albania: A focus on the absorption capital variables

Over the past three decades, Albania has had positive and increasing foreign direct investment (FDI) inflows that have brought significant changes in many economic sectors. The paper’s purpose is to analyze the dynamic relationship between FDI and economic growth, particularly emphasizing absorption capital variables. The research question is if the human capital development level, technological development, trade openness, public expenses, and financial system development in Albania help or hinder the materialization of the expected positive effect of FDI on economic growth? We used empirical analyses to evaluate these relationships based on the model created by Borensztein, De Gregorio, and Lee (1998). We changed a few variables in the model, and we used the multivariate vector autoregressive (VAR) model and the vector error correction model (VECM) to analyze the variables’ causal relationships. Some of the results achieved are consistent with other authors’ findings, so human capital is considered an essential element of host countries’ absorptive capacity. In the long run, in Albania, the FDI’s impact on economic growth positively affects human capital development, especially on knowledge and expertise and financial system development. However, the technological difference index gives a negative long-term impact on economic growth, and trade opening is statistically insignificant.

Turn to the Internet First? Using Online Medical Behavioral Data to Forecast COVID-19 Epidemic Trend

The surveillance and forecast of newly confirmed cases are important to mobilize medical resources and facilitate policymaking during a public health emergency. Digital surveillance using data available online has increasingly become a trend with the advancement of the Internet. In this study, we assessed the predictive value of multiple online medical behavioral data, including online medical consultation (OMC), online medical appointment (OMA), and online medical search (OMS) for the regional outbreak of coronavirus disease 2019 in Shenzhen, China during January 1, 2020 to March 5, 2020. Multivariate vector autoregression models were used for the prediction. The results identified a novel predictor, OMC, which can forecast the disease trend up to 2 days ahead of the official reports of confirmed cases from the local health department. OMS data had relatively weaker predictive power than OMC in our model, and OMA data failed to predict the confirmed cases. This study highlights the importance of OMC data and has implication in providing evidence-based guidelines for local authorities to evaluate risks and allocate resources during the pandemic.

Did Diversification Impact Economic Growth in Nigeria in the Last 20 Years of Democratic Government (1999–2019)? A Vector Error Correction Model

Diversification of the Nigerian economy from oil-based to other non-oil sectors has become a recurrent economic solution to the growing challenges associated with the Nigerian economy. For the past 20 years of uninterrupted democratic government in Nigeria, the successive federal governments have focused on the development of the agricultural sector as a credible option for diversification, partly for the past positive roles of the agricultural sector in the Nigerian economy before the discovery of oil. Using the multivariate vector autoregressive (VAR) model on the data obtained from 1999 to 2019, this study applied the vector error correction (VEC) model to determine the impacts of diversification of the Nigerian economy on economic growth, focusing on the manufacturing and the agricultural sectors. To determine the underlying impact of the democratic experience in Nigeria with diversification, we utilised the political rights of the population as a proxy variable. The empirical results showed that there exists cointegration among the variables used to represent the manufacturing and the agricultural sectors, political rights, and per capita gross domestic product (GDP) growth rate within the Nigerian economy. The manufacturing sector has a positive impact on the growth of the Nigerian economy; however, the agricultural sector and the political rights of the Nigerian people have adverse effects on the real GDP growth rate, in the short run. The Granger Causality tests found no evidence of causality among the variables. This study concludes that the diversification policy of the Nigerian government should be multi-faceted and that the political rights of the population are essential for the realisation of the diversification goal. Keywords: Diversification, Nigeria, GDP, Cointegration. DOI: 10.7176/JESD/11-22-02 Publication date: November 30 th 2020

Multivariate Time Series Analysis to Impact Assessment of Nominal Economic Indicators on Federal Government Revenue and Economic Growth in Nigeria

Nigeria has all it takes to become Sub-Saharan Africa’s largest economy and a key contributor in the global economy as a result of her agricultural and natural resource endowment. Unfortunately, these resources are not sufficiently and efficiently utilized, resulting to some negative economic implications such as underdeveloped economy in world ranking, high unemployment status, poor standard of living, abject poverty, devaluating currency, lack of technological innovations and unstable economy among others. This paper investigates the impact of the Oil and Non-Oil producing sectors to Federal Government Revenue and Economic Growth in Nigeria, using the concept of multivariate time series vector autoregressive (VAR) model. The basic concepts and techniques of multivariate time series modelling and analysis such as Granger Causality tests; Lag Length Selection based on Information Criteria; Augmented Dickey-Fuller and Phillips-Perron Unit Root tests; Kwiatkowski-Phillips-Schmidt-Shin (kpss) Trend & Level Stationarity tests; Sectoral Contributions to Federal Government Revenue, Economic Growth and Growth Rates using Line & Bar Graphs are discussed in detail. The study applied a model for predicting annual Federal Government Revenue and Economic Growth at any time using their respective historical monetary values together with the historical monetary values of the other economic indicators. The empirical findings in this paper revealed that the Federal Government Revenue and Economic Growth are predictable using their historical values together with the historical values of the other indicators. Furtherance to the statistically significant contribution of both the Oil and Non-Oil producing sectors, it is imperative to diversify the Nigerian economy in order to boost Federal Government Revenue, improve the standard of living, alleviate poverty, create employment opportunities and accelerate a long-run sustainable Economic Growth and stability.

Risk Interactions of Coronavirus Infection across Age Groups after the Peak of COVID-19 Epidemic.

Background: The COVID-19 pandemic has incurred significant disease burden worldwide, particularly on the elderly population. This study aims to explore how risks of coronavirus infection interact across age groups using data from South Korea. Methods: Daily new COVID-19 cases from 10 March to 30 April 2020 were scraped from online open sources. A multivariate vector autoregressive model for time series of count data was used to examine the risk interactions across age groups. Case counts from previous days were included as predictors to dynamically examine the change of risk patterns. Results: In South Korea, the risk of coronavirus infection among elderly people was significantly affected by other age groups. An increase in virus infection among people aged 20–39 was associated with a double risk of infection among elderly people. Meanwhile, an increase in virus infection among elderly people was also significantly associated with risks of infection among other age groups. The risks of infection among younger people were relatively unaffected by that of other age groups. Conclusions: Protecting elderly people from coronavirus infection could not only reduce the risk of infection among themselves but also ameliorate the risks of virus infection among other age groups. Such interventions should be effective and for the long term.

Network connectedness of green bonds and asset classes

We identify network connectedness between green bonds and different asset classes over different investment horizons in the EU and US asset markets. We first focus on dynamic correlations between green bond and asset class price changes in different time scales on the basis of wavelet coherence. We then identify network connectedness by decomposing time series into different frequency components, in each of which we evaluate connectedness on the basis of the error variance decomposition of a multivariate vector autoregressive model. Our empirical evidence reveals strong connectedness between green bonds and treasury and corporate bonds in the short and long run and in both the EU and the USA, with green bonds receiving sizeable spillovers from treasury and corporate bond prices and transmitting negligible effects. Likewise, we find that green bonds are weakly connected with high-yield corporate bond, stock and energy assets over different time scales. These findings have implications for green bond investors regarding portfolio design and hedging decisions, and for the channelling of financial flows to economic activities that are consistent with a decarbonized economy.