Global Vector Autoregressive Model Research Articles

Spillovers between cryptocurrencies and financial markets in a global framework

We employ the Bayesian Global Vector Autoregression (BGVAR) model to examine the transmission of adverse shocks originating in the cryptocurrency market to global financial markets. The analysis shows that these spillover effects are not limited to a specific group of countries but are instead global in nature. The results indicate that shocks originating in the cryptocurrency market adversely affect stock markets, bond indices, exchange rates, and volatility indices. These shocks, while typically moderate in magnitude and short in duration, suggest that cryptocurrencies act as mediators of short-term negative shocks. The study also underscores the heterogeneous nature of these impacts across different financial markets and countries, highlighting the varying sensitivities and responses to cryptocurrency market fluctuations. Importantly, this research represents the first application of the GVAR model in the context of the cryptocurrency market, to the best of our knowledge.

Global uncertainty and exchange rate conditions: Assessing the impact of uncertainty shocks in emerging markets and advanced economies

Economic and financial conditions respond significantly to increased measures of uncertainty, particularly in economic policy, monetary policy, and financial markets. This research employs the global vector autoregression (global VAR) model to assess the response of bilateral exchange rates, exchange rate volatility, exchange market pressure, and nominal and real effective exchange rates to shocks in global economic and financial uncertainty, along with U.S. monetary policy uncertainty, U.S. equity market volatility, and U.S. financial stress indicators. Spikes in uncertainty correspond to immediate increases in exchange rate volatility, buildup of exchange market pressure, and depreciation for emerging markets. The results are compared to advanced economies, China, and Russia separately, identifying key differences across markets.

Does fintech matter for financial inclusion and financial stability in BRICS markets?

We investigate whether fintech development expedites financial inclusion and affects the stability of the financial sector in BRICS economies. We first seek to identify the linkage between Fintech development and financial inclusion in the BRICS economies. We then explore if Fintech poses any threat to financial stability by studying the impact of Fintech on three main factors of financial stability; Country risk, Liquidity, and Price volatility to see any possible threat to financial stability. We apply the robust Global Vector Autoregressive (GVAR) model with Bayesian framework to analyze a monthly dataset ranging from 2015 to 2022. By applying a positive shock to Fintech over financial inclusion and financial stability, we reveal a positive dynamic relationship between fintech development and financial inclusion, with fintech exhibiting a long-term influence on financial inclusion in some BRICS countries than others. However, we found no significant evidence that fintech presents any threat to financial stability; at least not in the short term. We offer several policy implications and future research directions.

Assessing the baseline model of WTI oil and stock returns under financial volatility and spillover effects

Do financial volatility shocks matter for WTI oil and stock markets? They should since world economic activity responds to volatility. To investigate this question, we revisit two empirical frameworks widely used in the literature. Our baseline involves the standard identification of oil shocks in structural vector autoregressions (SVAR) with (world) oil supply, demand, and real oil prices. We then propose a global vector autoregressive (GVAR) model integrating 14 major world economies. We introduce the volatility measure (VIX), various stock markets, and compare two oil demand proxies: real economic activity (REA) based on commodity prices and world industrial production (WIP). The results of the impulse response analysis do not indicate major differences across models. However, when measuring the share of oil price variance explained by shocks to VIX (or to U.S. high-bond corporate yield) SVAR overestimates the influence of VIX. We find (world) demand shocks explain a larger part of the variance of oil and stock markets compared to supply shocks. In SVAR, new information coming from a one standard deviation shock to VIX is incorporated negatively and instantaneously on real oil prices: −1.6 for REA and −1.4 for WIP. In GVAR these are reduced slightly to −1.2 from VIX and to −0.6 if coming from corporate bond yield shocks. Our study highlights the sensitivity of econometric results to distinct approaches: one that identifies shocks and another that captures spillover effects.

Commodity markets and the global macroeconomy: evidence from machine learning and GVAR

Based on a strongly data-intensive machine learning approach, this study first identifies the most essential globally traded commodities in view of their role for the global macroeconomic performance. At the second stage we estimate a global vector autoregressive model to assess in more detail these global reactions. Our results from the first stage indicate that of the 55 analyzed commodity markets, only four are revealed as the most important. At the second step, our GVAR analysis indicates that the commodity market effects on macroeconomic activity are neither unanimous across the commodities nor across macrovariables. As an overall result, the commodity market exposure is clearly stronger among the advanced countries such as the euro area, other developed economies, and China, compared to the emerging economies of Africa, Asia, and Latin America, at both the country and regional levels. This puts a lot of pressure on economic policies aimed at reducing, e.g., the depriving effects of commodity market price development on aggregate economic performance of these countries.

Spatial and temporal diffusion of property prices and sales across British regions

Purpose The purpose of this study is to reveal the dynamics of house prices and sales in spatial and temporal dimensions across British regions. Design/methodology/approach This paper incorporates two empirical approaches to describe the behaviour of property prices across British regions. The models are applied to two different data sets. The first empirical approach is to apply the price diffusion model proposed by Holly et al. (2011) to the UK house price index data set. The second empirical approach is to apply a bivariate global vector autoregression model without a time trend to house prices and transaction volumes retrieved from the nationwide building society. Findings Identifying shocks to London house prices in the GVAR model, based on the generalized impulse response functions framework, I find some heterogeneity in responses to house price changes; for example, South East England responds stronger than the remaining provincial regions. The main pattern detected in responses and characteristic for each region is the fairly rapid fading of the shock. The spatial-temporal diffusion model demonstrates the presence of a ripple effect: a shock emanating from London is dispersed contemporaneously and spatially to other regions, affecting prices in nondominant regions with a delay. Originality/value The main contribution of this work is the betterment in understanding how house price changes move across regions and time within a UK context.

International financial stress spillovers during times of unconventional monetary policy interventions

In this study, we estimate a Bayesian global vector autoregressive model to uncover the effects of financial stress on output growth, inflation, and interest rates, accounting for several advanced and emerging economies for a period spanning from February 2008 until May 2022. We construct a financial stress index applicable to all countries, tracking periods of financial instability in the economies, and employ shadow short rates as a proxy measure of unconventional monetary policy. This study provides strong evidence that financial stress shocks are transmitted abroad as financial stress increases in all the countries in the sample. Our results also show that financial stress innovation generates important domestic and cross-border output, inflation, and interest rate spillovers for several countries. Additionally, we identify the active role of the financial and bank credit channels in the transmission of shocks across financial systems, while macroprudential policy can intercept the propagation of the shock. Our results carry policy implications for monetary and regulatory authorities.

A New Look at Cross-Country Aggregation in the Global VAR Approach: Theory and Monte Carlo Simulation

AbstractRequirements to understand and forecast the behavior of complex macroeconomic interactions mandate the use of high-dimensional macroeconometric models. The Global Vector Autoregressive (GVAR) modeling technique is very popular among them and it allows researchers and policymakers to take into account both the complex interdependencies that exist between various economic entities and the global economy through the world’s trade and financial channels. However, determining the cross-section unit size while using this approach is not a trivial task. In order to address this issue, we suggest an objective procedure for the detection of the size of the cross-country aggregation in GVAR models. While doing so, we depart from the Akaike Information Criterion (AIC) and propose an analytical modification to it, mainly employing an ad hoc approach without violating Akaike’s main principles. To supplement the theoretical results, small sample performances of those procedures are studied in Monte Carlo experiments as well as implementing our approach on real data. The numerical results suggest that our ad hoc modification of AIC can be used to determine the structure of the cross-section unit dimension in GVAR models, allowing the researchers and policymakers to build parsimonious models.

China’s macroeconomic performance affects trading partners: How can their policies respond?

Utilizing expanded data analysis, this study aims to determine how China's macroeconomic performance affects the global economy. Employing the Global Vector Auto Regression (GVAR) model, we investigate the impact of China's macroeconomic variables on the economies of its selected trading partners throughout the world. For 2000Q1–2019Q4, developed countries (the US, Hong Kong), developing Europe countries (Russian Federation and Poland), and two developing Asia countries (Vietnam and India) show that Chinese macroeconomic performance has a greater impact than middle east and central Asia, Sub-Saharan Africa, and Western hemisphere. The Chinese GDP shock hits emerging Europe harder because of its extensive Russian production network. Results reveal that Chinese depreciation impacts developing Europe and Asia, while the Chinese stock market shock hits advanced economies more. Advanced and developing Asian economies are greatly affected by TV shock. Chinese FDI affects the Middle East and Central Asia, because of the China-Pakistan Economic Corridor. China trading partners use macroeconomic statistics to assess their economic priorities, industrial strengths, and weaknesses to create national interest plans. Currency adjustment, trade policies, domestic stimulus, and market diversification can minimize negative effects or capitalize on possibilities from China's macroeconomic conditions.

The three co’s to jointly model commodity markets: co-production, co-consumption and co-trading

In this study, we develop a framework, based on a global vector autoregression (GVAR) model, to unite two perspectives on commodity markets, the commodity-specific, single-market-centered approach, investigating the micro- and macroeconomic drivers of commodity prices, and the market perspective, which observes joint movements of commodity prices on exchanges. Thereby, the GVAR model disentangles single market from inter-market effects, while simultaneously accounting for the impact of macroeconomic factors. We apply the framework to the six industrial metals markets, reflecting their interdependencies via their co-production, co-consumption, or co-trading relation. In particular, the numerous significant spillover effects in the cross-commodity dimension underline the importance of jointly modeling commodity markets. While the strong co-movement between industrial metal prices is represented exceptionally well by our framework, the microeconomic supply and demand attributes of the commodities have significant impact, within and across markets, even on price variables, highlighting their relevance in modern commodity market models. Moreover, we detect global shocks, e.g., an increase in global demand, affect each commodity market to a similar extent.

The Effect of US Uncertainty Shock on International Equity Markets: The Role of the Global Financial Cycle

We contribute to the literature on the international propagation of uncertainty shocks with a Global Vector Autoregressive (GVAR) model that quantifies the spillover effects of uncertainty shocks in the US on to real equity prices of 32 advanced and emerging countries (besides the US). In this regard, we also account for the role of global financial market conditions in the propagation of these shocks, using high and low values of the Global Financial Cycle (GFCy) index. Using quarterly data over 1980:1 to 2019:2, our findings reveal greater response of advanced markets than emerging counterparts to an US uncertainty shock. Further, we show consistent higher negative responses during weak financial conditions than otherwise, confirming the intervening role of the GFCy index. Our results have important implications for investors and policymakers.

Macro Credit Risk Stress Testing in Tanzanian Banking Sector: A Global Vector Autoregressive (GVAR) Approach

ABSTRACT This study conducted a macro credit risk stress testing framework for assessing the impact of global transmissions on Tanzania’s banking sector from Q12006 to Q42020, using the Global Vector Autoregressive Regression (GVAR) model with seven countries. The adoption of the GVAR model enabled us to take into account non-linear relationships, capture business cycle effects and the global interaction. This study focused on assessing the projected responsiveness of Tanzania’s credit risk to shocks originating from domestic, global and foreign variables in relation to USA, China and South Africa. This study found that the global transmissions have an impact on the Tanzanian banking sector, with a significant exposure witnessed from the increase in the crude oil price index. On a domestic level, the results revealed that projected inflation rate shocks have a more responsive impact on banking sector credit risk compared to real GDP growth. In addition, GDP growth shocks from foreign countries have a slight impact on the banking sector compared to exchange rate risk transmission, given the growing global interconnectedness, thus leading to a spillover effect on the banking sector. Conclusively, Tanzania’s banking sector is not invulnerable to domestic, foreign and global shocks, and therefore warrants monitoring to enhance resilience.

New evidence on sources of macroeconomic fluctuations in sub‐Saharan African countries

AbstractFollowing a global vector autoregressive (GVAR) approach, this paper presents new evidence on the validity of international transmission of economic shocks from key trading partners as sources of macroeconomic fluctuations in sub‐Saharan African (SSA) countries. The GVAR model was estimated for 21 SSA countries grouped into three country classes—oil‐rich, other‐resources‐rich and non‐resource‐based economies, to account for output shocks from crucial trading partner countries—United States, United Kingdom, China and Europe. Furthermore, the generalized forecast error variance decompositions results reveal that output shocks from key trading partners constitute significant contributors to changes in key macroeconomic indicators—real gross domestic product, inflation, exchange rate and short‐term interest rate, in the SSA region. The generalized impulse response functions indicate that these economic shocks have more significant impacts on oil‐rich countries than on other country groups. A key recommendation from this study is that SSA countries, especially the resource‐rich economies, need to strengthen and diversify their economic structure, including the trade basket.

The cross-border interconnectedness of shadow banking

Shadow banking has shown a consistent growth in global financial markets, raising concerns about its cross-border interconnectedness and the potential transmission of financial shocks. Previous financial crises have highlighted the risks associated with interconnectedness, underscoring the need for a comprehensive analysis of shadow banks’ international linkages. Using a global vector autoregressive model and data from twelve countries for 2000–2020, this study examines the international interconnectedness of shadow banking. The empirical findings demonstrate that shadow banking is internationally interconnected and that these interconnections are statistically associated with funding conditions in the global money and capital markets. Specifically, cross-border statistical associations exist between shadow bank assets, short- and long-term interest rates, and equity prices in many countries, as well as with the US risk premium and term spread. Hence, shadow banks are significantly exposed to cross-border shocks and can potentially transmit and propagate these shocks.

Industry effects of unconventional monetary policy, within and across countries

While conventional monetary policy causes differential impacts on industry output, how unconventional monetary policy affects industries across countries is as of yet unknown. This paper characterizes the effects of unconventional monetary policy on domestic industry output and spillover effects on foreign industry output between the US, the UK, and Japan. I set up a Bayesian global vector autoregressive model and identify monetary policy shocks using a sign restrictions identification. I find that the effects on output have substantial heterogeneity both within a country and across countries, however, the pattern of the industry-level output responses within a country and across countries are similar to each other. Regression analysis indicates that industries with lower working capital and larger firm size are associated with a large industry output response to unconventional policy, indicating the relevance of the interest rate channel and portfolio balance channel. Overall, unconventional policy can be used as another tool in the policymakers’ toolbox with similar industry impacts as conventional policy but with somewhat different transmission mechanisms.

Economic Policy Uncertainty: Cross-Country Linkages and Spillover Effects on Economic Development in Some Belt and Road Countries.

With the increase of economic environment uncertainty, it is of great importance to study the linkage and spillover effects of economic policy uncertainty among countries. Especially, this article selects eight countries along the Belt and Road as the core countries (China, Korea, Croatia, India, Russia, Greece, Pakistan, and Singapore) and four countries (Germany, France, Japan, and UK) as the peripheral countries, and then copula technique and mixed-frequency global vector autoregressive model are employed to analyze the correlation and the spillover effect of the economic policy uncertainty (EPU) for the twelve selected countries, respectively. The proposed empirical findings show clearly that the EPU correlation among the eight core Belt and Road countries is stronger and the spillover effect of the core countries to the peripheral countries is statistically significant. As a result, for harmonious and win-win development, the Belt and Road countries should pay a close attention to the EPU, because the stability of the EPU promotes greatly the economy development.

Assessment of the Effects of Monetary Policy Shocks from ECOWAS Member Countries to Nigeria

The paper evaluated the effects of monetary policy shocks from ECOWAS Member Countries to Nigeria using a combination of quarterly, annual time-series and panel data spanning from 1995–2021. The Standard Global Vector-Autoregressive (GVAR) Model of analysis was used to draw structural inference for the study using data on: consumer prices, international commodity prices (oil, agricultural raw material and metal ore), exchange rate, interest rate (short and long run), real output, money supply, and export diversification index. The result of the Impulse Response Function (IRF) revealed that shocks resulting from monetary policy transmission from the ECOWAS member Countries to Nigeria has diverse effects on Nigeria and is largely determined by the extent of monetary policy accommodative stance. The shock that may occur is likely to be country-specific because ECOWAS is an economic union that hardly has the foundation to share common economic or monetary risk. Additionally, the extent of trade flow between Nigeria and the rest of ECOWAS, determines the tendency that such shocks affect Nigeria. Finally, whether ECOWAS is a shock source, or a channel of transmission determines how much of an impact shocks from that region will have.

A network GVAR model for analyzing maritime transport demand: evidence from freight relationships among Greek ports

The aim of this study is to analyze the freight relationships between three major mainland Greek ports (Piraeus, Thessaloniki and Volos), based on containers’ transportation. The methodology used is the Global Vector Autoregressive (GVAR) model. The empirical analysis is based on time series data spanning the period January 1998–December 2012. The most important finding is that a shock in one port doesn’t have, in general, a significant impact on any other port and the corresponding effects settle down relatively quickly, usually in less than two months.

Estimation of Russia's GDP Losses Due to Sanctions Using the Global Vector Autoregression Model

Using mathematical and statistical methods, the authors made an attempt to estimate the losses in the domestic economy under the impact of the imposed sanctions. As a measurement tool is proposed a global vector autoregression model. In this model, the dynamics of GDP is considered as the main performance characteristic, and the rate of change in foreign trade turnover with key partners is used as the key prerequisites for the forecast. The use of this model building scheme is explained by the fact that the economic sanctions imposed on Russia have a negative impact on domestic macroeconomic indicators, and this happens through a variety of channels, key ones being the restriction of external demand and supply, leading to a decrease in trade turnover with the main trading partners, and change in oil revenues. This approach takes into account the relationship between the economies of different countries and their dependence on global markets. To obtain quantitative estimates, conditional forecasts are used as part of scenario analysis.According to the authors’ approach the losses from economic sanctions are measured by the decrease in the volume of Russia's foreign trade with key foreign trade partners, as well as by the discount for Russian oil compared to its global price. In addition to the forecast estimates, the validity of the approach is proofed with the forecast for the crisis period of 2014–2015 as an example. Based on the analysis of the current situation in foreign trade, Russian GDP in 2022 in the baseline scenario may fall by 5.4%, but with some reorientation of trade a decrease may be around 3.7%.

Macro‐financial effects of monetary policy easing

AbstractThis study examines the impact of Fed monetary policy shocks on financial stress, inflation, and output growth, considering several advanced and emerging economies. To that end, it uses shadow rates as a proxy measure of unconventional monetary policy actions and a financial stress index as an indicator of financial instability. We apply a global vector autoregressive model and present the outcome of impulse response functions and forecasts of macroeconomic and financial stress variables. Our results show that US output and inflation are positively affected in response to a domestic negative monetary policy shock, whereas financial stress is unresponsive to the same shock. This policy also has international effects, as we find significant spillovers with the US interest rate shock impacting inflation abroad. The findings of this study are useful to policymakers and investors.