Vector Autoregression Research Articles

Oil price shocks and bond risk premia: Evidence from a Panel of 15 Countries

We study the effect of oil price shocks on bond risk premia. Based on Baumeister and Hamilton (2019), we identify the different sources of oil price shocks using a structural vector autoregressive (SVAR) model of the global market for crude oil. These structural factors are then used as unspanned factors in an affine term structure model based on the representation of Joslin et al. (2014). This is done for a total of 15 countries. Unspanned factors are responsible for most of the variability in bond risk premia for short holding periods, while spanned factors dominate the variance decomposition for longer holding periods. In both cases, global oil supply and global economic activity are clearly the most important unspanned shocks. A historical decomposition around the outbreak of the COVID-19 crisis shows the clear influence of global economic activity shocks during the months of February and March 2020, increasing bond risk premia significantly.

Community-based economic romance and integration: assessing the feasibility of a currency union in South Asia

Purpose This purpose of this study aims to critically evaluate the feasibility of establishing a single currency area within the South Asian Association for Regional Cooperation (SAARC) by examining the economic integration of its member states. The analysis focuses on the extent to which the region meets the criteria of the optimum currency area (OCA) theory, particularly in terms of business cycle synchronization, labor mobility and capital flows. Design/methodology/approach Using a vector autoregression (VAR) model within the aggregate demand-aggregate supply framework, this research investigates the symmetry of supply and demand shocks across SAARC economies. The study analyzes the synchronization of business cycles and the mobility of labor and capital to determine the readiness of SAARC for a unified currency. Findings The results indicate significant asymmetries in business cycles among SAARC countries, with substantial disparities in economic responses to shocks. These findings suggest that the region lacks the necessary economic synchronization required for a successful single currency area. Limited labor and capital mobility further complicate the potential for economic integration within SAARC. Research limitations/implications The study is constrained by data inconsistencies and the limited range of economic indicators available for SAARC countries. Future research should expand the analysis to include a broader set of socioeconomic factors and more comprehensive data sets to better assess the region’s potential for monetary integration. Practical implications The study highlights the challenges of forming a currency union in South Asia due to economic disparities and limited mobility. However, gradual steps toward deeper regional integration, improved financial infrastructure and enhanced cross-border collaboration could foster long-term economic stability, growth and social cohesion in the SAARC region. Social implications The research highlights the potential social benefits of enhanced economic integration, such as increased community resilience and social cohesion, while also warning of the risks associated with premature monetary union in a region with significant economic disparities. Originality/value This study provides a detailed analysis linking the theoretical framework of the OCA to the practical realities of economic integration in South Asia. By focusing on the specific economic conditions of SAARC member states, the research offers valuable insights for policymakers considering regional monetary integration.

Results of Economic Tendency Surveys in Nowcasting GDP Growth

The article proves the usefulness of including aggregate information from economic tendency surveys in the system of nowcasting of economic growth. The authors compare the efficiency of vector autoregressive models that include the economic sentiment indicator based on the results of Rosstat business activity and consumer expectations surveys, and the business climate indicator based on the results of the Bank of Russia enterprise monitoring.Statistical testing of time series of composite indicators and GDP volume index confirmed a significant correlation and causality between them, as well as the similarity of their cyclical profiles. Both indices pass cyclical turning points synchronously or ahead of the GDP volume index, their predictive capabilities are enhanced by the early availability of information. The capabilities of GDP growth nowcasting using survey information are assessed in three specifications of the vector autoregressive model with dummy variables. On the in-sample period, the model including two composite indicators and the GDP volume index demonstrates the highest accuracy.The proposed approach allows us to obtain flash estimates of GDP growth prospects that are significantly ahead of the official statistical information

Costs of Adjustment, Portfolio Separation, and the Dynamic Behavior of Bank Loans and Deposits – Revised

AbstractWe revise the solutions of the dynamic optimization model of Elyasiani, Kopecky, and Van Hoose (1995), and show that the optimal policy function for the case of adjustment costs and no portfolio separation is a vector autoregressive model of order 1, and not of order 2. In addition, the empirical approach of Elyasiani, Kopecky, and Van Hoose (1995) is incorrect. The optimal policy functions for the no portfolio separation case have to be estimated with a panel vector autoregression model and not with a simultaneous equation approach. We also describe how the portfolio separation hypothesis can be tested empirically based on the correct policy functions.

Investor attention and market activity: evidence from green cryptocurrencies

Purpose This paper aims to investigate the relationship between investor attention and market activity (return, volatility and volume) using a sample of 14 clean energy cryptocurrencies (hereafter green cryptocurrency), namely, Chia, Cardano, Stellar, Tron, Ripple, Nano, IOTA, EOS, Bitcoin Green, Alogrand, Hedara, Polkadot, FLOW and Tezos. Design/methodology/approach This paper use 26040 crypto-day observations and a range of econometric techniques, including Dynamic Granger causality, Panel vector autoregression (VAR), Impulse response function and the decomposition of forecast error variance. Findings Based on 26040 crypto-day observations, this paper finds a bidirectional Granger causal relationship between investor attention and all measures of market activity, namely, return, absolute volatility, squared volatility and volume. The panel VAR and impulse response function demonstrate that market activity in the green crypto ecosystem, especially volatility and volume, is considerably responsive to changes in investor attention proxied by Google search volume (hereafter Google search volume (GSV)). The findings also demonstrate a significant asymmetric effect of return and volume on investor attention since past negative shocks “or bad news” in return and volume are more likely to grab the investor’s attention. All in all, our study emphasizes the crucial role of investor attention in the green crypto ecosystem. Originality/value (i) The research is the first to shed light on investor attention in the green cryptocurrency market. (ii) The paper uses a wide range of green cryptocurrencies to offer a comprehensive picture of the green cryptocurrency ecosystem. (iii) This paper is the first to use the panel Granger causality to investigate investor attention in the cryptocurrency market which provides several advantages over the conventional Granger causality approach. (iv) This paper is the first to provide novel empirical evidence on the prevalent influence of investor attention in the green crypto market.

Fiscal Policy, International Spillovers, and Endogenous Productivity

AbstractThe paper presents empirical evidence on the international effects of U.S. fiscal policy from structural vector autoregressions identified through external instruments in a panel setting for the G7 countries. An exogenous increase in U.S. government spending is estimated to produce sizable positive responses of output and consumption in the rest of the G7 countries, both about half as large as their domestic U.S. counterparts, while strongly depreciating the U.S. terms of trade and lowering the U.S. trade balance and short‐run real interest rates. Moreover, fiscal shocks are estimated to have a strongly positive impact on hourly labor productivity in the private sector. We solve a two‐country New Keynesian model in closed form and show that a low cost elasticity of varying technology utilization can simultaneously explain the positive productivity, consumption, and international spillover effects as well as the real depreciation resulting from expansionary U.S. government spending shocks.

Manufacturing production as a source of growth: the case of Portugal

The aim of this paper is to test the relationship between manufacturing production and economic growth in Portugal from the 1950s to the present day, following Nicholas Kaldor’s (1908–1986) thinking, which assumes that manufacturing is the true engine of the economic activity of a country. By estimating a series of econometric models, including a Vector Autoregressive (VAR) model, it was possible to identify a positive relationship between growth and manufacturing production since the 1950s. The results also show that each increase of 1 percentage point (pp) in real growth rate of the production of basic metallurgical industries and manufacture of metallic products in a certain year t−1 for the period 1956–2019, caused an increase of 0.07 pp in the Portuguese economic growth during a following year t on average. We hope that these findings will stimulate new and interesting research regarding the important issues of manufacturing production and economic growth.

The Relationship Between the Development of the Art Industry and the Turnover of Economic Cycles

Abstract: In recent years, the area has grown significantly, creating a trend in art appreciation. This boom has led to a surge in the popularity of museums such as the National Gallery. This project aims to find out more about the economic contribution of art exhibitions. Using advanced time series analysis models including the intervention analysis model and vector autoregressive model, we obtained the following conclusions. First, the positive impact of low levels of economic development and high levels of government expenditure on the arts industry is obvious. Second, the growth of the arts industry has a small impact on most economic indicators. Third, the final impact of GDP on the arts industry is still uncertain due to the complex pattern of the model, with the possibility of a positive correlation with ln (art prices) being observed in the early stages of small GDP growth, while a quadratic pattern may be observed when the GDP index grows above a certain level and art prices fall Conclusion. Further research is needed to investigate the complex relationship between inflation and art prices. Our academic contribution mainly focuses on the multiple-way causal relationships between the economy, art exhibition, tourism, and education, which reveals the underlying mechanism of how art exhibitions promote the economy.

Research on the Correlation Between Carbon Emission Rights and Energy Market: A Case Study of EUA and Oil Market

This study focuses on the importance of the relationship between carbon and oil in the context of global climate change and energy transition. In particular, this paper explores the link between fluctuations in the price of Brent crude oil in the United Kingdom and changes in the European Union Emission Allowances (EUA), which has significant implications for insight into energy market dynamics and policy making. This study uses an autoregressive (VAR) model to analyze the dynamic relationship between the European Union Emission allowances (EUA) market and the oil market from 2018 to 2024. Through Augmented Dickey-Fuller (ADF) stability test, the VAR model is established, the optimal lag order is determined, and the impulse response and Variance decomposition are analyzed. The results show that the EUA market has a short-term positive impact on the response of the oil market. But this effect has diminished over time, indicating the market's ability to self-regulate and stabilize. These findings provide new perspectives for understanding the interaction between global energy markets and environmental policy tools. The findings highlight the importance of considering these market dynamics when developing market strategies and environmental policies.

Dynamic interactions among exchange rate, natural gas demand, production in manufacturing and battery industry export – evidence from Hungary

In the context of the current global economic transformation, the rapid rise of the electric vehicle industry has made the battery industry a new focus of competition among countries. Despite its relatively small economic and demographic size, Hungary has garnered significant academic interest by emerging as the world’s third-largest battery producer in 2021. The study employed a dynamic vector autoregressive (VAR) model to determine that fluctuations in the Hungarian exchange rate have a notable immediate influ-ence on exports in the battery business，which suggests that changes in exc-hange rates directly affect international competitiveness. The battery sector is experiencing a progressive increase in the influence of fluctuations in na-tural gas demand, particularly in terms of their impact on production costs and supply chains. The battery industry has had substantial advancements in technological innovation and production efficiency as a direct result of the expansion in manufacturing production. And Hungary’s total industrial deve-lopment is significantly influenced by the long-term effects of export expan-sion in the battery industry. JEL Codes: L62, E44, O14, C22

Coal price, economic growth and electricity consumption in China under the background of energy transition

With the increasing share of renewable energy generation in China's electricity consumption and the dramatic fluctuation of coal prices in recent years, the discussion of the relationship between coal prices, economic growth and electricity consumption in China becomes more relevant. This paper collects monthly data from 2016 to 2023, uses industrial value added to represent economic growth, and explore the relationship between the three through a time-varying parameter vector autoregressive (TVP-VAR) model, which shows that: (1) The mutual impact effects among coal prices, economic growth, and electricity consumption vary across short-, medium- and long-term. This finding underscores the importance of considering different time horizons when evaluating the interactive effects of these variables. (2) Compared to 2017 and 2022, the lagged impact effects among the three have undergone certain changes, indicating that the understanding of their relationships should be contextualized within specific developmental phases. (3) The relationship between coal prices, economic growth, and different categories of electricity consumption exhibits heterogeneous characteristics, suggesting that recognition of these relationships should be based on specific types of power generation. This research enhances the understanding of the dynamic relationship between coal prices and the macroeconomy in China at the current stage.

Research on the Impact of Economic Policy Uncertainty and Investor Sentiment on the Growth Enterprise Market Return in China—An Empirical Study Based on TVP-SV-VAR Model

This study employs the economic policy uncertainty index to gauge the level of economic policy uncertainty in China. Utilizing textual data from the growth enterprise market internet community, we construct the growth enterprise market investor sentiment index by applying the deep learning ERNIE (Enhanced Representation through Knowledge Integration) model, thereby capturing investors’ sentiment within the growth enterprise market. The dynamic interplay between economic policy uncertainty, investor sentiment, and returns of the growth enterprise market is scrutinized via the TVP-SV-VAR (time-varying parameter stochastic volatility vector auto-regression) model, and the asymmetric response of different industries’ stock returns within the growth enterprise market to economic policy uncertainty and investor sentiment shock. The findings of this research are that economic policy uncertainty exerts a negative influence on both investor sentiment and returns of the growth enterprise market. While it may trigger a temporary decline in stock prices, the empirical evidence suggests that the impact is of short duration. The influence of investor sentiment on the growth enterprise market returns is characterized by a reversal effect, suggesting that improved sentiment may initially boost stock prices but could lead to a subsequent decline over the long term. The relationship between economic policy uncertainty, investor sentiment, and returns of the growth enterprise market is time-variant, with heightened sensitivity observed during bull markets. Lastly, the effects of economic policy uncertainty and investor sentiment on the returns of different industries within the growth enterprise market are found to be asymmetric. These conclusions contribute to the existing body of literature on the Chinese capital market, offering a deeper understanding of the complex dynamics and the factors influencing market behavior.

Determinants of Household and Professional Forecasters’ Inflation Expectations in India

ABSTRACTThis study analyses and compares the determinants of inflation expectations among households and professional forecasters in India. To this end, we utilize quarterly survey data from the Reserve Bank of India (RBI), specifically the Inflation Expectations Survey of Households (IESH) and the Professional Forecasters’ Survey (SPF). The analysis employs a non‐recursive structural vector autoregressive (SVAR) model to explore the relationships between variables. The impulse response function and variance decomposition analysis shed light on the response of inflation expectations to macroeconomic shocks. Our findings indicate that apart from conventional macroeconomic factors like realized inflation and monetary policy, exogenous factors such as oil prices significantly shape inflation expectations. Comparing IESH and SPF, we found out that household expectations are more sensitive to shocks related to food prices, reflecting the influence of food expenses on household decision‐making. Conversely, professional forecasters’ expectations are more influenced by core inflation. Finally, we observed that inflation expectations adjust more quickly than realized inflation in response to macroeconomic shocks, suggesting a leading role. This study is novel with the contribution to the existing literature as it provides a comparative analysis of determinants of inflation expectations between households and professional forecasters in India, while also considering inflation targeting as a significant factor.

Financial and market risks of bitcoin adoption as legal tender: evidence from El Salvador

Bitcoin adoption as a legal tender threatens a financial crisis because of the lack of regulatory frameworks and systems for exchanging Bitcoin into local currencies. This study analyzes monthly data from 2010 to 2022 using a structural vector autoregressive model, estimating Bitcoin’s pass-through into remittance, money multiplier, the US Dollar index, and gold price. The results show that Bitcoin prices decrease the money multiplier and gold prices in both the short and long run, while remittances moderately increase in the long run. The implication of these results suggests the potential for international business opportunities to stimulate the credit, savings, and investment monetary policy channel. The results are robust to alternative SVAR identification strategies.

Impact of bank-affiliation on liquidity seeking of foreign mutual funds during adverse shocks: Evidence from China

Using daily shareholding data and event-based measures of composite shocks, we investigate the liquidity-seeking behavior of foreign mutual funds in China that are affiliated with commercial banks, comparing it with the behavior of unaffiliated foreign mutual funds. Fixed effect and panel structural VAR models both reveal differential responses between affiliated and unaffiliated foreign mutual funds. While both types of mutual funds seek liquidity by withdrawing from illiquid, volatile and growth-oriented stocks facing negative shocks, unaffiliated mutual funds exhibit a higher degree of liquidity seeking, consistent with the results of bank-affiliated funds providing liquidity stability. This finding is robust to the selection of shock periods and measurements. The evidence shed new lights on the liquidity management of open-end foreign mutual funds in emerging markets.

Energy factors affecting environmental pollution for sustainable development goals: The case of India

This study investigates the nexus between electricity consumption, fossil fuel dependency, renewable energy adoption, population growth, trade activities, economic growth, and environmental pollution in India. The primary objective is to understand how these factors interrelate and influence each other, focusing on their implications for sustainable development. The study used data from the World Bank from 2000 to 2023; the methodology adopted includes vector autoregression modeling, Granger causality tests, cointegration analysis, impulse response functions, and variance decomposition. These econometric techniques were selected due to their ability to capture dynamic relationships, determine causality, and identify long-term equilibrium among the variables. The findings reveal that economic growth significantly increases electricity consumption and fossil fuel usage, leading to higher carbon dioxide emissions. On the other hand, renewable energy adoption reduces environmental pollution. The study also highlights the complex interplay between population growth, urbanization, and trade activities in shaping India's energy demand and environmental outcomes. The implications of these findings are critical for India's sustainable development. The results suggest that while economic growth is essential, it must be balanced with sustainable energy practices to mitigate environmental pollution. The findings emphasize the need for policy interventions that promote renewable energy, enhance energy efficiency, and enforce environmental regulations. Recommendations include accelerating renewable energy adoption, implementing stringent energy efficiency standards, and developing integrated policies that simultaneously address the economic, energy, and environmental dimensions. These actions will help India achieve a sustainable balance between economic growth and environmental protection, ensuring a healthier future for its population.

How do Carbon Emissions Spillovers Reshape Metal Market Dynamics? Time– Frequency Insights on Precious and Non-precious Metals

Global disruptions, such as health crises and geopolitical tensions, significantly impact both climate and commodity dynamics. This study, well-grounded on environmental finance theory, input–output modelling, socio-transition philosophy and behavioural finance perspectives, explores the evolving interactions between carbon emissions (CE) and metal markets during COVID-19 pandemic and Russia–Ukraine war (RUW). The study uses time-varying parameter vector autoregressive model (TVP-VAR) technique to evaluate time and frequency varying connectedness between CE and metal markets from 3 January 2020 to 28 June 2024. During the COVID-19 pandemic, initial connectedness among selected markets peaked at 85%, averaging 46%, highlighting a significant CE–metal nexus that necessitates strategic responses. In the RUW period, connectedness averaged 47.82%. CE influence metal markets primarily in the short term. Wavelet coherence analysis reveals that palladium and platinum are highly sensitive to CE over the long term, while gold and silver may serve as effective diversifiers and hedges against carbon-related risks in metal investments. The study is relevant for investors in the metal sector with environmental considerations. JEL Classifications: G110, Q430, L720

Influencing Factors and Formation Mechanism of Carbon Emission Rights Prices in Shanghai, China

With the intensification of global climate change issues and increasingly stringent carbon emission controls, carbon emissions trading has become a crucial market-based mechanism for reducing carbon emissions. A deeper understanding of the factors influencing carbon emission rights prices and their formation mechanisms can enhance market transparency and effectiveness and promote the healthy development of the carbon trading market. This paper identifies the main factors affecting carbon emission rights prices and explores their formation mechanisms from both the internal and external perspectives of enterprises. Using a vector autoregression (VAR) model, it quantifies the impacts of energy prices, the climate, and technology levels on carbon trading prices in Shanghai, China. The results show that, in the short term, enterprise technology levels and energy prices significantly impact carbon emission rights prices, while the climate becomes more critical in the medium to long term. These findings propose specific policy recommendations for improving the carbon emissions trading market, with a focus on these three factors.

Food-fuel nexus beyond mean-variance: New evidence from a quantile approach

This paper investigates the dynamic relationship between crude oil, ethanol, and corn markets across various quantiles of return distributions, as well as at higher statistical moments. Using a quantile vector autoregression model and data from 2007 to 2022, we find that the cross-market linkages are quantile dependent, with the strongest connections observed in the tails of the distribution. A shock to the oil market significantly impacts ethanol and corn returns under extreme bearish and bullish conditions. Positive shocks to the corn market reduce ethanol returns when the ethanol market is highly bullish, but this effect becomes positive in the left tail of the distribution. We also identify significant co-movement in higher statistical moments between these markets. Extreme excess kurtosis in the food-fuel nexus is more likely to occur with high financial market uncertainty, a bullish stock market, contracting industrial production, and a strong US dollar. In addition to these variables, credit spreads, futures market liquidity, futures term structure, and hedging pressure also influence kurtosis in individual markets within the nexus.

Research on the mechanism and path of the coupling of digital technology and environmental regulation to promote urban green efficiency

Digital technology, renowned for its embeddedness and integration, revolutionizes governance ideas, institutions, and models. Based on the panel data of 285 cities in China from 2007 to 2021, a panel vector autoregressive model (PVaR) is built to reveal the interaction mechanism between digital technology and environmental regulation. The fixed-effects panel model explored the green impact of the coupling of digital technology and environmental regulation, considering the mediating role of industrial upgrading, factor allocation, and public participation. The results show that digital technology and environmental regulation promote each other. The coupling of digital technology and environmental regulation enhances urban green efficiency, especially in economically advanced and eastern regions. Industrial upgrading, factor allocation, and public participation mediate this effect. Therefore, government departments should build ecological civilization by improving environmental management decision-making, promoting the deep integration of digital technology and environmental regulation, and exploring a new path of urban green development.