Vector Autoregressive Approach Research Articles

Unveiling the Impact of Geopolitical Risks on Turkish Economy and Fiscal Dynamics

This research aims to examine the impact of geopolitical risks on Türkiye's trade openness, household consumption, investment, government debt, and budget deficit for the quarters 2006:I–2022:IV. There are no empirical studies in the literature that examine the relationship between geopolitical risks and Turkish fiscal policy. The vector autoregressive (VAR) approach is utilized to examine the relationships among the variables. Geopolitical risks contribute to a very serious increase in inflation in Türkiye. The increase in geopolitical risks negatively affects consumption and investment and increases budget deficits and government debt. Additionally, Türkiye's trade volume has also decreased as a result of rising geopolitical risks, according to the VAR model's results. These findings demonstrate how vulnerable the Turkish economy is to geopolitical risks. Succinctly, Türkiye's public finances and economy are negatively impacted by geopolitical risks according to the analysis's findings. As a matter of fact, taking precautionary fiscal policy measures against geopolitical risks is important in eliminating the fiscal and economic losses that may arise. In this respect, this study has the potential to make a new contribution to the fiscal policy literature and to guide fiscal policy makers.

Return and volatility spillovers between non-fungible tokens and conventional currencies: evidence from the TVP-VAR model

This study investigates the static and dynamic return and volatility spillovers between non-fungible tokens (NFTs) and conventional currencies using the time-varying parameter vector autoregressions approach. We reveal that the total connectedness between these markets is weak, implying that investors may increase the diversification benefits of their multicurrency portfolios by adding NFTs. We also find that NFTs are net transmitters of both return and volatility spillovers; however, in the case of return spillovers, the influence of NFTs on conventional currencies is more pronounced than that of volatility shock transmissions. The dynamic exercise reveals that the returns and volatility spillovers vary over time, largely increasing during the onset of the Covid-19 crisis, which deeply affected the relationship between NFTs and the conventional currencies markets. Our findings are useful for currency traders and NFT investors seeking to build effective cross-currency and cross-asset hedge strategies during systemic crises.

Pattern and determinants of tail-risk transmission between cryptocurrency markets: new evidence from recent crisis episodes

The main objective of this study is to investigate tail risk connectedness among six major cryptocurrency markets and determine the extent to which investor sentiment, economic conditions, and economic uncertainty can predict tail risk interconnectedness. Combining the Conditional Autoregressive Value-at-Risk (CAViaR) model with the time-varying parameter vector autoregressive (TVP-VAR) approach shows that the transmission of tail risks among cryptocurrencies changes dynamically over time. During crises and significant events, transmission bursts and tail risks change. Based on both in- and out-of-sample forecasts, we find that the information contained in investor sentiment, economic conditions, and uncertainty includes significant predictive content about the tail risk connectedness of cryptocurrencies.

Time-Varying Granger causality between dollarization, exchange rate and stock market: Lag-Augmented Vector Autoregressive (LA-VAR) Approach

Time-Varying Granger causality between dollarization, exchange rate and stock market: Lag-Augmented Vector Autoregressive (LA-VAR) Approach

An economic policy uncertainty index for Portugal

This paper investigates the effects of policy uncertainty on major macroeconomic variables in Portugal, employing a Structural Vector Autoregression (SVAR) approach. I develop an Economic Policy Uncertainty (EPU) index utilizing data from over twenty news sources, which captures key moments such as elections, budget negotiations, and various crises. In response to a rise in policy uncertainty, firms delay projects, leading to a decline in industrial output and a rise in unemployment. Consumers, in turn, reduce their non-essential spending, resulting in a gradual decline in retail sales. On the financial side, rising policy uncertainty drives down equity prices and widens credit spreads, reflecting the concerns of investors and lenders.

Trade uncertainty and investments in an emerging country: a Fourier VAR approach

PurposeThis investigation aims to determine the effect of trade uncertainty on domestic investment (DI) and foreign direct investment (FDI) for the Turkish economy from the first quarter of 2005 to the first quarter of 2020.Design/methodology/approachThe authors adopt the vector autoregression (VAR) model augmented with Fourier terms. Using this methodology, the authors obtain the empirical results of the impulse-response functions and the variance decomposition analysis.FindingsThe empirical results demonstrate that a shock to trade uncertainty has a slight negative impact on DI for up to approximately 1.5 years, whereas its impact on FDI is negative but long-lasting. Moreover, the contribution of trade uncertainty to FDI is relatively higher than to DI in the error variance decomposition for the investigated period. These empirical results can be beneficial for shaping the Turkish authorities' trade policies in the following periods.Research limitations/implicationsThese findings have implications within the macroeconomic setting. Government authorities can provide tax exemptions for specified sectors and debureaucratize investment processes for both domestic and foreign entrepreneurs. Additionally, institutional quality and property rights should be protected strictly and developed gradually.Originality/valueThis study is the first to examine the impact of world trade uncertainty on Türkiye’s DI and FDI. Because trade uncertainty might act as fixed costs, this creates the option value of waiting and seeing the market, and firms hesitate to incur investment.

Tail risk connectedness in G7 stock markets: Understanding the impact of COVID-19 and related variants

This study uses a time-varying parameter vector autoregression (TVP-VAR) approach to examine the transmission of tail risk among G7 stock markets from January 2000 to September 2022, focusing on major financial episodes like the dot-com collapse, the 2008 Global Financial Crisis, and the European debt crisis, as well as the COVID-19 pandemic and its variants. Our analysis shows fluctuating tail risk connectedness across G7 markets during the pandemic, influenced by lockdowns, supply chain issues, interventions, and investor sentiment. Notably, the UK and Italy were major tail risk transmitters, whereas Japan predominantly absorbed risk, highlighting its distinct vulnerability. The findings stress the need for a comprehensive understanding of tail risk dynamics to inform decisions by investors, governments, and regulators, ensuring financial system resilience during heightened economic stress.

Economic Policy Uncertainty and Equity Fund Flows to India: A Bayesian Approach

We compare the impacts of economic policy uncertainty (EPU) and global economic policy uncertainty (GEPU)-related shocks on equity fund flows (EFFs) to India using a Bayesian vector autoregression approach. We find that (a) Indian EPU and GEPU are strongly and negatively related to EFF; (b) EFF are more sensitive to GEPU relative to Indian EPU; (c) evidence of trend-chasing behaviour by fund managers in India; and (d) GEPU is an important factor for forecasting returns on the Bombay Stock Exchange. Taken together, our findings indicate that EPU is important to understanding equity allocation decisions and returns in India. JEL Codes: F21, F39, G11

Measuring the Impact of Monetary Policy and Macroeconomic Volatility in Rwanda: Application of Factor Augmented Vector Autoregressive (FAVAR) Approach

Measuring the Impact of Monetary Policy and Macroeconomic Volatility in Rwanda: Application of Factor Augmented Vector Autoregressive (FAVAR) Approach

Spillover among Sovereign Credit Risk and the Role of Climate Uncertainty

This study investigates sovereign credit risk spillovers in G20 nations, emphasizing the impact of global climate policy uncertainty. This study analyzes the dynamics of these spillovers using sovereign credit default swap (SCDS) spreads and the time-varying parameter vector autoregressive approach. The study finds that Indonesia and Mexico are key risk transmitters within the G20. Linear and quantile regressions show an inverse relationship between climate uncertainty and SCDS spillovers, particularly affecting highly integrated G20 countries. These results highlight the need for coordinated and transparent climate policies to ensure financial stability among G20 nations.

When eco-anger (but not eco-anxiety nor eco-sadness) makes you change! A temporal network approach to the emotional experience of climate change

Research on the emotional experience of climate change has become a hot topic. Yet uncertainties remain regarding the interplay between climate change-related emotions (i.e., eco-anxiety, eco-anger, eco-sadness), general emotions (i.e., regardless of climate change), and pro-environmental behaviors. Most previous research has focused on cross-sectional studies, and eco-emotions in everyday life have seldom been considered. In this preregistered study, 102 participants from the general population rated their eco-emotions (i.e., eco-anxiety, eco-anger, eco-sadness), general emotions (i.e., anxiety, anger, sadness), and pro-environmental intentions and behaviors daily over a 60-day period. Using a multilevel vector autoregressive approach, we computed three network models representing temporal (i.e., from one time-point to the next), contemporaneous (i.e., during the same time-frame), and between-subject (i.e., similar to cross-sectional approach) associations between variables. Results show that eco-anger was the only predictor of pro-environmental intentions and behaviors over time. At the contemporaneous level, the momentary experience of each eco-emotion was associated with the momentary emotional experience of the corresponding general emotion, indicating the distinctiveness of each eco-emotion and the correspondence between its experience and that of its general, non-climate-related emotion. Overall, our findings 1) emphasize the driving role of eco-anger in prompting pro-environmental behaviors over time, 2) suggest a functional and experiential distinction between eco-emotions, and 3) provide data-driven clues for the field's larger quest to establish the scientific foundations of eco-emotions.

Understanding the cyclical patterns of carbon dioxide emissions to mitigate climate change: Evidence from the QUAD countries

Addressing the challenge of climate change requires a nuanced understanding of the factors driving CO2 emissions. While numerous studies have explored the relationship between economic variables and emissions, few have examined their cyclical interdependencies, and none as per our knowledge have conducted their study for the QUAD-the United States, India, Japan, and Australia. This gap motivated us to investigate whether discernible cyclical patterns exist in CO2 emissions, trade, GDP, and fossil fuel consumption in these nations. Utilizing the Hodrick-Prescott filter on annual data from 1970 to 2020, sourced from the World Bank and Our World in Data, this study uncovers country-specific procyclical trends in CO2 emissions. To scrutinize the dynamic interactions among these variables, we employ a Structural Vector Autoregression (SVAR) approach. Our empirical analysis reveals that a GDP shock elicits different contemporaneous responses in CO2 emissions across the QUAD countries: a positive response in India, Japan, and Australia, but a negative one in the United States. These findings emphasize the need for tailored strategies in managing CO2 emissions, akin to business cycle management, thereby underscoring the policy implications of our research.

Agricultural credit guarantee scheme fund and oil palm production in Nigeria: A vector autoregressive (VAR) approach

Oil palm has been identified as one of the key agricultural products that can diversify Nigeria’s economy. However, its production and export have been hampered by capital constraints. Consequently, the Agricultural Credit Guarantee Scheme Fund (ACGSF) was introduced to encourage lending to farmers and agro-processors to enhance agricultural productivity. Therefore, this study aims to determine the effectiveness of ACGSF allocations to the oil palm production subsector. Time series data on oil palm production, as well as data on ACGSF allocations to oil palm and cash crop production, prime lending rate and commercial banks' loans to agriculture were obtained from FAOSTAT and the Central Bank of Nigeria. Vector autoregression, Granger causality and impulse response functions were used to estimate the short-run relationship. The results reveal that ACGSF allocations to the oil palm subsector and commercial banks’ loans to agriculture have negative and positive effects, respectively, on oil palm production. There was unidirectional causality from ACGSF and commercial banks to agriculture to oil palm production. The findings suggest expansion and close monitoring of the ACGSF scheme, and the intensification and esterification of oil palm production. The study shows that estimating the effect of ACGSF on overall agricultural productivity does not provide deep insight into commodity-specific credit packages.

Vector Autoregressive Model of Maize Production in Northern Region of Ghana

Agricultural growth plays a crucial role in the Comprehensive African Agriculture Development Programme (CAADP, 2009) agenda. The program recognizes that increasing agricultural productivity is essential for reducing poverty, meeting food production targets, and lowering production costs and food prices for the impoverished. This study aimed to develop two types of models. The first model employed a vector autoregressive (VAR) approach, which involved regressing the production of maize in one district against the production of maize in other districts at various lags. The second model utilized a VAR framework where the maize production in each district was regressed against the production of maize in other districts and the corresponding climate conditions at different lag periods. The available data spanned from 1968 to 2018 and were recorded on an annual basis. Six climatic variables were included in the analysis. A lag order of 3 was selected for the models. The results of the autocorrelation test using the portmanteau test indicated no serial autocorrelation across all lag periods. The test for normality revealed that the residuals followed a normal distribution. Additionally, there was no evidence of heteroscedasticity in the data. Furthermore, a Granger causality test was conducted on the selected districts to explore causal relationships. Variance decomposition analysis was performed to assess the variance relation in the data and understand the contribution of different factors. Based on adjusted R-squared, mean absolute error (MAE), and root mean squared error (RMSE) values, the models that incorporated climatic variables were found to be the most suitable for forecasting maize production in the selected districts. The VAR model, which captures the interdependencies between the variables, was utilized in this analysis. All variables in the VAR model were treated symmetrically, meaning that their relationships were considered equally important.

Impact of Google searches and social media on digital assets’ volatility

Advanced digitalization and financial technology have of recent times become among the most crucial tools. Data mining and sentiment analysis have revealed the importance of digitalization in modern times. This study examines the influence of Google search activity on the volatility of digital assets. We analyzed six digital asset prices for Bitcoin, Bitcoin Cash, Ethereum, Ethereum Classic, Litecoin, and Ripple from the Coinmarketcap database. We used tweets on Twitter to survey users’ sentiment by using the Twitter search Application Programming Interface and Google trend search from web searches, news searches, and YouTube searches data using RStudio software. The study spanned 1 September 2019 to 31 January 2020 and employed the Vector Autoregression (VAR) approach for analysis. The VAR estimation revealed that Google search variables have significantly influenced the volatility of Bitcoin, Ethereum, Litecoin, and Ripple, as supported by the Granger causality test and impulse response function. The results of this study could be useful for investors and policymakers in drawing up strategies to reduce market volatility. These results should thus be useful to investors in developing profitable investment strategies to mitigate the impact of market turbulence.

Is there a nexus between NFT, DeFi and carbon allowances during extreme events?

PurposeThe authors target the interrelationships between non-fungible tokens (NFTs), decentralized finance (DeFi) and carbon allowances (CA) markets during 2021–2023. The recent shift of crypto and DeFi miners from China (the People's Republic of China, PRC) green hydro energy to dirty fuel energies elsewhere induces investments in carbon offsetting instruments; this is a backdrop to the authors’ investigation.Design/methodology/approachThe quantile vector autoregression (VAR) approach is employed to examine extreme-quantile-connectedness and spillovers among the NFT Index (NFTI), DeFi Pulse Index (DPI), KraneShares Global Carbon Strategy ETF price (KRBN) and the Solactive Carbon Emission Allowances Rolling Futures Total Return Index (SOLCARBT).FindingsAt bull markets, DPI is the only consistent net shock transmitter as NFTI transmits innovations only at the most extreme quantile. At bear markets, KRBN and SOLCARBT are net shock transmitters, while NFTI is the only consistent net shock receiver. The receiver-transmitter roles change as a function of the market conditions. The increases in the relative tail dependence correspond to the stress events, which make systemic connectedness augment, turning market-specific idiosyncratic considerations less relevant.Originality/valueThe shift of digital asset miners from the PRC has resulted in excessive fuel energy consumption and aggravated environmental consequences regarding NFTs and DeFi mining. Although there exist numerous studies dedicated to CA trading and its role in carbon print reduction, the direct nexus between NFT, DeFi and CA has never been addressed in the literature. The originality of the authors’ research consists in bridging this void. Results are valuable for portfolio managers in bull and bear markets, as the authors show that connectedness is more intense under such conditions.

Green bonds and green environment: exploring innovative financing mechanisms for environmental project sustainability.

This study delved into the intricate relationships between green bonds (GB), Environmental National Cap (ENC), the European Commission's green growth metrics (EC), and innovative financial mechanisms (INN_FM). Employing the Pesaran CD test, the research underscored significant cross-sectional dependence among the examined countries. The subsequent unit root tests affirmed the first-order integration of variables, causing the panel vector autoregressive (PVAR) approach for deeper insights. The findings indicated that while GB notably influenced the EC's metrics, and its effect on ENC was relatively subdued. Notably, INN_FM appeared to insignificantly influence the issuance of GB. By leveraging variance decomposition, we discerned that the dynamics between these factors, especially in green economic growth, is complex and can vary across regulatory and national contexts. This research provides an essential foundation for policymakers, regulators, and investors to understand the multifaceted interplays in green finance mechanisms and craft strategies to optimize their impact on sustainability outcomes. Hence, the study provides multiple policy implications for the associated stakeholders.

Are there inextricable connections among automobile stocks, crude oil, steel, and the US dollar?

According to the International Organization of Motor Vehicle Manufacturers (OICA), Automobile manufacturers use 40% of steel to make the body structure, 23% to make engines and gears, and 12% for suspensions. We untangled the perceived inextricable connections between crude oil, steel, the US dollar, and stocks of Toyota, Daimler, and Volkswagen as the automobile firms with the largest capitalization by using the time series daily data from 1998 to 2023. We used an augmented version of the Diebold and Yilmaz (2012) connectedness approach, which is based on the Time-Varying Parameter Vector Auto-regression (TVP-VAR) approach as introduced by Antonakakis et al. (2020). Findings suggested that Volkswagen, Daimler, and Toyota are more affected by shocks in steel prices than by USD and oil price shocks, with a spillover of 9%, 11.72%, and 8.72% respectively from steel. The next transmitter is USD, which contributes 4.94%, 6.37% and 3.96% of shocks to Volkswagen, Daimler, and Toyota stocks, and the third variable is the oil price shocks, which contributes 4.25%, 5.41% and 3.50% of spillover effects to Volkswagen, Daimler, and Toyota stocks, respectively. This shows the heterogeneous responses of automobile stocks with the highest capitalization and sales to the error variances transmitted from steel, USD as well as oil. Most notably, overall steel is a net transmitter and USD as net receiver in TYP-VAR system for Toyota and Daimler and can therefore be used as a diversifier. Our findings are impactful for the shareholders as well as portfolio managers for risk reduction and efficient portfolio management.

Integration of flows and signals data from mobile phone network for statistical analyses of traffic in a flooding risk area

In this paper, we present a robust spatiotemporal statistical methodology that is capable of accurately forecasting traffic in the flood-prone area of the Mandolossa in the Province of Brescia (Italy). An innovative combination of two sources of mobile phone data is proposed to obtain an extremely accurate representation of the flows of people passing by the streets directly linked to the risky area. Three types of flows have been considered: outflows (from the flood-prone area to the neighborhood), inflows (from the neighborhood to the flood-prone area), and internal flows (within the flood-prone area). The three flows are assumed to be dependent on each other and are modeled using a vector autoregressive approach. We found evidence of both weekly and daily seasonal components in the time series. To capture the seasonality, a dynamic harmonic regression component has been included, where the optimal number of Fourier bases in the periodic functions has been chosen according to a criterion based on the Akaike Information Criteria. On the other side, the set of autoregressive parameters has been defined in such a way as to represent the time period necessary for the mobile phone company to observe, process, and release the data. The forecasting ability of the model has been assessed using blocked k-folds cross-validation along with the mean absolute percentage error and the hit rate. Though the model performs better for non-summer days, we found that it satisfactorily forecasts both the number and the level of people moving.

Banking Transparency, Financial Information and Liquidity Risk Management: Case of Saudi Banks

The article aims to assess the impact of banking transparency on liquidity risk. To do so, we first test the determinants of Liquidity Coverage Ratio (LCR) as well as ensure the resilience of the Saudi banking system over the period from 2014 to 2021. Using System GMM with bank-specific and macroeconomic variables, results show that capital adequacy ratio, SIZE, GDP growth as well as past LCR levels significantly influence the LCR.  Secondly, we adopt the Panel Vector Auto Regression (PVAR) approach to assess the response of the LCR to various shocks. Impulse Response Functions (IRF) and variance decomposition demonstrate that the shocks to past LCR, AQ, CAR and GDP increase future liquidity risk. Thirdly, we prove that Saudi banks implement less than 50% of the transparency dimensions. They mainly disclose financial information and information on information credibility. Barely 18% of information on non-financial components of banking activity is made available to the public. Information on liquidity risk and on the timeliness of information is not available either in annual reports or on the bank's website. On average, the banks in the sample do not give importance to the publication of reports. These results may undermine the effectiveness of the guidelines of the Basel Committee agreements to reduce risk-taking by Saudi banks.