Vector Autoregression Research Articles

Is digital development a factor of university-industry R&D collaboration and vice versa?

Abstract The purpose of the article is to determine and characterize relationships and their causality between indicators of digitalization of social relations and level of university-industry R&D collaboration. To conduct the research, a sample was formed for 20 countries of the world leaders in University-Industry R&D Collaboration indicator (as a part of Global Innovation Index) in 2022, which covers the following indices for period from 2011 to 2020: indicators of university-industry R&D collaboration, access to ICT, government online services, online creativity (as assessed by WIPO Global Innovation Index), and export of ICT goods (according to the World Bank). The methodological basis of the study was methods of correlation analysis (Pearson or Spearman, depending on data distribution, for which Shapiro-Wilk test for normal data distribution was previously applied) taking into account possible lags in time, VAR modelling, Granger test, and corresponding toolkit of STATA 18 software. As a result of the research, it was confirmed that the level of university-industry R&D collaboration is affected by such indicators of digitalization of social relations as online creativity (in 16 from 20 sample countries), access to ICT (in 12 countries) and the share of ICT goods exports in total exports (in 11 countries). At the same time, university-industry R&D collaboration is a cause of changes in the level of online creativity (in 15 from 20 sample countries), access to ICT (in 11 countries), public online services (in 10 from 19 sample countries) and the share of ICT goods exports (in 10 from 20 sample countries). The obtained results can be useful for stakeholders in R&D, innovative activities, development of state policy in the innovation and information sphere for making the most effective decisions in the context of stimulating the role of cooperation.

A TVP-VAR assessment of the spillover effects of geopolitical risk shocks on macroeconomic variability: a study of the Ghanaian economy

Our study verified the implications of the spillover of geopolitical risk (GPR) shocks to the economic crisis in Ghana. Our analysis employed the VAR-based spillover models by Diebold and Yilmaz (Int J Forecast 28:57–66, 2012; J Econ 182:119–134, 2014) and the Time-Varying Parameter Vector Autoregressive (TVP-VAR) connectedness approach by Gabauer and Antonakakis (Munich personal RePEc archive refined measures of dynamic connectedness based on TVP-VAR refined measures of dynamic connectedness based on TVP-VAR*, 2017). We scrutinized the interconnections and transmission mechanisms among key macro-financial variables spanning from 2000 to 2022. Our findings indicate that GPR is a fundamental source of shocks to the foreign exchange reserve (FXI), real exchange rate (REER), consumer price index (CPI), and debt. Other significant contributors include export (EXP) and import (IMP), with EXP standing out as the main shock transmitter. On the receiving end, CPI is most impacted by transmissions from IMP and GPR. Our study demonstrates that EXP and IMP are the top shock contributors, while FXI and CPI are the major recipients of these shocks. Such findings provide policymakers with valuable insights into the ramifications of geopolitical risk on the macroeconomic environment. Hence, policymakers are expected to provide necessary buffers to curb the influence of geopolitical risks on the economy.

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.

The Impact of Quantitative Easing on Stock Prices in the U.S. Stock Market - Structural VAR Model Analysis

In an effort for future investment implications, the current study examined the relationship between historical Quantitative Easing (QE) programs and the stock market. This study analyzed weekly basis data in the United States between 2007 and 2023. Through a Vector auto-regression (VAR) model, a significant rise following a quick drop in stock prices to a positive shock in QE was discovered, indicating lagged response. Further robustness check proved the validation of relationship, by sectioning the whole data set into 4 periods corresponding to four QE programs, except relatively insignificant effects in QE2 and QE3 periods. These lagged responses were pointed out firstly in this paper and connected with policy motivations and contemporaneous economy environment across programs. The findings may enable investors to anticipate market movements and generate respectable returns in the event of such unconventional money shocks.

Is Islamic Monetary System Possible in Indonesia? Interrelation Study between BI 7 Days Reverse Repo Rate and Nisbah Rate

The purpose of this study is to investigate the interrelationship between Bank Indonesia 7 Days Reverse Repo Rate (BI 7 DRRR) and Profit-Loss Sharing Rate (Nisbah Rate) in Islamic Sharia Banks (Bank Syariah Indonesia). Indonesia had been establishing conventional monetary system ever since and have yet to introduce Islamic approach to its monetary system. Now, in Islamic monetary perspective, interest rate doesn’t exist. Therefore, this research aimed to discover the possibility of Islamic approach in Indonesia’s monetary system, using Nisbah Rate launched by Islamic Sharia Banks as a substitution. The research was conducted using VAR (Vector Autoregression) to study the interrelationship between BI 7 Days Reverse Repo Rate and Nisbah Rate, using data obtained from Islamic Banking Statistics through Indonesia’s Financial Service Authority (OJK) monthly during 2021-2023. The result showed that both BI 7 Days Reverse Repo Rate and Nisbah Rate did cause impact on one another, and thus the possibility of establishing Islamic monetary system is not completely disregarded. This investigation contributes to the ongoing discourse surrounding the integration of Islamic financial principles into conventional monetary frameworks. By highlighting the reciprocal influence between conventional monetary policies and Islamic financial instruments, this study underscores the potential for a hybrid monetary system that accommodates Islamic principles within the Indonesian financial landscape.

The Spillover Effect of Stock Market Sentiment Intensity and Uncertainty on Housing Market in the United States

Investor sentiment is a key indicator of market performance. Many have studied how investor sentiment affects market returns or returns of different categories of stocks. Besides, many also studied how information revealed in media can affect the housing market. This paper focuses on the sentiment revealed in social media, Tweet, by combining these two key factors that have not been studied. This paper studies the spillover effect of stock market sentiment on the housing market by applying Vector Autoregression. The VAR model helps explain the relationship between housing prices and stock market sentiment from tweets, where sentiments are divided into sentiment intensity and sentiment uncertainty. The series of sentiment intensity and series of housing prices interact with each other, and the change in sentiment in the last period spills over to the housing market which affects housing prices, but the change in housing price does not affect sentiment in reverse. Similarly, the series of sentiment uncertainty and the series of housing prices interact with each other, and the change in sentiment diversity in the last period also spills over to the housing market which affects housing prices, but the change in housing price does not affect sentiment in reverse.

Predictive Modeling of Default Scenarios in the CMBS Market: A Macroeconomic Approach

This study analyzes potential default risks in Mortgage-Backed Securities (MBS) using time series data from 2010 to 2023. Employing Vector Autoregression (VAR) models to forecast economic indicators and calculate DTI and LTV ratios, which are crucial for predicting defaults in the MBS market. Our analysis, based on two economic models, examines the influence of macroeconomic factors and credit default risks on residential and commercial properties, such as Fixed-Rate and Adjustable-Rate Mortgages. The findings suggest that despite the significant impact of the pandemic, the likelihood of default in the American MBS market over the next 15 years remains relatively low. The study emphasizes the significance of comprehending the relationship between macroeconomic conditions and MBS default risks, providing a comprehensive methodology for future research.

The shale oil boom and the US economy: Spillovers and time‐varying effects

SummaryWe provide new evidence that the transmission of oil price shocks to the US economy has changed with the shale oil boom. To show this, we develop a time‐varying parameter factor‐augmented vector autoregressive (FAVAR) model with a large data environment of state‐level, industry, and aggregate US data. The model effectively captures potential spillovers between oil and non‐oil industries, as well as variation over time. Specified in this way, we find that investment, income, industrial production, and (non‐oil) employment in most oil‐producing and some manufacturing‐intensive US states increase following an oil‐specific shock—effects that were not present before the shale oil boom.

Transmission of Inflation and Exchange Rate Effects: The Markov Switching Vector Autoregressive Methodology

The aim of this study is to delve into the intricate the mechanism through which alterations in currency exchange rates give rise to shifts in inflation rates, while taking into careful consideration the country’s economic cycle. In order to accomplish this objective, we used a dataset that spanned from 1 January 1999 to 1 July 2023, focusing our analytical lens on three specific geographic areas, namely the Eurozone, the United Kingdom, and Canada. In our pursuit of understanding this complex relationship, we employed the Markov Switching Vector Autoregressive model. Our research outcomes can be succinctly encapsulated as follows: in the initial stages, particularly during phases characterized by robust economic growth, the transmission of exchange rate effects onto inflation levels appeared to exhibit a partial impact across all geographic areas under examination. However, during periods marked by economic downturns, both the United Kingdom and Canada displayed a distinctly more comprehensive transmission of these effects. Moreover, the prevailing projections for the forthcoming time horizon, across all the countries encompassed by our study, strongly indicate the onset of an expansionary phase that is projected to extend over a span of 25 months. Lastly, concerning the implications of unexpected disturbances or shocks, it is noteworthy that the response of exchange rates to inflation induced shocks was neither immediate nor as pronounced as the corresponding reaction of inflation to sudden shifts in exchange rates.

When you need them, they are not there: hedge capacities of cryptocurrencies disappear in downtrend markets

We provide empirical evidence supporting the economic reasoning behind the impossibility of diversification benefits and the hedge attributes of cryptocurrencies remaining in force during the downside trends observed in bearish financial markets. We employ a spillover connectedness model driven by time-varying parameter vector autoregressions on daily data covering January 2018 to November 2022 to analyze spillover transmissions between conventional and digital markets, focusing on the role of stablecoin issuances. We study the stock, bond, cryptocurrency, and stablecoin markets and find very high connectedness, which varies over time in response to up/down trends in financial markets. The results show that during financial turmoil, cryptocurrencies amplify downside risks rather than serve as diversifiers. In addition to risky assets from conventional financial markets, cryptocurrencies champion the transmission of spillovers to digital and conventional markets. In contrast, changes in stablecoin issuances produce few shocks because of their pegged prices, but they facilitate investors’ switch from volatile cryptos to more stable digital instruments; that is, we observe a phenomenon designated by us as the “flight-to-cryptosafety.” We draw insightful conclusions, provoking new thinking regarding portfolio hedge strategies that could potentially benefit investors when searching for less volatile investment performance.

Discover the Impact by COVID-19 Pandemic on Biopharmaceutical Index and Its Related Industry Portfolio

This research is trying to explore the changes in biopharmaceutical and other medically related indices under the impact of the pandemic, and how pharmacy related portfolio impact by the pandemic. The research utilizes EGARCH and VAR models to analyze the volatility of the biopharmaceutical index and the dynamic relationships among various related indices. The research mainly demonstrates the changes in risk of the biopharmaceutical index before and during the pandemic, providing a certain degree of alert to investors who are interested in investing in biopharmaceutical-related stocks but are relatively risk-averse. It also explores the impact of the biopharmaceutical index on other pharmaceutical-related indices, verifying its influence on other indices and whether it has predictive capabilities. Finally, by comparing the portfolios constructed using the biopharmaceutical index and its related industry indices before and during the pandemic, it provides recommendations for investors interested in investing in pharmaceutical stocks during large-scale health events.

Testing for Granger causality in heterogeneous panels with cross-sectional dependence

AbstractThis paper proposes a panel Granger causality approach for heterogeneous panels with cross-sectional dependence. We define a panel VAR model with unobserved common factors and apply the PANIC procedure to obtain the de-factored data. We then estimate the lag augmented (LA)-VAR model for each cross section and construct the panel statistics based on the meta-analytic approach that combines the p-values of the individual statistics. The Monte Carlo simulations indicate that the combination tests show good size and power properties and appear suitable for the panels where cross sections may have different unit root or co-integration properties. We finally re-investigate Granger causality between export and economic growth in OECD countries. The results shed light on the importance of accounting for cross-sectional dependence within a factor model framework in determining direction of Granger causality for country-specific analysis. The results further reveal that export and economic growth do not cause each other in the majority of the European Union countries.

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

AbstractBased 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.

Comparing Islamic and conventional stock markets in GCC: a TVP-VAR analysis

PurposeThis study investigates the dynamic interdependence between Islamic and conventional stock markets in the Gulf Cooperation Council (GCC) economies and the influence of global financial uncertainties on this interconnection.Design/methodology/approachThe study employs the time-varying parameter vector autoregressions (TVP-VAR) technique and analyzes daily data from December 1, 2008 to July 14, 2021.FindingsThe research reveals robust interconnectedness within individual countries between Islamic and conventional stock markets, particularly during crises. Islamic stock markets exhibit greater susceptibility to spillover effects compared to conventional stocks. The UAE and Kingdom of Saudi Arabia (KSA) stock markets are identified as net transmitters of spillovers, while Oman, Bahrain and Kuwait receive more spillovers than they transmit. Global financial uncertainty measures (GVZ, USEPU and UKEPU) positively influence financial market interconnectedness, with EVZ exhibiting a negative impact while VIX and OVX remain statistically insignificant.Practical implicationsInvestors and portfolio managers in Oman, Bahrain and Kuwait should carefully evaluate the UAE and KSA markets before making investment decisions due to the latter's role as net transmitters in the region. Additionally, it is emphasized that Islamic and conventional stocks should not be considered interchangeable asset classes for risk hedging.Social implicationsInvestors must be aware that Islamic and conventional stocks cannot be used as an alternative asset class to hedge risk.Originality/valueThe present article offers valuable insights for practitioners and researchers delving into the comparative analysis of Islamic and conventional stock markets within the GCC context. It enhances our comprehension of the dynamic interdependence between Islamic and conventional stock markets in the GCC economies and the impact of global financial uncertainties on this intricate relationship.

Research on Pricing on Re Market in Sevastopol

Affordability of housing is one of the most important and topical trends, that is taken into account in pursuing state economic policy in Russia. Pricing process on RE market acts as a serious factor influencing housing affordability and appeal of certain regions for continuous residence. Construction volumes directly depend on effective demand and conditions of mortgage crediting. The article discusses acute issues of certain factor impact on pricing process on RE market. In order to identify the interconnection of these factors the research conducted econometric assessment of the impact of wage dynamics and mortgage crediting portfolio on changes in market RE cost in the city of federal importance Sevastopol. Methods used in the research: models of vector auto-regression (standard and bias). Data of official statistics were used as an information base, including data of the Bank of Russia, as well as publications by economists. The initial hypothesis of the research said that proposed variables influence seriously the actual figure of housing cost in the region. Econometric analysis of this thesis proved in general the hypothesis. Findings of the research can form a scientific base for forecasting inflation processes on RE market in the city of Sevastopol and can be used in other entities of the Russian Federation.

How does inflation propagate among CPI components? Evidence from the euro area

This study employs a time-varying parameter vector autoregressive (TVP-VAR) model to investigate infla- tion propagation and spillovers among twelve major Consumer Price Index (CPI) components across eleven euro area countries from 1999 to 2023. We uncover empirical regularities in the transmission channels of inflation, noting Transport and Clothing and Footwear as primary shock transmitters, whereas Communi- cation, Alcohol and Tobacco consistently absorb shocks. These insights, consistent across various contexts and countries, contribute to understanding inflation persistence and households’ inflation experiences in the Euro area.

IMPACT OF COVID-19 ON THE FINANCIAL STABILITY OF COMMERCIAL BANKS IN KENYA

<p>Countries worldwide were gripped by the COVID-19 pandemic for the greater part of 2020 and 2021. COVID-19 spread to virtually all nations around the globe, causing a contraction of the global economy, and Kenya was no exception. Governments worldwide deployed social distancing, lockdowns, and curfews, which resulted in employee lay-off, business closure, and suppressed demand for commodities and services, eventually trickling down to commercial banks. The Kenyan banking sector experienced deterioration in asset quality which has been worsening since 2014 when it stood at 5.6 percent, reaching an all-time high of 14.5 percent in 2020 whereas Return on Assets which has also been declining since 2014, stood at 4.46 percent dropped to a record low of 2.07 percent in 2020 during the pandemic. This study, therefore, sought to determine how the financial stability of Kenyan commercial banks has been impacted by the COVID-19 shock. The study sought to specifically establish how both Z-score and capital adequacy of Kenyan commercial banks were impacted by the COVID-19 pandemic. Financial intermediation theory, Capital buffer theory, and Financial Instability Hypothesis anchored the study. The research design embraced was non-experimental, while the financial stability proxy was Z-score. The study’s target population was 19 commercial banks in Kenya between the years 2015 and 2022, which had complete data on all the study variables. Annual bank-level data was obtained from Kenya`s Central Bank`s annual supervision reports from 2015-2022. Event study methodology was used while collecting data whereby, the event window was 2020-2021, the period before the event (COVID-19) was 2015-2019, and the period after the event was 2022. The study espoused a panel vector autoregression model in data analysis where the impulse response functions were generated. The researcher discovered that the COVID-19 pandemic adversely impacted Z-score and capital adequacy. Based on the research findings, the Government of Kenya ought to institute non-disruptive pandemic control measures such as proper hygiene and wearing of masks as opposed to quarantines and lockdowns, which are detrimental to commercial banks' operations and other businesses, ultimately leading to a decline in income for commercial banks. Moreover, since capital acts as a shock absorber for banks, Kenyan commercial banks should strive to achieve and maintain the minimum capital adequacy ratios set by the Central Bank of Kenya. This will ensure that commercial banks in Kenya cushion themselves against economic shocks generated by pandemics such as COVID-19.</p><p> </p><p><strong>JEL:</strong> E44, E58, G01, G21, G28, G32</p><p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/edu/0227/a.php" alt="Hit counter" /></p>

The monetary policy of the State Bank of Vietnam, households and income distribution: the evidence from DSGE model

PurposeThis paper aims to propose a dynamic stochastic general equilibrium (DSGE) model for the State Bank of Vietnam (SBV) to assess the response from the household sector to monetary policy shocks through the consumption function. Moreover, the transmission from monetary policy to household consumption and income distribution is experimented with through the vector autoregression (VAR) model.Design/methodology/approachIn this study, the authors used the maximum likelihood estimation to estimate the DSGE and VAR models with the sample from 1996Q1 to the end of 2021Q4 (104 observations).FindingsThe DSGE model’s results show that the response of the household sector is as expected in the theory: a monetary policy shock occurs that increases the policy interest rate by 0.29%, leading to a decrease in consumer spending of about 0.041%, the shock fades after one year. Estimates from the VAR model give similar results: a monetary policy shock narrows income inequality after about 2–3 quarters and this process tends to slow down in the long run.Research limitations/implicationsBased on the research results, the authors propose policy implications for the SBV to achieve the goal of price stability, and stabilizing the macro-economic environment in Vietnam.Originality/valueThe findings of the study have theoretical contributions and empirical scientific evidence showing the effectiveness of the implementation of the SBV’s monetary policy in the context of macro-instability, namely: flexibility, caution and coordination of different measures promptly.

Exchange rate, external reserves and current account balance nexus in oil-dependent countries: A Toda-Yamomoto-based Panel Vector Autoregressive (PVAR) approach

The international monetary authorities have been consistently advising oil-dependent countries to change their exchange rate policy from a fixed to a floating exchange rate regime. While some of these countries including Nigeria have announced their adoption of a free-floating exchange rate system, evidence shows that the majority are suffering from “fear of floating”, hence operating an abridged exchange rate system. This study employs the Toda-Yamomoto-based Panel Vector Autoregressive (PVAR) model to use the causality approach to determine the exchange rate system that best explains the de facto exchange rate policy system operating in these countries. This is explained by the dynamic causality between exchange rates and foreign reserves. The dynamic causality between the exchange rate and current account balance also explains the potential effect of devaluation to improve the external trade balance, which implies the J-curve and Marshall Lerner condition. The results show that there is no significant causality from foreign reserves and trade balance to the exchange rate, suggesting that oil-dependent countries are more aligned to a fixed exchange rate regime than a floating exchange rate regime. We also find significant negative causality from the exchange rate to foreign reserves, while foreign reserves have a positive causal effect on the current account balance. This implies that the expected devaluation gains that may be prompting oil-dependent countries to stick to fixed exchange rate regimes are not there, as currency devaluation tends to worsen trade performance and foreign reserves rather than improve them. While oil-dependent countries are not benefiting much from a fixed exchange rate system, it is recommended that appropriate policy to boost private sector generation of foreign exchange should be put in place before the adoption of a full-fledged floating exchange rate system.

Servicification in Global Value Chains in Emerging and Developing Asian Economies

Servicification in global value chains (GVCs) in emerging and developing Asian economies has become a trend recently. However, there have been no scientific studies to elucidate the mechanism of servicification in GVCs. To fill this gap, this study aims to investigate the involvement of service sectors in GVCs in Asian economies in terms of the quantitative interactions between service inputs and manufacturing exports and inputs and between service inputs and service exports. For this purpose, a panel vector-autoregressive model and the Trade in Value Added database of the Organization for Economic Cooperation and Development (OECD) were used for the empirical analysis during 1995–2018. The estimation results find that, first, there exist reciprocal interactions between the business services and manufacturing sectors; foreign business service inputs are induced by manufacturing exports, whereas manufacturing inputs are induced by business service exports. Second, foreign manufacturing inputs facilitate foreign business service inputs. Third, business service inputs are promoted by business service exports. These trends in the involvement of business services’ involvement in GVCs have accelerated since the mid-2000s. To enhance the role of services in GVCs, Asian economies should facilitate the removal of explicit restrictions in service trade and address regulatory divergence across countries.