Vector Autoregression Research Articles

On the connectedness of stock returns and exchange rates in emerging and frontier markets in Africa

AbstractThe effect of currency volatility on investments in Africa continues to dominate the headlines, especially in the recent period of global crisis and heightened geopolitical tensions. This paper tackles the dynamic relationship between stock returns and exchange rates in nine emerging and frontier African markets. The study departs from the usual VAR and GARCH models and employs a tool that accounts for time–frequency co‐movements and lead/lag relationships between exchange rates and stock returns in Africa. The bivariate wavelet technique applied to daily data from 1 April 2013 to 31 March 2022 established a profound negative co‐movement between stock returns and exchange rates, especially in the medium to long‐term frequencies. With exchange rates dominantly playing a leading role, it presents a case for policy consideration toward currency stability. We employed the partial wavelet approach to determine how stock returns and exchange rates related during the peak period of the COVID‐19 pandemic and found negative co‐movements within the short‐term frequency, revealing that investors preferred the short‐term horizon in times of crisis. However, when the covariate (COVID‐19) was controlled or suppressed, we discovered the health pandemic failed to drive both stock returns and exchange rates.

Urban air pollution and systemic risk of the real estate market in China

Air pollution is a major environmental risk to the economy nowadays. Given that urban air pollution is one of the determinants of urban housing price, and that both urban air pollution and housing price have significant spatial spillover effects, this paper explores the impact of inter-city air pollution spillover on systemic risk of the real estate market, using housing price data of 79 cities in 8 major urban agglomerations in China. This paper innovatively introduces the principal component analysis into the VAR model and appropriately conducts historical decomposition to construct a new systemic risk measurement index of the real estate market, which can identify both direction and magnitude of the impact. The results show that, on the time dimension, air pollution is found to contribute most time when systemic risk of the real estate market reaches a high level. On the spatial dimension, this paper identifies the urban agglomerations and node cities whose systemic risks of the corresponding real estate markets are more susceptible to air pollution spillover. On the spillover channels, this paper finds that the past cultivation of environmental awareness and the implementation of environmental protection policies in China have alleviated the negative impact of air pollution spillover on the systemic risk of the real estate market.

Income inequality, unemployment, and government transfer: what do their dynamics tell us

PurposeIn this research, we explore the dynamics among measures of income inequality in the USA, male and female unemployment rates and growth in government transfer using time series data.Design/methodology/approachThis research adopts a macro-econometric approach to estimate a structural VAR model using time series data.FindingsOur structural impulse responses found that growth in government transfer increases unemployment rates for both males and females. Female income inequality declines with increased government transfer. When the female income ratio rises, we observe that government transfer outlays fall over the forecast horizon. Variance decomposition finds that growth in government transfers is impacted by the male unemployment rate relatively more than the female unemployment rate. This research, therefore, suggests gender-specific government transfers to reduce income inequality. This, in effect, may reduce government transfer outlays over time.Practical implicationsThis research, therefore, suggests gender-specific government transfers to reduce income inequality. This, in effect, may reduce government transfer outlays over time.Originality/valueThis research investigates the dynamics among income inequality, government transfer and unemployment rates. There is a dearth of research articles that adopt a macro-econometric in this area.

Fiscal policy and deforestation

We investigate the impact of fiscal policy on deforestation in the Brazilian Amazon and Atlantic Forests. We first construct a theoretical dynamic model to examine the effects of government spending and tax revenues on the environment. We then run a VAR model to test the implications of fiscal policies on Brazilian deforestation, using data from June 1998 to May 2022. Our results suggest that government expenditures reduce deforestation in both forests. Moreover, deficit-inducing and balanced-budget fiscal policy shocks have dampening effects on deforestation, with the latter being more effective. This is good news for developing countries like Brazil. They can combat deforestation through fiscal policy in a way that doesn't worsen their fiscal situation. Our findings were unaltered when we considered the effects of a simulated fiscal policy based on spending on public goods only (social assistance, science and technology, defense, education, health, national security, and transportation).

Online graph topology learning from matrix-valued time series

The focus is on the statistical analysis of matrix-valued time series, where data is collected over a network of sensors, typically at spatial locations, over time. Each sensor records a vector of features at each time point, creating a vectorial time series for each sensor. The goal is to identify the dependency structure among these sensors and represent it with a graph. When only one feature per sensor is observed, vector auto-regressive (VAR) models are commonly used to infer Granger causality, resulting in a causal graph. The first contribution extends VAR models to matrix-variate models for the purpose of graph learning. Additionally, two online procedures are proposed for both low and high dimensions, enabling rapid updates of coefficient estimates as new samples arrive. In the high-dimensional setting, a novel Lasso-type approach is introduced, and homotopy algorithms are developed for online learning. An adaptive tuning procedure for the regularization parameter is also provided. Given that the application of auto-regressive models to data typically requires detrending, which is not feasible in an online context, the proposed AR models are augmented by incorporating trend as an additional parameter, with a particular focus on periodic trends. The online algorithms are adapted to these augmented data models, allowing for simultaneous learning of the graph and trend from streaming samples. Numerical experiments using both synthetic and real data demonstrate the effectiveness of the proposed methods.

Unleashing the Central Bank Digital Currency Revolution and its Impact on Exchange Rate: A Monetary Approach Synthesis

Research Originality: The introduction of CBDC by the Central Bank of Indonesia has increased the intensity of news about CBDC. Besides aiming to introduce the future direction of the payment system, the news potentially causes uncertainty and speculation in the market, which may impact the rupiah exchange rate.Research Objectives: This research analyzes the impact of CBDC news on the rupiah exchange rate, synthesizing a sticky price version of the monetary approach to the exchange rate. Research Methods: A CBDC News Index is constructed based on a compilation of news data from major online media between 2018 and 2023. The structural VAR (SVAR) method is thenemployed to investigate the impact of CBDC news on the exchange rate dynamic.Empirical Results: The results suggest a trend consistent with the hypothesis, indicating that news about CBDC may lead to pressure on the domestic currency, resulting in depreciation. However, the impact is not statistically convincing as the coefficient is not statistically different from zero. The monetary approach synthesis findings suggest that raising the policy rate is likely efficacious in counteracting the pressure of domestic currency depreciation. Meanwhile, other monetary approach variables exhibit anomalies related to exchange rate dynamics.Implications: As the central bank plans to implement a Central Bank Digital Currency (CBDC), it must carefully control the dissemination of information about what the CBDC will entail and how it will be rolled out. CBDC blueprint and official disclosure help reduce uncertainty and speculation about implementing CBDC.JEL Classification: D80, E58, F31How to Cite:Kurnia, A. S. & Assidiq, M. A. (2024). Unleashing the Central Bank Digital Currency Revolution and Its Impact on Exchange Rate: A Monetary Approach Synthesis. Etikonomi, 23(2), 271-286. https://doi.org/10.15408/etk.v23i2.37788

Setting the tone: the diffusion of moral and moral-emotional appeals across political and public discourse

Abstract Whether a topic is seen in a moral or moral-emotional light has significant political implications. Yet, we lack knowledge about the process of moralization: Who defines the way topics are communicated about? Where prior research has investigated the relative power of different actors to place a topic on the agenda or shape opinions, we study who sets the moral and moral-emotional tone of debate. To do so, we zoom in on immigration discourse in Germany and analyze fine-grained social media data from politicians, political parties, newspapers, and members of the public over a period of more than four years. After employing a transformer model to identify moral and moral-emotional appeals, we use structural vector autoregression models to demonstrate the important role of radical-right challengers in shaping public discourse in a negative moral-emotional direction. The results inform theories of moralization and political entrepreneurship.

Application of VAR Models to Assess the Impact of Budget Expenditures on GDP Dynamics

Application of VAR Models to Assess the Impact of Budget Expenditures on GDP Dynamics

Long-Run Trade Relationship between the U.S. and Canada: The Case of the Canadian Dollar with the U.S. Dollar

This study investigates the long-run relationship between the U.S. dollar and the Canadian dollar by analyzing the bilateral exchange rate induced by nominal and real shocks. The methodology centers on a structural vector autoregressive (SVAR) model, including the analysis of impulse response and variance decomposition to account for the impact of nominal and real shocks on exchange rate movements. This study also decomposes real shocks into demand and supply factors from both Canada and the U.S. and compares their impacts on the nominal and real exchange rates. The results are compared to shocks driven by country-specific nominal factors. This study uses quarterly data from December 1972 to December 2023. The findings suggest that real shocks have a permanent impact on both the nominal and real exchange rates, compared to nominal shocks, which have a temporary impact. Country-specific real supply-side factors have a more significant impact than country-specific real demand-side factors. Country-specific nominal factors barely impacted the nominal and real exchange rates between the U.S. and Canada.

The Impact of Climate Change Factors on Financial Markets: Evidence from China

ABSTRACT This paper examines the impact of climate change factors, namely Climate Risk Index (CRI), Climate Policy Index (CPI), and Climate Concern Index (CCI), on China’s financial markets. By employing quantile vector autoregression model and conducting network analysis, this paper intends to reveal the dynamic relationship and the risk transmission mechanism between climate factors and various financial markets under different market conditions based on the data ranging from Nov.1, 2011 to December 31, 2021. We find that climate shocks intensify the risk contagion among China’s financial markets, and the impact induced by each factor would present heterogeneity. The results also indicate that the systemic importance of climate shock index varies across market conditions. The findings shed light on the complex interaction between climate shocks and financial markets, which should contribute to policy implications for financial market stability and risk prevention.

Measuring Business Cycle Stylized Facts in Selected Oil-Producing Economies: A Comparative Study

AbstractWe document the stylized facts relating to the contemporary business cycle in eleven oil-producing economies that account for over 65% of global oil production using a set of macroeconomic aggregates covering the period 1981 to 2021. In the context of Nigeria, a vector autoregression analysis is used to measure a one standard deviation oil price shock on time series such as trade balance, real GDP, inter alia. Our study is unique in that we compared four different detrending techniques, including Hodrick–Prescott (HP), Baxter-King, Christiano–Fitzgerald, and Butterworth filters. This is a departure from the conventional method of relying on the HP filter, which has been known to have limitations, particularly in the context of oil-producing economies. Our findings indicate that the selected oil-producing economies with countercyclical inflation patterns suggest that supply shocks drive the business cycles. Conversely, the oil-producing economies with inflation in sync (procyclical) with the business cycle suggest that demand shocks may be the primary driver of business cycle variations. Additionally, our study shows that oil price innovations lead to higher inflation in Nigeria.

Scaled envelope models for multivariate time series

Vector autoregressive (VAR) models have become a popular choice for modeling multivariate time series data due to their simplicity and ease of use. Efficient estimation of VAR coefficients is an important problem. The envelope technique for VAR models is demonstrated to have the potential to yield significant gains in efficiency and accuracy by incorporating linear combinations of the response vector that are essentially immaterial to the estimation of the VAR coefficients. However, inferences based on envelope VAR (EVAR) models are not invariant or equivariant upon the rescaling of the VAR responses, limiting their application to time series data that are measured in the same or similar units. In scenarios where VAR responses are measured on different scales, the efficiency improvements promised by envelopes are not always guaranteed. To address this limitation, we introduce the scaled envelope VAR (SEVAR) model, which preserves the efficiency-boosting capabilities of standard envelope techniques while remaining invariant to scale changes. The asymptotic characteristics of the proposed estimators are established based on different error assumptions. Simulation studies and real-data analysis are conducted to demonstrate the efficiency and effectiveness of the proposed model. The numerical results corroborate our theoretical findings.

Evolution Characteristics of Heilongtan Spring Discharge and Its Response Law to Precipitation in Lijiang City, China

The contradiction between water supply and spring preservation issues is becoming increasingly apparent as Lijiang City develops. An investigation into the dynamic variations in the discharge rate of Heilongtan Spring in Lijiang City and the response law between the water level of the spring and precipitation is crucial for safeguarding the landscape water of Heilongtan Spring. This study employed linear regression analysis, Mann–Kendall (MK) mutation test, wavelet analysis, and vector autoregression (VAR) to examine the fluctuating pattern of the Heilongtan Spring discharge and the response of the Heilongtan Spring water level to precipitation in Lijiang City. Furthermore, the study discussed the influence of human activities on the alteration of Heilongtan Spring. The results indicate that the mean discharge rate of Heilongtan Spring is 0.94 m3/s, with an annual variation of 0.05 m3/s. The time series analysis reveals that the variation pattern of Heilongtan Spring discharge aligns with the precipitation trend in Lijiang City. Nevertheless, there is a distinction between the timing of the Heilongtan Spring discharge station point and the precipitation mutation point in Lijiang City. The significant primary cycle of spring discharge change occurs every 18 months, with a cycle length of 12 months. The vector autoregression (VAR) model demonstrates a lagged relationship between the water level of Heilongtan Spring and the precipitation in Lijiang City. Specifically, the water level of Heilongtan Spring has a four-month lag response to precipitation variability in Lijiang City. The results can provide a beneficial reference for preserving spring water and managing regional water resources.

The Dynamic Relationship Between Exchange Rate Volatility and Stock Prices in the Egyptian Real Estate Market and the Moderating Effect of Interest Rates

Among different channels of the financial market, the exchange rates and stock markets play a vital role in the domestic and international markets. This study aims to explore the dynamic relationship between the exchange rate (USD/EGP) and the real estate sector listed companies of the Egyptian Stock Exchange (EGX) from 2013 to 2023. Additionally, the study examines the moderating effect of interest rates on the relationship. To achieve this, the study employs several econometric methods such as Augmented Dicky Fuller (ADF) test, Granger Causality test to determine the direction of the relationship, Vector Autoregressive (VAR) model, and more. The results demonstrate that a long-run relationship exists between the variables. Furthermore, there is a significant unidirectional causality that runs from the exchange rate to real estate stock prices; thus, fluctuations in the exchange rate can significantly affect the profitability of investments in real estate stocks. Therefore, investors should closely monitor the movements in the exchange rate level to predict changes in real estate stock prices. When including the interest rate as a moderator, the outcome changes to bidirectional causality, meaning that the exchange rate and real estate stock price movements affect each other at a 1% significance level. Hence, interest rate could be used as an instrument to control demand in both the stock market and foreign exchange market. Finally, it is recommended that policymakers use the monetary policy while considering this bidirectional relationship in the presence of interest rate as a moderating variable.

PENGARUH VARIABEL MAKROEKONOMI TERHADAP DANA PIHAK KETIGA PADA BANK KONVENSIONAL DI INDONESIA

This study aims to determine the effect of macroeconomic variables on the collection of third party funds at conventional banks in Indonesia. The data used is monthly time series data from January 2017 to December 2022. The method used in this study is the Vector Autoregression (VAR)/Vector Error Correction Model (VECM) method. However, in choosing between the two methods, stationarity test must be done first. The variables used include inflation, BI rate, crude oil price, and gold price. The results of the study show that the inflation variable has a positive influence, while the BI rate, crude oil price, and gold price variables have a negative influence on third party funds of conventional banks in Indonesia. This research is expected to be applied to banking policy makers with the aim of being insightful to be able to adjust policies when there is instability in macroeconomic variables and for economic policy regulators in determining policies that can stabilize the banking system. In addition, it is used as knowledge for customers to make better decisions regarding investment portfolios with the aim of identifying risks based on macroeconomic conditions.

A Dynamic Impact Evaluation of the High-Quality Development of China’s Construction Industry Using the Panel Vector Autoregressive Model

A Dynamic Impact Evaluation of the High-Quality Development of China’s Construction Industry Using the Panel Vector Autoregressive Model

Can stronger governance and institutional quality drive growth through inward foreign direct investments in BRICS nations?

Background: This research focusses on the significance of institutional quality (INSQ) and governance in shaping the relationship between foreign direct investments (FDI) and growth. It specifically examines the BRICS nations (Brazil, Russia, India, China and South Africa) because of their economic significance in the global economy.Aim: This study aims to analyse the impact of INSQ and governance on sustainable growth, with a particular focus on its effects through the channels of FDI in the BRICS countries.Setting: Annual panel data from the Organisation for Economic Cooperation and Development (OECD) statistics, the United Nations Conference on Trade and Development (UNCTAD) statistics and the World Bank indicators spanning two decades (2000–2022) are used to analyse BRICS nations.Methods: The research study employed the Bayesian time-varying coefficient vector autoregression (BTVC-VAR) model to achieve the objective of the study.Results: The findings indicate that there is no long-term relationship between FDI, INSQ and economic growth in the BRICS countries. However, there is a noticeable co-movement among these variables in the short run.Conclusion: Given the obtained results, the policymakers should prioritise efforts to strengthen institutional capacity in the short term while focussing on the Sustainable Development Goals (SDG-8 and SDG-16).Contribution: The existing studies have assumed static economic, social and political conditions, potentially failing to accurately depict the complexities of an actual economy. This study offers methodological innovation by employing Bayesian time-varying coefficient vector autoregression (BTVC-VAR), enabling coefficients to adapt to evolving economic conditions over time. This effectively captures the dynamic nature of variables and provides reliable estimates.

Public debt determinants: A time-varying analysis of core and peripheral Euro area countries

This study employs a Bayesian Interacted Panel VAR model to estimate time-varying Generalized Forecast Error Variance Decomposition, analyzing how key determinants affect debt in Core and Peripheral Euro Area countries. Results highlight varying effects of determinants across periods and subgroups.

Macroeconomic effects of monetary policy in Japan: an analysis using interest rate futures surprises

AbstractWe estimate the effects of monetary policy on the aggregate economy in Japan during the last three decades when the effective lower bound (ELB) on interest rates was occasionally binding. We use monetary policy surprises from the interest rate futures market as the external instrument to identify monetary policy shocks in the vector autoregressive model. We find that monetary policy has been effective in Japan during the last three decades, and the effect was more persistent in the ELB regime than in the non-ELB regime. In a simulation exercise, we further show that a New Keynesian model with forward guidance can replicate our empirical finding.

Financial Markets Integration and the Consequences for Financial Stability

Financial integration, as part of the financial market union, implies closer interaction between global economies. The positive contribution of digitization can be found in the specialized literature, but it fails to provide an overview of the link between financial intermediation indicators and the use of digitization, taking into account the impact on the individual. In this sense, the motivation of this research consists in emphasizing therole of digital financial services in reducing poverty, having a beneficial impact on economic development and the economy welfare.The study relevance resides in capturing the Global Findex Index trend, by measuring the use of financial services every 3 years, starting in 2011. As a result, the research aims to analyze the financial intermediation degree, and, implicitly, digitization, based on a survey of 140 economies, using a Vector Autoregressive model, impulse response functions and the variance decomposition of the included macroeconomic variables.The research resultssummarize the fintech contribution in diminishingthe financial instability risks and outlining the policy implications for governments, benefits such as money laundering and reducing corruption.