THE CONNECTEDNESS BETWEEN THE SENTIMENT INDEX AND STOCK RETURN VOLATILITY UNDER COVID-19: A TIME-VARYING PARAMETER VECTOR AUTOREGRESSION APPROACH

We analyze the connectedness between the sentiment index and the return and volatility of the crude oil, stock and gold markets by employing the time-varying parameter vector autoregression model vis-à-vis the coronavirus disease (COVID-19) epidemic. Our sentiment index is constructed via text mining technology. We also employ a network to visualize and better understand the structure of the connectedness. The results confirm that the sentiment index is the net pairwise directional connectedness receiver, while the infectious disease equity market volatility tracker is the transmitter. Furthermore, the impact of the COVID-19 pandemic on the total connectedness of volatility is unprecedented.

Exchange rate pass-through in Russian regions: Searching for spatial dependencies

Introduction. The article examines the spatial heterogeneity and factors of the exchange rate pass-through effect in consumer prices in Russian regions. Two hypotheses are tested. The first hypothesis is that there are differences in the magnitude of the passthrough between the Russian regions, the second is the significant influence of spatial relationships between regions on the magnitude of the pass-through effect. Theoretical analysis. The factors of the interregional differences in the pass-through effect are analyzed: the share of imports in the consumption structure, the share of value added produced in the domestic market in the final price of goods, transaction costs, the level of competition and the market structure. Empirical analysis. First pass-through estimates were obtained by means of vector autoregression model. Then the spatial dependence of the exchange rate pass-through was investigated on the basis of the global Moran and Geary indices, LISA, SAR and SEM models. Results. The results indicate the heterogeneity of the pass-through effect in Russian regions, which confirms the first of the hypotheses put forward. Confirmation of the second hypothesis was found only for food products in the short term, which is due to the nature of commodity flows between Russian regions. It is concluded that it is necessary to study the spatial relationships of the pass-through effect based on disaggregated prices.

The Effect of Economic Digitization on Financial Inclusion and International Trade in Southeast Asia

The rapid development of information technology has made economic digitization a necessity throughout the world, including Southeast Asia. This study aims to analyze the effect of economic digitization on financial inclusion and international trade using the Vector Autoregression Model analysis tool for ten countries in ASEAN for the 2017-2019 period. The results showed that international trade and financial inclusion variables could respond quickly to shocks in the variable of economic digitization. Economic growth can respond quickly to shocks in global trade variables and financial inclusion variables. There is no causal relationship between economic growth and international trade. However, there is a one-way causality relationship between economic growth and financial inclusion, where inclusion affects economic growth but not vice versa.

Money supply, inflation and output: an empirically comparative analysis for Vietnam and China

PurposeThis study focuses on analyzing the relation between money supply, inflation and output in Vietnam and China.Design/methodology/approachUsing the error correction model and the vector autoregression model (ECM and VAR) and the canonical cointegration regression (CCR), the study shows similar patterns of these variable relations between the two economies.FindingsThe study points out the difference in the estimated coefficients between the two countries with different economic scales. While inflation in Vietnam is strongly influenced by expected inflation and output growth, inflation in China is strongly influenced by money supply growth and output growth.Originality/valueTo the best of the authors’ knowledge, this is the first empirical and comparative research on the relation between money supply, inflation and output for Vietnam and China. The study demonstrates that the relationship between money supply, inflation and output is still true in case of transition economies.

FINANCIAL CYCLES IN THE ECONOMY AND IN ECONOMIC RESEARCH: A CASE STUDY IN CHINA

This work explores the relationship between financial cycles in the economy and in economic research. To this aim, we take China as an empirical example, and an intuitive bibliometric analysis of selected terms concerning financial cycles in economic research is performed first. Both in the economy and in economic research, we then conduct singular spectrum analysis to further isolate and describe the specific length and amplitude of financial cycles for China based on quarterly time-series data. Finally, according to the estimated cycles that detrended by Hodrick-Prescott filter for financial and bibliometric variables, the Granger causality test scrutinizes the results of the first two steps. Moreover, a time-varying parameter vector autoregression model is estimated to quantitatively investigate the time-varying interaction between financial and bibliometric variables. Our study shows that financial cycles have a strong effect on the developments in the financial-related literature. In particular, the 2008 global financial crisis’s impulse intensity is significantly higher than in other periods. Surprisingly, discussions on financial cycles in the literature also have an impact on financial activities in real life. These findings contribute to nascent work on the patterns in financial cycles, thus providing a new and effective insight on the interpretation of financial activities.

Does Military Expenditure Impact Environmental Sustainability in Developed Mediterranean Countries?

This study aims to examine the relationship between military expenditure and environmental sustainability in developed Mediterranean countries: Greece, France, Italy, and Spain. Sustainable economic growth is strictly related to energy consumption which leads to producing a higher level of carbon emissions. Besides, there may be a nexus between military expenditures and environmental pollution. This study focuses on developed Mediterranean countries since carbon emissions and greenhouse gas emissions are relatively high in these countries. Furthermore, France and Italy are the top countries in terms of total military spending. We investigate the relationship between military expenditure and carbon emissions using the Global Vector Autoregression model proposed by Pesaran, Schuermann, and Weiner (2004) and Dees et al. (2007) between 1965 and 2019. The empirical findings indicated that the relationship between carbon emission and military expenditure should be taken into account from a global perspective for environmental sustainability, and an increase in the global military expenditure seems to be very harmful to the global environment. It can be concluded that country-based prevents cannot provide the desired solution in combating environmental pollution.

Estimating Fiscal Multipliers in Selected Asian Economies

The surge of the coronavirus disease (COVID-19) pandemic has driven countries worldwide to launch substantial stimulus packages to support economic recovery. This paper estimates effects of fiscal measures on output using data from 2000 to 2019 for a panel of nine developing Asian economies and a vector autoregression model. Results show that (i) the 4-quarter and 8-quarter cumulative fiscal multipliers for general government spending range between 0.73 and 0.88 in baselines, in line with recent estimates for developed countries but larger than those for developing countries; (ii) government spending is more effective than tax cuts in boosting the economy; and (iii) an accommodative monetary policy regime can make fiscal measures more effective.

Topology-informed information dynamics modeling in cyber–physical–social system networks

Cyber–physical–social systems (CPSS) are physical devices that are embedded in human society and possess highly integrated functionalities of sensing, computing, communication, and control. CPSS rely on their intense collaboration and information sharing through networks to be functioning. In this paper, topology-informed network information dynamics models are proposed to characterize the evolution of information processing capabilities of CPSS nodes in networks. The models are based on a mesoscale probabilistic graph model, where the sensing and computing capabilities of the nodes are captured as the probabilities of correct predictions. A topology-informed vector autoregression model and a latent variable vector autoregression model are proposed to model the correlations between prediction capabilities of nodes as linear functional relationships. A hybrid Gaussian process regression model is also developed to capture both the nonlinear spatial and temporal correlations between nodes. The new information dynamics models are demonstrated and tested with a simulator of CPSS networks. The results show that the topological information of networks can improve the efficiency in constructing the time series models. The network topology also has influences on the prediction capabilities of CPSS.

Predicting total electron content in ionosphere using vector autoregression model during geomagnetic storm

The ionospheric total electron content (TEC) severely impacts the positional accuracy of a single frequency Global Positioning System (GPS) receiver at the equatorial latitudes. The ionosphere causes a frequency-dependent group delay in the GPS-ranging signals, which reduces the receiver’s accuracy. Further, the variations in TEC due to various space weather phenomena make the ionosphere’s behaviour nonhomogeneous and complex. Hence, developing an accurate forecast model that can track the dynamic behaviour of the ionosphere remains a challenge. However, advances in emerging data-driven algorithms have been found helpful in tracking non-stationary behavior in TEC. These models help forecast the delays in advance. The multivariate Vector Autoregression model (VAR) predicts the Ionospheric TEC in the proposed model. The prediction model uses input data compiled in real-time from the lag values of incoming TEC data and features extracted from TEC. The TEC is predicted in real-time and tested for different prediction intervals. The metrics – Mean Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) are used for testing and validating the accuracy of the model statistically. Testing the predicted output accuracy is also done with the dynamic time warping (DTW) algorithm by comparing it with the actual value obtained from the dual-frequency receiver. The model is tested for storm days of the year 2015 for Bangalore and Hyderabad stations and found to be reliable and accurate. A prediction interval of twenty-minute shows the highest accuracy with an error within 10 TECU for all the storm days.