Vector Autoregression Research Articles

The trend of digital finance: unveiling the multidimensional network of cryptocurrency risk propagation

ABSTRACT This study primarily explores the mechanisms of risk propagation among cryptocurrencies, unveiling for the first time the frequency dimension of risk propagation within the cryptocurrency market and identifying the role of oscillation frequency in this process. By employing Variational Mode Decomposition (VMD) and the DY spillover matrix to construct a complex network, the paper analyzes the frequency dimension risk propagation mechanisms of nine major cryptocurrencies from 2017 to 2023. Key findings include the significant risk propagation capabilities of Ethereum (ETH) and Bitcoin (BTC) during periods of high market volatility, while stablecoins such as Tether and USD Coin exhibit minimal risk propagation ability. Additionally, the characteristics of cryptocurrency risk propagation have been enhanced following the COVID-19 pandemic. Overall, the risk propagation of most cryptocurrencies is primarily realized through high-frequency oscillations. The robustness of the conclusions is verified using the Time-Varying Parameter Vector Autoregressive (TVP-VAR) model. The results are significant for understanding the dynamic characteristics of the cryptocurrency market, predicting future market risks, and formulating risk management strategies. Furthermore, the methodology and findings of this study provide new perspectives and tools for exploring the risk propagation relationships among cryptocurrencies.

Research on the effectiveness of hedging on SSE 50 stock index futures

Stock index futures is an important investment product, which gives financial market participants a new way to manage risks, especially in the case of investors holding more spot positions, the risk avoidance function of stock index futures is more prominent. Therefore, this paper to the optimal hedging ratio of stock index futures as the research focus, the above card 50 stock index futures main contract (code: IHL 8) as the research object, on this basis, using OLS and VAR model, for the SSE 50 index (code: 000016), and use the effectiveness of hedging index He to evaluate the effectiveness of the hedge, so as to get the most effective hedging model. Through the empirical research in this paper, it is found that the hedge effect obtained by using the VAR model is better than that of the traditional OLS model. By using the stock index futures, the market participants can effectively avoid the risk of the spot market.

Green horizons: Enabling the energy transition through climate change policies

In response to the goals set by the COP21 Summit, the challenges brought about by the COVID-19 pandemic, and the ongoing conflict between Russia and Ukraine, this study aims to develop an effective policy framework to support the transition to sustainable energy. This framework considers key elements such as managing energy risks, leveraging green finance, navigating carbon markets, and evaluating three climate change policy indicators: the impact of climate change summits (CCS), government climate programs (CCGP), and climate change taxation (CCT). Utilizing the Quantile Vector Autoregressive (QVAR) approach and analyzing monthly data from the United States from August 1, 2014, to August 30, 2022, our research examines the influential role of these climate policy indicators. The results show that climate summits, governmental initiatives, and climate taxes are significant influencers, affecting both renewable and conventional energy sectors, carbon market movements, and green finance. The study underscores the importance of government initiatives and climate policies in driving the transition to renewable energy and fostering environmentally sustainable financial activities. Focused on advancing Sustainable Development Goals 7 (Affordable and Clean Energy) and 13 (Climate Action), this research contributes to shaping effective policy frameworks and enriching policy debates on sustainable energy transition.

Volatility and returns connectedness between cryptocurrency and China’s financial markets: A TVP-VAR extended joint connectedness approach

We employ a time-varying parameter vector autoregression (TVP-VAR) joint connectedness approach to study the dynamic risk spillover effects between cryptocurrencies and China’s financial market, further exploring the impact of cryptocurrencies on China’s financial market. Our results show that there is asymmetric risk transmission between cryptocurrencies and China’s financial market, and the risk spillover effect is very weak. Specifically, the spillover of cryptocurrencies to China’s financial market is significantly stronger than the spillover of China’s financial market to cryptocurrencies. Cryptocurrencies have a stronger spillover effect to China’s exchange rate and gold. The net spillover effect of cryptocurrencies is weakening over time. Overall, the return spillover impact of cryptocurrencies on China’s financial market is greater than the volatility spillover impact, and the degree of impact of different cryptocurrencies is heterogeneous. The findings of this study have several implications for policymakers and investors.

Offshoring, firm-level adjustment and labor market outcomes

This paper studies how the China shock affects unemployment rates and wage inequality across high-skilled and low-skilled workers in the United States, with particular emphasis on the dynamic and general equilibrium channels of firms' production locations and entry decisions. To shed light on the subject, I build a two-country trade-in-task model with firm heterogeneity, search-and-matching labor market frictions, and firms' endogenous selections into entry and offshoring. The model, consistent with evidence from vector autoregression analyses, uncovers important dynamics with implications for the impact of the China shock on U.S. worker inequality. Namely, it shows association between a decrease in offshoring costs and a short-lived increase in low-skilled unemployment in the source country, a longer-term decline in high-skilled unemployment, a transient expansion of the wage gap between high- and low-skilled workers, and an increase in firm entry.

Climate change and rice yield in the far north of Cameroon: reciprocal effects using the vector autoregressive model

Climate change and rice yield in the far north of Cameroon: reciprocal effects using the vector autoregressive model

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.

Does the U.S. export inflation? Evidence from the dynamic inflation spillover between the U.S. and EAGLEs

Given the crucial role of inflation as a key economic barometer, our paper investigates the dynamic inflation spillover between the U.S. and the nine emerging and growth-leading economies (EAGLEs) between 1991M1 and 2020M2. Employing the recently developed time-varying parameter vector autoregressions (TVP-VAR)-based connectedness approach, we find evidence of a moderate inflation spillover across the sample countries at normal condition. We further point out that inflation spillover effects with the U.S. are more pronounced for the emerging markets with higher openness, the net oil-importing emerging markets, and the emerging markets following free-float exchange rate regimes. More importantly, the inflation spillover index among the system rises dramatically to over 70% under extremely inflationary conditions, implying that the transmission of spiral inflation is very high. Additionally, the time-varying analysis shows that the role of the U.S. in the inflation shock transmission with emerging countries varies between being a net inflation-exporter and inflation-importer over times. Finally, an investigation of the drivers of the inflation spillovers reveals that the U.S. dollar, emerging markets' economic policy uncertainty, and bilateral trade are key determinants of the inflation shock transmission among the system. Our findings justify central banks’ actions in decreasing U.S. dollar reserves to safeguard their domestic currencies.

Risk Premium and Fear of Investors in Crisis’ Periods: An Empirical Approach Based on Fama–French and Carhart Factor Models

This study aims to answer the question about the interactions between “investors’ fear”, two factors proposed by Fama & French, the Carhart momentum factor, andthe risk premium, and how these interactions were affected by two financial crises, the Dot-Com and Sub-Prime crises. This paper is the first empirical study that considers the effects of these financial crises. It is of critical importance as it changes the specificity of the empirical models for different periods, significantly affecting the results compared to previous research work. The main findings include a general negative change in fear over all of the sub-periods. Secondly, no consistent positive trend was observed in any of the risk premiums over time. After each crisis, the relationships between the endogenous variables had significant changes. More specifically, investors’ fear, on the first day of the week, appears to be systematically higher across all sub-periods except during the Sub-Prime crisis. Finally, after the Sub-Prime financial crisis, there is an almost complete loss of the explanatory power of the VAR models. Although fear does not seem to affect risk premiums or momentum, it was nevertheless found that the results are sensitive to the specification of the models.

Foreign Exchange Futures Trading and Spot Market Volatility in Thailand

This paper investigates how the introduction of foreign exchange futures has an impact on spot volatility and considers the contemporaneous and dynamic relationship between spot volatility and foreign exchange futures trading activity, including trading volume and open interest in the Thailand Futures Exchange context, with the examples of the EUR/USD futures and USD/JPY futures. The results of the EGARCH (1,1) model show that the introduction of foreign exchange futures decreases spot volatility. It also increases the rate at which new information is impounded into spot prices but decreases the persistency of volatility shocks. A positive effect of unexpected trading volume and a negative effect of unexpected open interest on contemporaneous spot volatility are in line with the VAR(1) model results of the dynamic relationship between spot volatility and foreign exchange futures trading activity. With the impact on spot volatility caused by unexpected open interest rate being stronger than by unexpected trading volume, foreign exchange futures trading stabilizes spot volatility.

Structured prior distributions for the covariance matrix in latent factor models

Factor models are widely used for dimension reduction in the analysis of multivariate data. This is achieved through decomposition of a p×p\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p \ imes p$$\\end{document} covariance matrix into the sum of two components. Through a latent factor representation, they can be interpreted as a diagonal matrix of idiosyncratic variances and a shared variation matrix, that is, the product of a p×k\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p \ imes k$$\\end{document} factor loadings matrix and its transpose. If k≪p\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$k \\ll p$$\\end{document}, this defines a parsimonious factorisation of the covariance matrix. Historically, little attention has been paid to incorporating prior information in Bayesian analyses using factor models where, at best, the prior for the factor loadings is order invariant. In this work, a class of structured priors is developed that can encode ideas of dependence structure about the shared variation matrix. The construction allows data-informed shrinkage towards sensible parametric structures while also facilitating inference over the number of factors. Using an unconstrained reparameterisation of stationary vector autoregressions, the methodology is extended to stationary dynamic factor models. For computational inference, parameter-expanded Markov chain Monte Carlo samplers are proposed, including an efficient adaptive Gibbs sampler. Two substantive applications showcase the scope of the methodology and its inferential benefits.

Spillover Effect Due to Macroeconomic Variables: Evidence from Volatility in BRICS Nations

Purpose: The aim of this study is to examine the spillover effect of volatility in macroeconomic variables on indices of emerging nations. Theoretical Framework: This research considered macroeconomic variables such as GDP, FDI, Inflation, and money supply and indices of emerging nations (Brazil, Russia, India, China and South Africa). This paper focuses on identifying how stock markets of BRICS are affected by variations in macroeconomic variables. Design/Methodology/ Approach: The data on GDP, inflation rate, FDI, Money Supply, and Indices (Chinese BOVESPA, Russian, Indian Bombay Stock Exchange, Chinese Shanghai Stock Exchange (SSE) Composite Index, and South African FTSE) was drawn for a period of 20 years from 2001 to 2021. Prices of the indices were acquired from www.investing.com. Macroeconomic indicators data was taken from the www.worldbank.org. For analysis, the VECM and VAR models are prepared. Findings: The findings show that all the economic variables are considerably leaving a spillover impact on the prices of BRICS nations. VECM models state relationship among the GDP, FDI, Inflation, and money supply and Stock markets of Brazil, Russia, India, and China. However, in the case of South Africa, these economic variables have a dynamic correlation with its stock market index as is depicted through the VAR Model. Research Practical and Social implications: The study addresses the issue of spillover effect of factors which had indirect impact on the volatility of the indices of BRICS nations. In the times of such high level of uncertainties like covid pandemic it is important to understand the direct and indirect factors and variables which may impact the variability of the stock markets. Originality/Value: This articles has conducted an extensive analyses of macroeconomic variables and their impact on indices of emerging nations. The authors provides clarity and insights in understanding the variability in markets in depth.

Can green bonds be a safe haven for equity investors?

AbstractWe investigate if green bonds can act as a safe‐haven asset for equity investors by analysing their relationship with stocks and other alternative safe havens, namely sovereign bonds and gold. Safe havens are defined as assets that exhibit zero or negative comovement with equity during a stock market downturn. We analyse the interrelationships between the asset classes using the Marginal Expected Shortfall of Acharya et al. (The Review of Financial Studies, 30(1), pp. 2–47, 2017) and by comparing the regime‐dependent GIRFs from a Markov‐switching VAR model. Our results suggest that green bonds are not safe haven assets for equity investors but rather show positive comovement during periods of market stress. The sovereign bond is the most consistent in delivering diversification benefits across market conditions, while gold acts as a safe‐haven asset during all regimes except during rare periods of extreme turbulence.

Comparative Analysis of Risks and Their Impacts on the Banking Stability of Islamic and Conventional Banks in the MENA Region

This study aims to analyze and compare the impact of risks on banking stability associated with Islamic banks in comparison to their conventional counterparts in the MENA region. In this process, we first examine the correlation between liquidity risk and credit risk. Secondly, we assess whether these two types of risks are related to banking stability within both categories of banks. Using the PVAR (Panel Vector Auto Regression) econometric model on a sample of 110 conventional and Islamic banks in the MENA region, our results indicate that liquidity risk and credit risk exhibit a negative relationship in both types of banks. Furthermore, our findings suggest that there is no correlation between these risks and banking stability in both types of banks. Our conclusions have significant implications for banking institutions regarding the strategies and approaches required to manage these risks and ensure the stability of their banking operations.

Traffic flows time series in a flood-prone area: modeling and clustering on extreme values with a spatial constraint

Time series of traffic flows, extracted from mobile phone origin–destination data, are employed for monitoring people crowding and mobility in areas subject to flooding risk. By applying a vector autoregressive model with exogenous covariates combined with dynamic harmonic regression to such time series, we detected the presence of many extreme events in the residuals, which exhibit heavy-tailed distribution. For this reason, we propose a time series clustering procedure based on tail dependence which is suitable for data characterized by a spatial dimension, since objects’ geographical proximity is taken into account. The final aim is to obtain clusters of areas characterized by the common tendency to the manifestation of extreme events, that in this case study are represented by extremely high incoming traffic flows. The proposed method is applied to the Mandolossa, a strongly urbanized area located on the western outskirts of Brescia (northern Italy) which is subject to frequent flooding.

Vector autoregressive model: to analyze the influence of the agriculture, mining and quarrying sectors on local revenues

Vector autoregressive model: to analyze the influence of the agriculture, mining and quarrying sectors on local revenues

Effect of fuel prices on sailing speeds in short-sea shipping

ABSTRACT In this paper, we estimate fuel price elasticities of sailing speed for general cargo vessels using data covering the Baltic Sea and Norwegian coastal areas during the years 2007–2018. To disentangle the correlation between fuel prices and freight rates arising from changes in global demand, we instrument fuel prices on price changes triggered by supply shocks. The shocks are derived from the structural vector autoregressive (SVAR) model proposed by Kilian (2009). In a sensitivity analysis, we use unanticipated oil supply shocks derived from oil supply outages as an alternative instrument for fuel prices. We find that disregarding the correlation between fuel prices and freight rates yields biased parameter estimates, which can partly explain the mixed results in the previous literature. Our preferred estimate for the fuel price elasticity of speed is −0.08.

Unravelling the devaluation puzzle: Empirical insights into the transmission channel on balance of payments and output in Ethiopia

Background Empirical studies on the impact of devaluation in developing countries, including Ethiopia, have revealed diverse and mixed results. The effects can be positive or negative depending on the specific economic context and policies in place.This study addresses the devaluation puzzle by providing a more comprehensive and nuanced understanding of how devaluation affects the balance of payments and output. Methods To achieve this, we employ a recursive structural vector autoregressive (SVAR) model focusing on Ethiopia from 2001Q1 to 2023Q4. Results The findings reveal that exchange rate shocks negatively affect the balance of payments in both the short and long run, with a greater impact observed in the short run. Shocks in other variables, such as foreign exchange rate reserves, inflation, and interest rates, exert a stronger influence on the balance of payments than the exchange rate itself in the long run. In addition, shocks in the exchange rate significantly impact foreign exchange rate reserves in the long run, highlighting the complex dynamics of exchange rates and the indirect nature of the exchange rate channel. Exchange rate shocks also negatively impact output growth in both the short and long run. Conclusion The study concludes that devaluation has a contractionary effect through various channels. Devaluation increases the money supply, leading to inflationary pressures and a decline in output. It also increases interest rates, which further reduces output. In addition, devaluation reduces foreign exchange reserves, resulting in a fall in the balance of payments and a decrease in output. We suggest policymakers prioritize exchange rate stability through practical monetary and fiscal policies. In general, International financial institutions, particularly the National Bank of Ethiopia, should reconsider their devaluation-focused policies, considering the complexities and potential negative consequences associated with devaluation.

Implementation of the Vector Autoregressive (VAR) Model in Electricity Supplier Companies

The government's policy regarding setting special prices for coal as fuel for power plants is a dilemma for investors because if the policy is implemented there is a possibility that it will affect the company's profitability and impact the company's share price. Scientific forecasting of a company's future based on share price volatility with various considerations including political, social and economic aspects of the country are things that must be taken into account when making decisions. The method that will be used in this research is quantitative descriptive through the application of the VAR model to be able to describe the causal relationship between variables. The result is a VAR model for each variable that is used as a forecasting model for the daily share price volatility of companies supplying coal to PLN, so that investors can make strategic steps in investing.

Did COVID‐19 induce a reallocation wave?

AbstractWe critically examine the hypothesis that COVID‐19 has ushered in a large reallocation shock in the USA, beyond typical business cycle patterns. We take a broad perspective, and first consider data from the CPS and JOLTS; there is no noticeable uptick in occupation or sector switches, either at the aggregate level or in the cross‐section. The dispersion of sectoral growth rates over the three years before the pandemic was similar to the previous period. The recovery from the initial shock was characterized by very high quits and low layoffs, patterns indicative of a strong labour market, not excessively high reallocation relative to previous business cycles. High growth of small employers in the recovery, and larger ones once the labour market tightened, is also a common cyclical pattern. We then examine whether mismatch unemployment rose as a result of the pandemic; using an off‐the‐shelf multisector search and matching model, there is little evidence for an important role for mismatch in driving the unemployment rate during the pandemic. Finally, we employ a novel Bayesian Structural Vector Autoregression framework with sign restrictions to identify a reallocation shock; we find that it has played a relatively minor role in explaining labour market patterns in the pandemic, at least relative to earlier episodes.