Vector Autoregressive Framework Research Articles

Modeling shock propagation and resilience in financial temporal networks.

Modeling how a shock propagates in a temporal network and how the system relaxes back to equilibrium is challenging but important in many applications, such as financial systemic risk. Most studies, so far, have focused on shocks hitting a link of the network, while often it is the node and its propensity to be connected that are affected by a shock. Using the configuration model-a specific exponential random graph model-as a starting point, we propose a vector autoregressive (VAR) framework to analytically compute the Impulse Response Function (IRF) of a network metric conditional to a shock on a node. Unlike the standard VAR, the model is a nonlinear function of the shock size and the IRF depends on the state of the network at the shock time. We propose a novel econometric estimation method that combines the maximum likelihood estimation and Kalman filter to estimate the dynamics of the latent parameters and compute the IRF, and we apply the proposed methodology to the dynamical network describing the electronic market of interbank deposit.

Macroeconomic Effects of Fiscal Policy Shocks in Nigeria: A New Proof from Mixed-Frequency Vector Autoregression Approach

The empirical investigation of the macroeconomic consequences of fiscal or monetary policy changes has typically relied on the conventional vector autoregressive framework, which often has temporal aggregation concerns. This present study therefore provides new insights into the macroeconomic effects of fiscal policy in Nigeria. The empirical analysis was implemented using the mixed-frequency vector autoregression technique to overcome the temporal aggregation issue. The results demonstrate that using both fiscal policy indicators is effective in stimulating and stabilizing the Nigerian economy, although their effectiveness may vary depending on the economic conditions of the country. Specifically, the findings suggest that fiscal expansion is a more proactive measure for stimulating economic performance, whereas tax revenue is better suited for mitigating inflationary pressure in Nigeria. This study underscores important policy insights for achieving fiscal sustainability in Nigeria based on these results.

A VAR Framework of Exchange Rates, Interest Rates, and Inflation Through COVID-19 in Turkey: Empirical Evidence From Linear Cointegration and Causality Analysis

In this study, we investigate the impact of exchange rates and interest rates on inflation in Turkey using monthly data from January 2004 to July 2020 obtained from the Turkish Statistical Institute. Our vector autoregressive (VAR) model showed an evidence of stochastic behaviour among the series. The autoregressive distributed lag (ARDL) results showed a short-run and long-run covariate between exchange rates, interest rates and inflation. Specifically, the findings showed a short-run and long-run relationship between inflation, producer price index (PPI), and Turkish interbank offer rates (TIBOR). However, there was no connection between inflation and dollar exchange rates (DSR) and commercial banks’ interest rates (CBIR). The VAR Granger causality results revealed the variables to be exogenous except DSR, which displayed endogenous to other variables. Nevertheless, the results revealed unidirectional causality from the producer price index (PPI) to DSR and unidirectional causation from TIBOR to CBIR. It means an increase in production costs through raw materials importation led to devaluation of the Turkish lira. Similarly, TIBOR rates drive CBIR high, making domestic lending more expensive, which will inhibit loan provisions to the private sectors, which will result in an economic contraction and eventually high inflation. Our unit roots breakpoint results pointed to breaks in the dataset were between 2016-2019, reflecting the effects of the Fetullah Terrorist Organization (FETO) failed coup, the 2018 U.S. embargo, and an assumed fiscal dominance as the major and direct causes of economic instability and inflation. However, Covid-19 may have acted as a contributing factor since then. Thus, we recommend that the monetary authorities articulate policy to avoid the assumed fiscal dominance.

Tail risk transmission between decomposed oil shocks and green finance markets: evidence from an EVT–TVP–VAR approach

ABSTRACT This paper provides a novel framework based on extreme value theory, i.e. the autoregressive conditional Fréchet–time-varying parameters–vector autoregression (AcF–TVP–VAR) framework, to explore the tail risk spillovers between decomposed oil shocks (supply, demand, and risk shocks) and green finance markets. The empirical results reveal that there are significant time-varying tail risk spillovers between the two markets, with spillovers intensifying during extreme events. Additionally, throughout the sample period, supply shock is found to be a net transmitter of tail risk, while clean energy market is a net receiver. Notably, the safe haven properties of green bond market are captured, indicating it can serve as a diversified investment option. The findings are important for financial investors and policymakers.

The European energy crisis and the US natural gas market dynamics: a structural VAR investigation

The Russian invasion of Ukraine triggered severe disruptions in the European energy market, causing also significant shifts in global natural gas flows. We investigate how this European shock has affected the dynamics and altered the estimates of the elasticities on the US natural gas market. We use the Bayesian Structural Vector Autoregression framework proposed by Baumeister and Hamilton (Am Econ Rev 109(5):1873–1910, 2019, BH) for the crude oil market and applied by Rubaszek et al. (Energy Econ 103:105526, 2021, RSU) to analyze the dynamics of the US natural gas market. By extending the RSU model for natural gas exports and imports and deriving model’s posterior using most recent data, we approximate the impact of the European energy crisis on the US natural gas market. We show that the estimates of the US natural gas market elasticities change due to our modification, while simply updating the same prior beliefs with most recent data impacts the posterior estimates to a very limited extent. We also find that a shock even as major as the European energy crisis has only marginally affected the US natural gas market, thus confirming the results from the literature that the EU and US natural gas markets evolve independently.

Good vs. bad volatility in major cryptocurrencies: The dichotomy and drivers of connectedness

Cryptocurrencies exhibit unique statistical and dynamic properties compared to those of traditional financial assets, making the study of their volatility crucial for portfolio managers and traders. We investigate the volatility connectedness dynamics of a representative set of eight major crypto assets. Methodologically, we decompose the measured volatility into positive and negative components and employ the time-varying parameters vector autoregression (TVP-VAR) framework to show distinct dynamics associated with market booms and downturns. Our findings indicate that crypto connectedness reflects important events and oscillates substantially while reaching lower limit values when compared to traditional financial markets. Periods of extremely high or low connectedness are clearly linked to specific events in the crypto market and macroeconomic or monetary history. Furthermore, existing asymmetry from good and bad volatility indicates that market downturns spill over substantially faster than comparable market surges. Overall, the connectedness dynamics are driven by a combination of both crypto (momentum, on-chain activity, off-chain activity) and legacy financial and economic (financial and economic uncertainty, and financial market performance) factors, while the asymmetry is more connected to the off-chain crypto activity and the combination of economic, financial, and monetary factors. In both the total connectedness and asymmetry modeling, these can serve as hands-on indicators to be further translated into specific portfolio re-balancing decisions, risk management, and regulatory frameworks.

On bank stock return spillovers in South Africa: Implications for portfolio hedging

The purpose of this study is to investigate the return spillovers among the five systemically important banks in South Africa. The study employs a time-varying parameter vector autoregression (TVP-VAR) framework utilising daily data between 2000 and 2024. A minimum return connectedness portfolio is constructed from the spillover indexes and compared with traditional portfolio optimisation methods in terms of risk reduction and reward-to-risk ratios. The research findings reveal that Capitec, the smallest bank in terms of assets, has weaker return connectedness with other banks while ABSA, FirstRand, Nedbank and Standard Bank are moderately connected. Moreover, the two largest banks in terms of assets, Standard and FirstRand, are more connected than any other pairwise connection in the network. During significant events such as national elections, the global financial crisis and the COVID-19 pandemic, system-wide connectedness increases.

Nonseparability of credit card services within Divisia monetary aggregates

Abstract We use the New Keynesian Dynamic Stochastic General Equilibrium (DSGE) framework and Vector Autoregressive (VAR) to investigate the usefulness and relevancy of monetary services, augmented to include credit card transaction services. We use the new credit-card-augmented Divisia monetary aggregates in the models to further the existing research on their usefulness and relevancy. In this research, we compare three different monetary aggregates within the New Keynesian framework: (1) the aggregation-theoretic “true” monetary aggregate, (2) the credit-card-augmented Divisia monetary aggregate, and (3) the simple sum monetary aggregate. We acquire the following primary results. (1) The credit-card-augmented Divisia monetary aggregate tracks the theoretical (true) monetary aggregate, while simple sum does not. Although this result would be expected from the theory in classical economic models, the result is not an immediate implication of the theory in New Keynesian models and therefore needs empirical confirmation. (2) Under the recursive VAR framework, the credit-card-augmented Divisia monetary aggregate serves as a preferable monetary policy indicator compared to the traditional federal funds rate. (3) On theoretical grounds, we find that the separability condition for existence of a monetary aggregator function could fail, if credit card deferred payment services were excluded from the monetary services block, unless all markets are perfect.

Skill intensity in manufacturing exports: Do basic, technology-intensive or differentiated exports cause growth in Kuwait?

This study examines the causality between basic, technology-intensive, and differentiated manufacturing exports and economic growth in Kuwait using data from 1970 to 2021 and two augmented production function models: one with natural resource exports (Model 1) and the other without on both sides of the model (Model 2). The Johansen cointegration and the autoregressive distributed lag model (ARDL) bound tests are conducted to examine the long-run relationship between the variables. In addition, the Granger causality test in a vector autoregressive framework (VAR) and the Toda–Yamamoto test are employed to explore the directions of the short- and long-run causality between variables, respectively. The empirical results of Model 1 indicate that neither of the decomposed manufacturing exports directly causes economic growth in the short or long run at any conventional significance level, whereas natural resource exports cause economic growth, basic and technology-intensive manufactured exports in the short-run at the 5% level. Model 2 estimations confirm the absence of direct causality between decomposed manufacturing exports and economic growth, whereas a long-run causality runs from output net of natural resource exports to basic manufactured exports at the 10% level. Both model estimations indicate that all the variables jointly cause economic growth and basic manufactured exports in the short and long run, directly or indirectly through imports, confirming the existence of a circular causation. These findings can serve as the basis for designing specific export–import policies to foster diversification and a sustainable economic growth in line with Kuwait’s Vision 2035.

Fluctuations oil prices in the world and their impact on Indonesian macroeconomics

Abstract Changes in international crude oil prices can significantly impact economic outcomes. This study tested the impact of oil price fluctuations on macroeconomic variables in Indonesia. The data used is annual data from 1990 to 2021 with the specific purpose of solving the current research deficiency about swings in oil prices. study adds five macroeconomic variables as explanatory factors to accomplish this purpose. We use five macroeconomic variables: economic growth, inflation, interest rates, industrial production, and exchange rates. The impact of oil price fluctuations on GDP, inflation, exchange rates, interest rates, and industrial production indices in Indonesia is examined within a structural vector autoregressive (SVAR) framework. Research shows that oil price shocks significantly impact GDP, inflation, and exchange rate. The results also show that oil price shocks hurt two other variables: the interest rate variable and the industrial production index. The findings derived from the analysis of the impulse response function indicate a detrimental effect on the growth of production, while exerting a favourable influence on inflation. However, the impact on industrial production, interest rates, and exchange rates is very modest and characterised by a degree of uncertainty.

What Drives Inventory Accumulation? News on Rates of Return and Marginal Costs

AbstractWe study the determinants of inventory accumulation in a structural vector autoregression (VAR) framework with news shocks. Specifically, we investigate how news shocks affect two key determinants of inventory movements, namely, rates of return and marginal costs. We establish that inventories react strongly and positively to news about future increases in total factor productivity. We provide evidence that changes in external and internal rates of return are central to the transmission for such news shocks. We do not find evidence for a dominant role of marginal costs.

Quantile-based extended joint connectedness between trade policy uncertainty and GCC Islamic stock sectoral volatility

This study quantifies the shock transmission mechanism between the trade policy uncertainty (TPU) index and Sharia-compliant stock sectoral conditional volatility in the Gulf Cooperation Council (GCC) countries. We employ a comprehensive analysis that includes the time-domain extended joint and frequency-domain quantum vector autoregressive (QVAR) frameworks. The time-domain QVAR demonstrates that TPU causes the most substantial shocks to utility sector volatility. Investors in the GCC must strategically allocate their investment portfolios to the consumer services and energy industries, which are less vulnerable to the TPU shock. The results show that TPU shocks provide more error variances to the long-term and bullish conditional volatility (or higher quantiles) of the utility, real estate investment trust funds (REIT), healthcare, and industry sectors. Conversely, during bearish volatility conditions (lower quantiles), TPU shocks result in higher shocks in the conditional volatility of the utility, REIT, finance, and industrial sectors. Long-term investors should diversify their portfolios to mitigate risk in the utility, REIT, and industry sectors through strategic investments in consumer service sector. However, in the short term and during both bearish and bullish sectoral volatility, the consumer service and industry sectors are more vulnerable to TPU shocks. Regarding diversity, the REIT, energy, and financial sectors are the least affected by TPU shocks, offering a protective buffer in the short term and during periods of higher and lower GCC Islamic sectoral volatility.

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.

Industry Impacts of US Unconventional Monetary Policy

Abstract Conventional monetary policy has been shown to create differential impacts on industry output. This paper looks at unconventional monetary policy to see its differential impacts on industries in the United States. Identification is achieved with zero and sign restrictions within a structural global vector autoregressive framework. The effects of unconventional monetary policy on output have substantial heterogeneity across industries. Furthermore, the effects on output and monetary policy transmission mechanisms are qualitatively similar to that of conventional monetary policy previously reported in the literature. These findings suggest a substitutability between conventional and unconventional monetary policies. Importantly, policymakers can use unconventional monetary policy and be reassured that impacts on specific industries are similar to those using conventional monetary policy.

Specifying and estimating vector autoregressions using their eigensystem representation

This article introduces the eigensystem vector autoregression (EVAR) framework, where VARs may be specified and estimated directly via their eigenvalue and eigenvector parameters. Eigensystem constraints control a VAR’s allowable dynamics, as illustrated with EVAR estimations guaranteed to be non-explosive.

Comparing sin and ethical stocks: a spillover dynamics perspective

ABSTRACT We delve into the comparison between sin and ethical stocks through a spillover dynamics analysis using a quantile vector autoregression framework. The findings reveal that: (1) Sin and ethical stocks exhibit strong connectivity, with total spillovers being notably asymmetric under different market conditions. (2) There is a consistent pattern of alternating roles between transmitter and receiver for both sin and ethical stocks across various quantiles and time. (3) Ethical stocks consistently show dominance in transmitting return spillovers, and the COVID-19 pandemic has little influence on spillover dynamics for both sin and ethical stocks.

Monetary policy spillovers between the US and African Central Banks: A time- and frequency-varying connectedness study

This study investigates time and frequency connectedness between monetary policy in the US and 7 African countries to determine the extent to which US monetary policy influences policy conduct amongst African Central Banks. We use a time-varying parameter vector autoregressive (TVP-VAR) framework with frequencies to extract the spectral representation of the forecast error variance decompositions of the TVP-VAR and form 3 bands of frequency strata corresponding to long-run, medium-run and short-run. The results of the study are as follows: firstly, the static analysis shows that the US is the dominant transmitter of systemic shocks across all frequencies and the vulnerability of African countries as recipients of these shocks varies across frequency bands. Secondly, the dynamic analysis further reveals stronger short- and medium-run systemic connectedness during the periods of the Large Scale Asset Purchase programme and forward guidance policies whereas long-run connectedness is prominent during periods of US conventional monetary policy particularly in the COVID-19 era. The findings from the country-by-country dynamic spillover specifically show that countries which are more (less) responsive to US monetary policy shocks have lower (higher) inflation rates averages since the start of the pandemic. The findings suggest that African Central Banks can be benefit from higher coordination with the US Federal Reserve and we further propose that Central Banks worldwide in setting similar inflation targets.

A systematic vector autoregressive framework for modeling and forecasting mortality

AbstractRecently, there is a new stream of mortality forecasting research using the vector autoregressive model with different sparse model specifications. They have been shown to be able to overcome some of the limitations of the more traditional factor models such as the Lee–Carter model. In this paper, we propose a more generalized systematic vector autoregressive framework for modeling and forecasting mortality. Under this framework, we progressively increase the sophistication of the diagonal parameters in the autoregressive matrix and formulate a range of model structures in a systematic fashion. They offer much flexibility for capturing the mortality patterns of different populations. The resulting models produce age coherent forecasts, and their parameters are reasonably interpretable for modelers, demographers, and industry practitioners. Using the mortality data of Australia, Japan, New Zealand, and Taiwan, we demonstrate that the proposed approach generates appropriate forecasts of mortality rates and life expectancies and produces very good performance in the fitting and out‐of‐sample analysis.

The Risk Spillover Effects and Network Connectedness Between Real Estate and Other Sectors in China

Using a high-dimensional time-varying parameter vector autoregression framework, we propose a volatility spillover network to analyze risk contagion direction, path, and intensity under supply-demand relationships when important events break out in the real estate market. We characterize daily return volatility spillovers across firms from January 2010 to April 2022 and find a high correlation among these firms because the real linkage based on industrial chain structure strengthened. Meanwhile, the outbreak of essential events in the real estate market corresponds to several peak values of return volatility. The empirical results show that excessive correlation induces risk contagion across the network, and spillover effects are significant under extreme market conditions, manifesting as cross-industry contagion and intersectoral diffusion. The volatility spillover effects within real estate firms are higher than spillover effects across markets. In addition, the industrial chain structure better explains the mechanism of cross-industry risk contagion, firms located upstream of the real estate industry chain are risk receivers, and those located downstream are risk spillovers. Real estate firms mainly spread risks to financial institutions and firms that provide production goods. Our findings are of constructive significance to the related policies by regulatory authorities and provide multi-angle empirical evidence support for the risk supervision theory of “too connected to fail.”

Sophisticated and small versus simple and sizeable: When does it pay off to introduce drifting coefficients in Bayesian vector autoregressions?

AbstractWe assess the relationship between model size and complexity in the time‐varying parameter vector autoregression (VAR) framework via thorough predictive exercises for the euro area, the United Kingdom, and the United States. It turns out that sophisticated dynamics through drifting coefficients are important in small data sets, while simpler models tend to perform better in sizeable data sets. To combine the best of both worlds, novel shrinkage priors help to mitigate the curse of dimensionality, resulting in competitive forecasts for all scenarios considered. Furthermore, we discuss dynamic model selection to improve upon the best performing individual model for each point in time.