Parameter Vector Autoregression Research Articles

Japanese stock market sectoral dynamics: A time and frequency analysis

AbstractThis study examines the Japanese stock market connectedness across different sectors, focusing on both the time and frequency dimensions. The dataset used spans from January 1999 to April 2022 and employs various methodologies, including time‐varying parameter vector autoregressions, TVP‐VAR frequency dependency, and Quantile coherency. The empirical findings reveal that cyclical or aggressive stocks predominantly act as net transmitters of shocks across sectors. Moreover, short‐term spillovers are more significant compared to intermediate‐term spillovers, indicating that Japanese sectors are more pronounced to market shocks in the short run. The spillover effects are also asymmetric and vary over time. Additionally, the real estate sector exhibits diversification benefits across different time horizons, while the energy sector provides protection primarily in the short run. This research contributes to the development of financial policies aimed at reducing sectoral imbalances and promoting stable growth. Furthermore, it offers insights for investors seeking to devise optimal portfolio diversification strategies.

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.

Economic policy uncertainty and geopolitical risk: evidence from China and Southeast Asia

This paper investigates the connectedness between China's economic policy uncertainty (EPU) and the geopolitical risk (GPR) of the countries in the Association of Southeast Asian Nations (ASEAN) using quantile time‐varying parameter vector autoregression. The empirical results indicate that China's EPU becomes a net risk transmitter at the median quantile, demonstrating that ASEAN countries' GPR is vulnerable to China's economic shocks. Furthermore, extreme events such as COVID‐19 amplify the connection between EPU and GPR, leading to a higher total connectedness index value. Moreover, the pairwise spillover effect analysis reflects that the risk launcher effect of China's EPU is longer and more prominent for Thailand and the Philippines than for Malaysia and Indonesia, which enriches the hypothesis. The contributions are as follows. This paper selects emerging China as the research object and first discusses its economic influence on neighbouring countries. In addition, fully analysing the heterogeneous spillover effects of EPU on the GPR of different countries makes our suggestions diversified and practical. Therefore, improving recognisable ability and institutional quality will promote ASEAN countries to mitigate their vulnerability to China's EPU shocks. Meanwhile, China's government should quickly respond to external uncertainties, prudently adjust economic policies, and avoid large swings of EPU volatility.

Spillover analysis across FinTech, ESG, and renewable energy indices before and during the Russia–Ukraine war: International evidence

AbstractThis study is epicentral to analyze the impact of the Russia–Ukraine war on the financial markets, specifically focusing on the connectedness and spillover dynamics of FinTech, Environmental, Social, and Governance (ESG), renewable energy, gold, and Morgan Stanley Capital International (MSCI) indices in developed and emerging countries. Data are collected from Thomson Reuters, ranging from May 8, 2020, to May 11, 2022, and a time‐varying parameter vector autoregression (TVP‐VAR) and the dynamic conditional correlation (DCC) generalized autoregressive conditional heteroskedasticity (GARCH) t‐Copula (DCC‐GARCH t‐Copula) are used to analyze the data. The results show that FinTech, ESG, and MSCI are net transmitters in developed countries, whereas gold and renewable energy are net receivers pre‐ and during war periods. ESG and MSCI are net transmitters in emerging countries, while FinTech, renewable energy, and gold become net receivers in both periods. The hedging ratio sheds light on the costs and weights of efficient pair investments that might change in the context of each region and under the combined scenario. The study has important implications for merchant bankers, policymakers, investors, hedgers, and risk managers.

시간가변 전이효과를 고려한 글로벌 대체투자 자산들의 변동성 파급경로에 관한 연구

In this study, volatility spillover was analyzed based on the Time Varying Parameter VAR(Vector Autoregressive) model to identify the volatility transmission mechanism of alternative assets for the S&P Global Infrastructure Index, S&P Merger Arbitrage Index, and S&P U.S. SPAC Index.
 The results of the empirical analysis over the sample period are as follows: First, the total connectedness of the indices to be analyzed was estimated to be 27.22%, and the volatility spillover between the S&P500 and the Global Infrastructure Index was found to be quite high, whereas the index with the least connectedness to the S&P500 index was found to be the U.S. SPAC Index. Second, according to the net bidirectional connectedness estimation results, the most affected index was reported as the Merger Arbitrage Index, and the most affected index was reported as the Global Infrastructure Index. In addition, the U.S. SPAC Index was relatively neutral, confirming the possibility of contributing greatly to the portfolio diversification effect. Third, the total connectedness of the indices showed a behavior of rapidly increasing and then decreasing again at the time of the global spread of the coronavirus, but different movements were observed for each index, and the SPAC Index reacted more sensitively than other indexes.

Spillover effects in Chinese carbon, energy and financial markets

AbstractAs China's carbon market continues to develop, its close connection with the financial and energy markets is becoming increasingly apparent. A systematic study of the spillover effects between markets is important, as it can help prevent excessive fluctuations in carbon prices. With this in mind, this study proposes a time‐varying parameter vector autoregression with Lanne–Nyberg decomposition extended joint connectedness approach to analyze quantitatively the spillover effects in the “carbon–energy–financial” system. Empirical results show that a bidirectional spillover effect exists among markets. Not only does the carbon market have the most pronounced return (volatility) linkages with the natural gas (clean energy) market, but the information connected with the energy markets is also more closely linked than with the financial markets. We also find that market fluctuations, caused by the China–US trade conflict and the COVID‐19 pandemic, have increased spillovers in the system.

AN OVERVIEW OF DYNAMIC MODEL AVERAGING TECHNIQUES IN TIME‐SERIES ECONOMETRICS

AbstractDynamic model averaging (DMA) has become a widely used estimation technique in macroeconomic applications. Since its introduction in econom(etr)ics by Gary Koop and Dimitris Korobilis in 2009, applications of DMA have increased in unimaginable ways. Besides applying the original (univariate) framework suggested by Koop and Korobilis on the data of interest, for example, the inflation rate of the country of choice or return on the rate of equity, practitioners have been able to use DMA‐based techniques to extend current models, thereby further improving out‐of‐sample forecast accuracy, overcome computational bottlenecks, and even help improve our understanding of economic phenomena by introducing new models. These include using Google search data in combination with the predictive likelihood to govern switching between different predictive regressions in the model set or specifying large time‐varying parameter vector autoregressions that can be estimated without resorting to simulation‐based techniques. This study provides an overview of DMA techniques and the ways in which they have evolved since the contribution of Koop and Korobilis.

A Large Dimensional VAR Model with Time-Varying Parameters for Daily Forex Forecasting

Economic and financial data is extremely volatile relative to the others especially in the time series data. Foreign Exchange market or forex data is one among the others. Despite the fact that it is still impossible to guarantee the profits from trading using advanced mathematical model for this time series, but the predictive performance via the mean squares forecasting error and mean absolute forecasting error obtained from new techniques recently is very much improved. In this paper, we apply vector autoregression (VAR) to forecast most traded forex pairs according to dailyfx.com. The parameters obtained in our work are extraordinary where we use the algorithm so-called dynamic model averaging (DMA) and dynamic model selection (DMS) to deal with the model uncertainty. This algorithm is based closely on Kalman Filtering which has a huge advantage when compared with Markov chain Monte Carlo method. We are able to perform up to 27 variables in VAR and set the lag of 14 periods. We forecast EUR-USD, GBP-USD and EUR-JPY with the number of horizon from $$h=1$$ to $$h=14$$ or one-day-ahead through fourteen-day-ahead prediction where each predicted value is obtained via iterated forecasting method. The findings in this work are: first, DMA algorithm, the Large-VAR with time-varying parameters perform well in predicting GBP-USD. EUR-USD and EUR-JPY. In addition, DMS, an algorithm that selects the highest model probability in Bayesian model averaging obviously outperforms DMA method. Secondly, by having algorithm which is able to track the degree of changes in each dimension for VAR via forgetting factors, the larger matrix in data usage results in more time variation degree in VAR parameters. Furthermore, we also illustrate the dynamic Minnesota prior for each point in time. We finally found that the proposed method delivered excellent predictive performance for large time-varying parameter VAR up to 27 variables.

The Time-series Linkages between US Fiscal Policy and Asset Prices

This article studies the interplay of fiscal policy and asset price returns of the United States in a time-varying parameter vector autoregressive (VAR) model. Using annual data from 1890 to 2013, we study the effects of dynamic shocks to both fiscal policy on asset returns and asset returns on fiscal policy. Distinguishing between low-volatility (bull market) and high-volatility (bear market) regimes together with a time-varying parameter VAR model enables us to isolate the different sizes and signs of responses to shocks during different time periods. The results indicate that increases in the primary surplus-to-gross-domestic-product ratio decrease house returns over the entire sample and at each impulse horizon. Unlike the house return responses, stock returns only decrease in the first year after the fiscal shock but then increase for the following eight years. Furthermore, the findings show that asset return movements affect fiscal policy, whereby fiscal policy responds more to equity returns than to house returns. The response of fiscal policy to asset returns proves relatively stable and constant over time while controlling for various asset return regimes. Asset returns respond uniformly to fiscal policy shocks since the 1900s.

Macroeconomic implications of uncertainty in South Africa

Aim: This article explores the extent to which economic policy uncertainty (EPU) influences selected macroeconomic variables in South Africa (SA). Methods: To this end, I construct a constant parameter vector autoregressive (VAR) model and a time-varying parameter (TVP) VAR model, where the latter model evaluates if the impact of uncertainty on the macroeconomic variables has changed over time. Setting: The models are estimated using quarterly South African data over the period 1990 to 2015, which include industrial production growth, consumer price inflation, 10-year government bond yield, real effective exchange rate, and economic policy uncertainty. Cholesky ordering of the variables are imposed to recover the orthogonal shocks. Results: The results of the constant parameter VAR model suggest that an unanticipated positive shock to the uncertainty index results in a decline in industrial production and real effective exchange rate, while fostering an increase in the general price level and 10-year government bond yield. Time-varying impulse responses show that the impact of uncertainty shocks on the selected macroeconomic variables has declined systematically over time. This is perhaps intuitive as the new unanticipated information is gradually picked up by media over time and incorporated into rational agents’ decision-making. Conclusion: The transmission of a positive uncertainty shock to the real economy has time-varying implications.

Industry bubbles and the cross‐sectional variation of expected consumption growth

AbstractWe study the relationship between the domestic consumption growth and industry bubbles, under the assumption that, at a global level, the bubbles created by productive countries totally or partially offset the bubbles created by unproductive countries. Using a methodology based on a time‐varying parameter vector autoregression, we define the expected consumption growth as a function of the exposure to bubbly episodes and the price of bubbles, among other variables. We test the model for nine European countries. Our results show that the variation of construction and technology bubbles has a strong explanatory power for the domestic consumption growth.

Should I stay or should I go? A latent threshold approach to large‐scale mixture innovation models

SummaryWe propose a straightforward algorithm to estimate large Bayesian time‐varying parameter vector autoregressions with mixture innovation components for each coefficient in the system. The computational burden becomes manageable by approximating the mixture indicators driving the time‐variation in the coefficients with a latent threshold process that depends on the absolute size of the shocks. Two applications illustrate the merits of our approach. First, we forecast the US term structure of interest rates and demonstrate forecast gains relative to benchmark models. Second, we apply our approach to US macroeconomic data and find significant evidence for time‐varying effects of a monetary policy tightening.

A New Approach to Integrating Expectations into VAR Models

This paper proposes a novel Bayesian approach to jointly model realized data and survey forecasts of the same variable in a vector autoregression (VAR). In particular, our method imposes a prior distribution on the consistency between the forecast implied by the VAR and the survey forecast for the same variable. When the prior is placed on unconditional forecasts from the VAR, the prior shapes the posterior of the reduced-form VAR coefficients. When the prior is placed on conditional forecasts (specifically, impulse responses), the prior shapes the posterior of the structural VAR coefficients. {{p}} To implement our prior, we combine importance sampling with a maximum entropy prior for forecast consistency to obtain posterior draws of VAR parameters at low computational cost. We use two empirical examples to illustrate some potential applications of our methodology: (i) the evolution of tail risks for inflation in a time-varying parameter VAR model and (ii) the identification of forward guidance shocks using sign and forecast-consistency restrictions in a monetary VAR model.

Macroeconomic Effects of Government Spending: The Great Recession was (Really) Different

AbstractWe estimate the effect of government spending shocks on the U.S. economy with a time‐varying parameter vector autoregression. The recent Great Recession period appears to be characterized by uniquely large impulse responses of output to fiscal shocks. Moreover, the particularity of this period is underlined by highly unusual responses of several other variables. The pattern of fiscal shock responses neither completely fits the predictions of the New Keynesian model of an economy subject to the zero lower bound on nominal interest rates, nor does it suggest regular variation of fiscal policy effects depending on the state of the business cycle. Rather, the Great Recession period seems special in that government spending shocks had a strongly negative effect on the spread between corporate and government bond yields and a strongly positive effect on consumer confidence and private consumption spending.

Comparing hybrid time-varying parameter VARs

Empirical questions such as whether the Phillips curve or the Okun’s law is stable can often be framed as a model comparison—e.g., comparing a vector autoregression (VAR) in which the coefficients in one equation are constant versus one that has time-varying parameters. We develop Bayesian model comparison methods to compare a class of time-varying parameter VARs we call hybrid TVP-VARs—VARs with time-varying parameters in some equations but constant coefficients in others. Using US data, we find evidence that the VAR coefficients in some, but not all, equations are time varying. Our finding highlights the empirical relevance of these hybrid TVP-VARs.

Comparing Hybrid Time-Varying Parameter VARs

Empirical questions such as whether the Phillips curve or the Okun’s law is stable can often be framed as a model comparison—e.g., comparing a vector autoregression (VAR) in which the coefficients in one equation are constant versus one that has time-varying parameters. We develop Bayesian model comparison methods to compare a class of time-varying parameter VARs we call hybrid TVP-VARs—VARs with time-varying parameters in some equations but constant coefficients in others. Using US data, we find evidence that the VAR coefficients in some, but not all, equations are time varying. Our finding highlights the empirical relevance of these hybrid TVP-VARs.

Bayesian model comparison for time‐varying parameter VARs with stochastic volatility

SummaryWe develop importance sampling methods for computing two popular Bayesian model comparison criteria, namely, the marginal likelihood and the deviance information criterion (DIC) for time‐varying parameter vector autoregressions (TVP‐VARs), where both the regression coefficients and volatilities are drifting over time. The proposed estimators are based on the integrated likelihood, which are substantially more reliable than alternatives. Using US data, we find overwhelming support for the TVP‐VAR with stochastic volatility compared to a conventional constant coefficients VAR with homoskedastic innovations. Most of the gains, however, appear to have come from allowing for stochastic volatility rather than time variation in the VAR coefficients or contemporaneous relationships. Indeed, according to both criteria, a constant coefficients VAR with stochastic volatility outperforms the more general model with time‐varying parameters.

The impact of monetary policy on household consumption in South Africa: Evidence from vector autoregressive techniques

Background: This article adds to scarce sub-Saharan African and South African literature on monetary policy transmission mechanisms by looking into: (1) the Keynesian interest rate channel of monetary policy transmission in South Africa, focussing on the behaviour of household credit and household consumption; (2) using the time-varying parameter vector autoregressive (VAR) techniques with stochastic volatility to capture the time-varying nature of the underlying structure of the South African economy to see whether it performs better than the constant parameter VAR in so doing and (3) policy implications emerging from the findings of the study.Aim: In testing the hypotheses of the interest rate channel of monetary policy transmission, the aim is to see how household credit and ultimately household consumption have evolved in South Africa: (1) before inflation targeting (1994–1999), (2) after inflation targeting (2000–2007) and (3) during the global financial crisis (2007–2012) in response to different monetary policy positioning.Setting: We focus on three periods: post transition from apartheid, during inflation targeting and during the global financial crisis, periods which saw changes in the monetary policy stance in South Africa.Methods: Quarterly data from 1994Q1 to 2012Q4, constant parameter VAR and time-varying parameter vector autoregressive (TVP-VAR) techniques are used in this study. The use of the TVP-VAR is to capture the time-varying nature of the underlying structure of the South African economy and also to investigate whether it performs better than the constant parameter VAR in so doing.Results: The results show that household credit and consumption declined and stayed negative post transition and after inflation targeting – periods of monetary tightening in South Africa - but increased during the global financial crisis, which saw expansionary monetary policy measures aimed at mitigating the negative output gap in South Africa.Conclusion: These changes in household credit and consumption across the different time periods show evidence of the cost of credit effect of monetary policy on household consumption in South Africa. They further reflect the impact of different structural changes and exogenous shocks on monetary policy conduct in South Africa and its pass through effect on household consumption in South Africa. We further conclude that the time TVP-VAR with stochastic volatility performs better than the constant parameter VAR in capturing the time-varying nature of the underlying structure of the South African economy.

A Bayesian Markov-Switching Correlation Model for Contagion Analysis on Exchange Rate Markets

This article develops a new Markov-switching vector autoregressive (VAR) model with stochastic correlation for contagion analysis on financial markets. The correlation and the log-volatility dynamics are driven by two independent Markov chains, thus allowing for different effects such as volatility spill-overs and correlation shifts with various degrees of intensity. We outline a suitable Bayesian inference procedure based on Markov chain Monte Carlo algorithms. We then apply the model to some major and Asian-Pacific cross rates against the U.S. dollar and find strong evidence supporting the existence of contagion effects and correlation drops during crises, closely in line with the stylized facts outlined in the contagion literature. A comparison of this model with its closest competitors, such as a time-varying parameter VAR, reveals that our model has a better predictive ability. Supplementary materials for this article are available online

The relationship between global oil price shocks and China's output: A time-varying analysis

We employ a class of time-varying Bayesian vector autoregressive (VAR) models on new standard dataset of China's GDP constructed by Chang et al. (2015) to examine the relationship between China's economic growth and global oil market fluctuations between 1992Q1 and 2015Q3. We find that: (1) the time varying parameter VAR with stochastic volatility provides a better fit as compared to it's constant counterparts; (2) the impacts of intertemporal global oil price shocks on China's output are often small and temporary in nature; (3) oil supply and specific oil demand shocks generally produce negative movements in China's GDP growth whilst oil demand shocks tend to have positive effects; (4) domestic output shocks have no significant impact on price or quantity movements within the global oil market. The results are generally robust to three commonly employed indicators of global economic activity: Kilian's global real economic activity index, the metal price index and the global industrial production index, and two alternative oil price metrics: the US refiners' acquisition cost for imported crude oil and the West Texas Intermediate price of crude oil.