Analyzing quantile spillover effects among international financial markets

This paper investigates the quantile-based spillover effects among 17 stock markets from January 1993 to January 2022, utilizing a quantile approach based on the variance decomposition of a quantile vector autoregression (QVAR) model. Compared with the traditional mean-based spillover measures, this new quantile approach allows for a nuanced investigation of spillovers at every quantile and capture spillovers under extreme events. The results show that: (1) the total spillover is high and exhibits strong time-varying characteristics, and the tail spillover is higher and more complex in scale and direction; (2) the spillover at each quantile level shows an upward trend, especially during the 2008 crisis and the COVID-19 epidemic; (3) developed countries (or regions) are the net exporters of stock market spillovers, while the developing countries are the net importers; and (4) the 17 stock markets constitute different local financial networks, which may be related to economic conditions and geographical location.     

Multi-scale systemic risk and spillover networks of commodity markets in the bullish and bearish regimes

This paper proposes a new network topology approach based on the STVAR model to identify asymmetric impacts of market conditions on multi-scale systemic risk spillovers of commodity markets. The results show that bearish market conditions enlarge low-frequency systemic risk spillovers in commodity markets, and bullish market conditions have more striking impacts on high-frequency systemic risk spillovers. Furthermore, the center of risk spillover networks varies across the market conditions and frequencies. Specifically, at the high-frequency level, sugar is the largest risk transmitter in bad regimes, and heating oil is in the center of the network in good regimes. At the intermediate frequency level, soybean becomes a more important risk transmitter in both regimes. In other cases, heating oil is the center of risk spillover networks.     

Better to give than to receive: Predictive directional measurement of volatility spillovers

Using a generalized vector autoregressive framework in which forecast-error variance decompositions are invariant to the variable ordering, we propose measures of both the total and directional volatility spillovers. We use our methods to characterize daily volatility spillovers across US stock, bond, foreign exchange and commodities markets, from January 1999 to January 2010. We show that despite significant volatility fluctuations in all four markets during the sample, cross-market volatility spillovers were quite limited until the global financial crisis, which began in 2007. As the crisis intensified, so too did the volatility spillovers, with particularly important spillovers from the stock market to other markets taking place after the collapse of the Lehman Brothers in September 2008.     

Unconventional monetary policy and financialization of commodities

Our paper has two stages of analysis. First of all, we examine whether volatility spillover between US equity and commodity markets has significantly changed with the heavy influx of index traders in commodity derivatives markets, which is a phenomenon referred to as financialization. Given that previous findings show institutional traders enter into commodity markets at high liquidity episodes, in the second stage of our analysis, we investigate the particular impact of US quantitative easing policy on spillover between commodity and US stocks. Our results indicate that during financialization period, spillover from stocks to commodities have significantly increased for almost all commodities. More importantly, we show that quantitative easing is one of the underlying reasons for increasing volatility spillover between markets. Including interest rate, currency factors or default spread does not diminish the explicit role of quantitative easing on spillovers.     

Connectedness of commodity,exchange rate and categorical economic policy uncertainties — Evidence from China

This paper provides a fresh perspective to explore the network correlations among commodity, exchange rate, and categorical economic policy uncertainties (EPU) in China. We try to contribute to the literature by examining the spillover mechanism with a relatively novel connectedness network using the monthly data over the period between June 2006 and January 2021. Our results suggest that prior to the recession, China’s commodity price is subject to greater spillovers from the exchange rate than recessions. The domestic commodity prices are more sensitive to monetary policy uncertainty and fiscal policy uncertainty. The occurrence of COVID-19 revises the dominance in the system from monetary policy uncertainty and fiscal policy uncertainty to trade policy uncertainty.     

The risk spillover between China’s economic policy uncertainty and commodity markets: Evidence from frequency spillover and quantile connectedness approaches

Based on the frequency spillover method extended by Baruník and Křehlík (2018), we explore the risk spillover relationship between China’s economic policy uncertainty (CNEPU) and commodity futures in different frequency domains with daily settlement price data of 14 commodity futures in China. The results show that the risk spillover relationship between CNEPU and the commodity market mainly occurs in the short term. Quantile connectedness results show that economic policy uncertainty, which mainly plays the role of risk transmitter, is more closely related to the commodity market during the market boom and recession. Soybeans, soybean meal, and corn have shown high investment value in the process of market recovery, which is exposed to less risk spillover from policy uncertainty. Finally, the economic crisis with different characteristics will have specific impacts on asymmetric risk spillovers based on certain impact mechanisms.     

The value of multivariate model sophistication: An application to pricing Dow Jones Industrial Average options

We assess the predictive accuracies of a large number of multivariate volatility models in terms of pricing options on the Dow Jones Industrial Average. We measure the value of model sophistication in terms of dollar losses by considering a set of 444 multivariate models that differ in their specification of the conditional variance, conditional correlation, innovation distribution, and estimation approach. All of the models belong to the dynamic conditional correlation class, which is particularly suitable because it allows consistent estimations of the risk neutral dynamics with a manageable amount of computational effort for relatively large scale problems. It turns out that increasing the sophistication in the marginal variance processes (i.e., nonlinearity, asymmetry and component structure) leads to important gains in pricing accuracy. Enriching the model with more complex existing correlation specifications does not improve the performance significantly. Estimating the standard dynamic conditional correlation model by composite likelihood, in order to take into account potential biases in the parameter estimates, generates only slightly better results. To enhance this poor performance of correlation models, we propose a new model that allows for correlation spillovers without too many parameters. This model performs about 60% better than the existing correlation models we consider. Relaxing a Gaussian innovation for a Laplace innovation assumption improves the pricing in a more minor way. In addition to investigating the value of model sophistication in terms of dollar losses directly, we also use the model confidence set approach to statistically infer the set of models that delivers the best pricing performances.     

Null models of economic networks: the case of the world trade web

In all empirical-network studies, the observed properties of economic networks are informative only if compared with a well-defined null model that can quantitatively predict the behavior of such properties in constrained graphs. However, predictions of the available null-model methods can be derived analytically only under assumptions (e.g., sparseness of the network) that are unrealistic for most economic networks like the world trade web (WTW). In this paper we study the evolution of the WTW using a recently-proposed family of null network models. The method allows to analytically obtain the expected value of any network statistic across the ensemble of networks that preserve on average some local properties, and are otherwise fully random. We compare expected and observed properties of the WTW in the period 1950–2000, when either the expected number of trade partners or total country trade is kept fixed and equal to observed quantities. We show that, in the binary WTW, node-degree sequences are sufficient to explain higher-order network properties such as disassortativity and clustering-degree correlation, especially in the last part of the sample. Conversely, in the weighted WTW, the observed sequence of total country imports and exports are not sufficient to predict higher-order patterns of the WTW. We discuss some important implications of these findings for international-trade models.     

Testing for financial spillovers in calm and turbulent periods

In this paper, we investigate financial spillovers between stock markets during calm and turbulent periods. We explicitly define financial spillovers and financial contagion in accordance with the literature and construct statistical models corresponding to these definitions in a Markov switching framework. Applying the new testing methodology based on transition matrices, we find that spillovers from the US stock market to the UK, Japanese and German markets are more frequent when the latter markets are in a crisis regime. However, we reject the hypothesis of strong financial contagion from the US to the other markets.     

Risk spillover and network connectedness analysis of China’s green bond and financial markets: Evidence from financial events of 2015–2020

This study investigated the dynamic return and volatility spillovers, together with the network connectedness analysis between China’s green bond and main financial markets. Based on a multidimensional DCC-GJRGARCH model and the spillover index method, we found significant two-way risk spillovers between the green bond market and traditional bond markets. Moreover, the green bond market was subject to one-way risk spillover from the stock and commodities markets. Meanwhile, risk spillovers between the green bond market, forex market, and monetary market were not significant. Finally, network connectedness analysis provided specific information about connectivity and strength during different subperiods corresponding to financial events. The analysis indicated that under the influence of emergencies, China’s financial market will enhance the risk-spillover level by transforming the same type of market’s internal spillover into cross-market spillover.     

Commodity-price volatility and macroeconomic spillovers: Evidence from nine emerging markets

The recent decade has witnessed wild swings in global commodity prices, with large increases preceding the Global Financial Crisis and steep declines following the crash. Many emerging markets find themselves destabilized by these fluctuations, not only when price increases lead to currency appreciations and reduced competitiveness, but also when price decreases cause capital outflows and deteriorations in the balance of payments. This study examines the volatility processes of six major commodity prices, before applying Multivariate GARCH analysis to examine spillovers among important commodity prices and output, exchange rates, interest rates and inflation in major emerging markets. While each commodity and each country behaves differently, we find that Chile is most closely tied to the copper price, and Indonesia to oil and tin, while neighbors such as Brazil and the Philippines are less affected. Perhaps surprisingly, Russia is found to be highly insulated from fluctuations in world oil prices.     

Network VAR models to measure financial contagion

Financial contagion among countries can arise from different channels, the most important of which are financial markets and bank lending. The paper aims to build an econometric network approach to understand the extent to which contagion spillovers (from one country to another) aris from financial markets, from bank lending, or from both. To achieve this aim we consider a model specification strategy which combines Vector Autoregressive models with network models. The paper contributes to the contagion literature with a model that can consider bank exposures and financial market prices, jointly and not only separately. From an empirical viewpoint, our results show that both bilateral exposures and market prices act as contagion channels in the transmission of shocks arising from a country to other countries.     

Modelling international sovereign risk information spillovers: A multilayer network approach

Applying the TVP-VAR model, we creatively construct multilayer information spillover networks containing return spillover layer, volatility spillover layer and extreme risk spillover layer among 23 countries in the G20 to explore international sovereign risk spillovers. From the perspective of system-level and country-level measures, this article explores the topological structures of static and dynamic multilayer networks. We observe that (i) at the system-level, multilayer measures containing uniqueness edge ratio and average edge overlap show each layer has unique network structures and spillover evolution behavior, especially for dynamic networks. Average connectedness strength shows volatility and extreme risk spillover layers are more sensitive to extreme events. Meanwhile, three layers have highly intertwined and interrelated relations. Notably, their spillovers all show a great upsurge during the crisis (financial and European debt crisis) and the COVID-19 pandemic period. (ii) At the country-level, average overlapping net-strength shows that countries’ roles are different during distinct periods. Multiplex participation coefficient on out-strength indicates we’ll focus on countries with highly heterogeneous connectedness among three layers during the stable period since their underestimated spillovers soar in extreme events or crises. Multilayer networks supply comprehensive information that cannot obtain by single-layer.     

Multivariate volatility forecasts for stock market indices

Volatility forecasts aim to measure future risk and they are key inputs for financial analysis. In this study, we forecast the realized variance as an observable measure of volatility for several major international stock market indices and accounted for the different predictive information present in jump, continuous, and option-implied variance components. We allowed for volatility spillovers in different stock markets by using a multivariate modeling approach. We used heterogeneous autoregressive (HAR)-type models to obtain the forecasts. Based an out-of-sample forecast study, we show that: (i) including option-implied variances in the HAR model substantially improves the forecast accuracy, (ii) lasso-based lag selection methods do not outperform the parsimonious day-week-month lag structure of the HAR model, and (iii) cross-market spillover effects embedded in the multivariate HAR model have long-term forecasting power.     

Unraveling the dynamic changes of high-speed rail network with urban development: Evidence from China

Previous studies have used complex network theory to examine the characteristics of China's High-Speed Rail (HSR). Nevertheless, little attention is paid to China's HSR network's dynamic evolution and the coevolution of node attributes and embedded structural characteristics. Our study builds China's HSR network using a complex network approach and spatial analysis technique to address this issue. Additionally, we employ a stochastic actor-oriented model (SAOM) to properly decompose the three types of effects on the generation of China's HSR network (i.e., network structure, attribute-based similarity, and exogenous control variables). This study discusses the underlying hypotheses and indicates that 3-cycles is a critical structural factor for the formation of China's HSR network. In terms of the effects of attribute-based similarity, cities with similar Gross Domestic Product (GDP) and geographical proximity are essential nodal drivers for the evolution of the HSR network. Notably, the administrative rank of cities exhibits a significantly negative driving force and a alter effect. These findings shed light on the interactive process of urban regional development and the dynamic evolution of the HSR network, assisting the central and local governments in enacting and implementing appropriate HSR planning policies, ultimately achieving effective urban planning and management.     

Connectedness and systemic risk spillovers analysis of Chinese sectors based on tail risk network

This paper investigates the systemic risk spillovers and connectedness in the sectoral tail risk network of Chinese stock market, and explores the transmission mechanism of systemic risk spillovers by block models. Based on conditional value at risk (CoVaR) and single index model (SIM) quantile regression technique, we analyse the tail risk connectedness and find that during market crashes, stock market exposes to more systemic risk and more connectedness. Further, the orthogonal pulse function shows that Herfindahl-Hirschman Index (HHI) of edges has a significant positive effect on systemic risk, but the impact shows a certain lagging feature. Besides, the directional connectedness of sectors shows that systemic risk receivers and transmitters vary across time, and we adopt PageRank index to identify systemically important sector released by utilities and financial sectors. Finally, by block model we find that the tail risk network of Chinese sectors can be divided into four different spillover function blocks. The role of blocks and the spatial spillover transmission path between risk blocks are time-varying. Our results provide useful and positive implications for market participants and policy makers dealing with investment diversification and tracing the paths of risk shock transmission.     

供应链技术溢出影响因素及其作用机制——基于供应链自组织过程的多案例研究

在市场竞争逐渐转变为供应链之间竞争的背景下,为提高供应链的竞争能力,供应链节点企业间的合作必将不断深化,由此,在供应链内部发生技术溢出现象就成为必然。因此,解析供应链技术溢出影响因素及其作用机制,对供应链管理研究具有重要意义。本文以大连市日资企业与本土企业构成的两组典型供应链为例,对不同阶段的技术溢出影响因素进行归纳,并应用自组织理论解析其作用机制,进而建立供应链技术溢出影响因素作用机制模型。     

Spillovers between sovereign CDS and exchange rate markets: The role of market fear

As important variables in financial market, sovereign credit default swaps (CDS) and exchange rate have correlations and spillovers. And the volatility spillovers between the two markets become further complicated with the effect of market fear caused by extreme events such as global pandemic. This paper attempts to explore the complex interactions within the “sovereign CDS-exchange rate” system by adopting the forecast error variance decomposition method. The results show that there is a relatively close linkage between the two markets and the total spillover index of the system is dynamic. For most of the past, the exchange rate has a higher spillover effect on the sovereign CDS than vice versa. Moreover, after the market fear variables are introduced, the “sovereign CDS-exchange rate” system and market fear variables present bidirectional spillovers. The results of the study have particular significance for maintaining the financial stability and preventing risk contagion between markets.     

COVID-19 and asymmetric volatility spillovers across global stock markets

In this study, I improve the assessment of asymmetry in volatility spillovers, and define six asymmetric spillover indexes. Employing Diebold-Yilmaz spillover index, network analysis, and my developed asymmetric spillover index, this study investigates the time-varying volatility spillovers and asymmetry in spillovers across stock markets of the U.S., Japan, Germany, the U.K., France, Italy, Canada, China, India, and Brazil based on high-frequency data from June 1, 2009, to August 28, 2020. I find that the global markets are well connected, and volatility spillovers across global stock markets are time-varying, crisis-sensitive, and asymmetric. Developed markets are the main risk transmitters, and emerging markets are the main risk receivers. Downside risk dominates financial contagion effects, and a great deal of downside risk spilled over from stock markets of risk transmitters into the global markets. Moreover, during the coronavirus recession, the total degree of volatility spillover is staying at an extremely high level, and emerging markets are the main risk receivers in the 2020 stock markets crash.     

Foreign direct investment and productivity spillovers in the Chinese manufacturing sector

In this paper a simple simultaneous model is constructed to examine whether there are productivity spillovers from both the presence of foreign direct investment (FDI) and competition between local and foreign firms. The model is tested on the data from China’s latest industrial census in 1995. The results indicate that the extent to which spillovers occur varies with different types of ownership of local firms and of FDI. While collective- and private-owned enterprises benefit from demonstration and contagion effects from foreign presence, productivity gains of state-owned enterprises largely come from competition with foreign firms. Productivities of local and foreign firms are jointly determined. Evidence also suggests that market-oriented FDI tends to generate spillovers mainly via competition with local firms.