Generalized Variance Decomposition Research Articles

Monitoring the Dynamic Networks of Stock Returns with an Application to the Swedish Stock Market

AbstractIn this paper, two approaches for measuring the distance between stock returns and the network connectedness are presented that are based on the Pearson correlation coefficient dissimilarity and the generalized variance decomposition dissimilarity. Using these two procedures, the center of the network is determined. Also, hierarchical clustering methods are used to divide the dense networks into sparse trees, which provide us with information about how the companies of a financial market are related to each other. We implement the derived theoretical results to study the dynamic connectedness between the companies in the Swedish capital market by considering 28 companies included in the determination of the market index OMX30. The network structure of the market is constructed using different methods to determine the distance between the companies. We use hierarchical clustering methods to find the relation among the companies in each window. Next, we obtain a one-dimensional time series of the distances between the clustering trees that reflect the changes in the relationship between the companies in the market over time. The method from statistical process control, namely the Shewhart control chart, is applied to those time series to detect abnormal changes in the financial market.

How do climate risks impact the contagion in China's energy market?

Energy security is a critical facet of national security, especially in energy-importing countries, and significant fluctuations in the energy market can profoundly influence the real economy. With the intensification of global climate change, extreme weather events and diverse energy transition policies emerge as prominent risk factors driving energy market volatility. In this study, we employed a time-varying parameter vector autoregression (TVP-VAR) model and the spillover index method of generalized variance decomposition to assess the endogenous contagion and exogenous shock risk of China's energy prices and the systematic risk in the broader energy market. Our analysis included the coal, oil, and natural gas prices in both Chinese and international markets. The results indicate that the international oil market plays a dominant role in the energy risk spillover network, with the exogenous shock risk in China's energy market surpassing the endogenous contagion risk. Further, quantile models were used to investigate the impact of climate risks, namely climate disasters, climate policy uncertainty, and climate risk concern. We found that in high-risk environments, all climatic factors exert pronounced impacts on China's energy market risks, with climate risk concern being especially influential. Overall, this study has important policy implications for energy security and adaptation to climate change.

Network-Based prediction of financial cross-sector risk spillover in China: A deep learning approach

There are complex risk correlations between financial sectors, and the risks generated by different financial sectors propagate, accrue, and cluster through the network of correlations, posing a threat to the entire financial system. This research constructs static and dynamic cross-sector risk spillover networks using VAR and generalized variance decomposition, and forecasts the evolution of risk spillover networks using recurrent neural network models such as RNN and LSTM. The results indicate that the risk spillover network will change rapidly in response to risk events, and that the total volatility spillover will increase during times of crisis, while banks and securities are the most important risk propagating and receiving sectors. The LSTM model can achieve more effective dynamic prediction of the multidimensional network, and the predicted network is essentially consistent with the actual network. According to the findings of the study, forecasting changes in the structure of financial sector networks may provide early warning of systemic financial risk and assist to a better understanding of the risk link between financial sectors.

Research on Risk Spillover Measurement of Fintech System Based on DCC-GARCH and Generalized Variance Decomposition Network Model

Research on Risk Spillover Measurement of Fintech System Based on DCC-GARCH and Generalized Variance Decomposition Network Model

Risk Spillovers between China’s Carbon and Energy Markets

In recent years, with the intensification of global warming and the greenhouse effect, the global consensus has focused on efficient, clean, low-carbon, and green development as a means of achieving new economic growth. China, as a major carbon emitter, has been at the forefront of efforts to reduce carbon emissions. The establishment of the carbon emissions trading market, commonly known as the “carbon market”, provides an economic solution for reducing carbon emissions in both the carbon and energy markets. As China’s carbon market continues to grow rapidly, fluctuations in the energy or carbon markets caused by information shocks can easily spread between the two markets, leading to increased interconnectedness. Moreover, the spillover effect of the volatility between China’s carbon market and energy market is not constant, and the intensity and direction of this effect vary depending on different market volatility levels and periods. Therefore, it is crucial to conduct a comprehensive study on the characteristics of the volatility spillover effect between China’s carbon market and energy market and to fully understand the mechanism of energy regulation on carbon prices. This research will have significant practical implications for promoting the establishment of a well-functioning internal price transmission mechanism between China’s carbon market and energy market. This study took the risk spillover between the carbon market and energy market as the research object and systematically combed through its pricing mechanism and spillover impact. Through constructing the DY overflow index model based on a VAR model and generalized variance decomposition method, this study explored the linkage between China’s carbon and energy markets, i.e., the linkage of price fluctuations between China’s energy and carbon markets, as well as the time-varying nature of inter-market spillovers, and provides suggestions on the risk control of price fluctuations between the carbon and energy markets.

Frequency heterogeneity of tail connectedness: Evidence from global stock markets

The propagation of tail risks poses a significant threat to global financial stability. Although a growing body of literature studies tail connectedness across global stock markets, little attention has been given to the frequency heterogeneity of tail connectedness. To fill this gap, we develop the frequency-heterogeneous tail connectedness measures using spectrum analysis and LASSO quantile regression within the generalized variance decomposition framework. The results show that tail connectedness is more informative than mean-based connectedness, and the importance of heterogeneous tail connectedness in predicting market downside risk depends on market conditions. Specifically, tail spillovers are largely driven by the short-term component during non-crisis periods, while the long-term component will rise sharply and dominate during crisis periods. This suggests that frequency-heterogeneous tail connectedness can be taken as a refined early warning signal for systemic risk.

Connectedness structure of Pan-European Equity REITs

ABSTRACT This paper examines the interconnectedness among European equity REITs (Real Estate Investment Trusts) due to increased financialization of the real estate sector and European economic integration. It uses two models, Diebold and Yilmaz's generalised variance decomposition and Barunik and Krehlik's time-frequency dynamics framework. The study covers a sample of 42 Pan-European REITs over a 15-year period from 2004 to 2018 and finds that the connectedness among REITs is influenced by the geographical location and performance of commercial real estate sub-sectors. The dynamic analysis shows that the source of shocks does not determine the persistence, but rather how the markets interpret the shocks during high-frequency intervals. The paper concludes that the connectedness structure among REITs reflects uncertainty interaction, with some effects observed mainly at high frequency levels, and few unexpected shocks found in the long-term stability of REIT returns.

High-dimensional CoVaR risk spillover network from oil market to global stock markets—Lessons from the Kyoto Protocol

This paper explores the impact of the Kyoto Protocol by investigating the correlation and risk spillover between the crude oil market and the stock markets of 28 countries during its two commitment periods. Besides time-varying Copula-CoVaR models, the Adaptive Lasso-VAR model with oracle properties is employed in generalized variance decomposition, and a risk connectedness network is constructed to explore risk spillovers between the stock markets of various countries when the crude oil market is at risk. The results reveal positive correlations between the crude oil market and stock markets, which become weaker in the second commitment period than in the first. The crude oil market has both upside and downside spillover effects to most stock markets during both commitment periods, and the upside risk spillover effect is stronger than the downside effect. Overall, most non-signatories of the Kyoto Protocol are net receivers of risk spillovers when the crude oil market is at risk, while most signatories are net exporters of risk spillovers.

Time-Frequency Spillover Effect of Domestic and Foreign Commodity Markets on China’s Price Levels

ABSTRACT In this study, a dynamic spillover index method based on generalized variance decomposition is used to measure the volatility and return spillover effects of futures and spot markets on China’s price levels (consumer and producer price indices) at different time-frequencies. The results indicate that both domestic and foreign futures and spot markets exerted time-varying volatility and return spillover effects on China’s price levels and that major global crises aggravate the overall volatility and return spillover effect of the global futures and spot markets. In addition, the spillover effect of domestic and foreign futures and spot markets on China’s price levels is asymmetrical. The results of this study also indicate that the longer the frequency of a cycle is, the stronger the spillover effect of each market on price fluctuations is, and the weaker the return spillover effect is. Finally, the results demonstrate that China can use a combination of monetary easing and credit tightening to control inflation more effectively.

Aggregate, asymmetric and frequency-based spillover among equity, precious metals, and cryptocurrency

This study evaluates the portfolio diversification potential of different classes of assets-equity, cryptocurrency and precious metals-using total, asymmetric and frequency-based spillover transmission framework. The VAR-based generalized variance decomposition method is used to analyse the daily prices of S&P 500, bitcoin, gold, silver and platinum between April 2011 through January 2021. The results of aggregate spillover support bitcoin as a potential diversifier due to its isolation from other sets of assets. The decomposition of overall spillover into downside and upside spillover reveals a higher downside connectedness than the upside, suggesting an asymmetric interdependence amongst these markets. Moreover, the frequency based aggregate spillovers suggest the connectedness is driven mostly by the shorter time-horizons. The study provides important policy implications for market participants with distinct investment objectives.

Research on China's Risk of Housing Price Contagion Based on Multilayer Networks.

The major issue in the evolution of housing prices is risk of housing price contagion. To model this issue, we constructed housing multilayer networks using transfer entropy, generalized variance decomposition, directed minimum spanning trees, and directed planar maximally filtered graph methods, as well as China’s comprehensive indices of housing price and urban real housing prices from 2012 to 2021. The results of our housing multilayer networks show that the topological indices (degree, PageRank, eigenvector, etc.) of new first-tier cities (Tianjin, Qingdao, and Shenyang) rank higher than those of conventional first-tier cities (Beijing, Shanghai, Guangzhou, and Shenzheng).

Research on stock volatility risk and investor sentiment contagion from the perspective of multi-layer dynamic network

In the post-epidemic period, the international economic structure has been readjusted, with risks contagious across financial and economic systems. This paper primarily uses the high-frequency TENET network and the Granger-causality network to describe the interconnectedness between the tail risk of stock volatility and investor sentiment, then the two-layer network is constructed by the generalized variance decomposition method to examine the inter-layer connectedness. Based on the two-layer network, the heterogeneity frequency response of network connectedness and dynamic network structure are further analyzed from the perspective of frequency domain. The study found that the tail risk of high-frequency stock volatility displays industry heterogeneity and time-varying property, and investor sentiment contagion network provides information transmission medium for stock risk. The double-layer network study found that stock volatility in consumer goods industry exhibits higher risk spillover to investor sentiment. The diversified financial industry, real estate industry and energy industry in the two-layer network are systemically important industries. In addition, the study of the frequency domain dynamic network found that the connectedness volatility in the short-term risk network of stock volatility was significantly higher than that of the investor sentiment network, and the short-term risk spillover effect of the network played a leading role in the total risk spillover. The research conclusions provide reference for preventing systemic risks from the perspective of systemically important industries and cyclical fluctuations.

The impacts of global liquidity on international housing prices

PurposeThe purpose of this paper is to examine how global liquidity affects international housing prices. The data sample covers 35 economies from 2000Q1 to 2017Q4.Design/methodology/approachThe existing papers seldom investigated whether the impacts of global liquidity on housing prices display differences between advanced and developing economies. Cesa-Bianchi et al. (2015) is an exceptional study in that they focused on the impulse response of house price volatility to global liquidity shocks but did not examine the long-run equilibrium relationship. To fill the gap in the existing research, this paper used panel cointegration of Pedroni (2000, 2004) to estimate the long-run linkage between global liquidity and housing prices in both advanced and developing economies, and generalized impulse response function (GIRF) and generalized variance decomposition (GVDC) were also applied to capture the relative strengths and contribution of global liquidity shock on house price volatility.FindingsFirst, the global liquidity elasticity of housing prices is 0.0679 in developing economies, and 0.0454 in advanced economies, implying that the positive effect of global liquidity on housing prices is higher in developing economies. Next, the results of generalized impulse response indicate that the innovation of global liquidity can significantly and positively impact housing prices only in developing economies and the duration is two quarters. Third, in light of the long-run portions of the global liquidity shock on house price volatility in individual economies, the two highest portions are 28.51% in the USA and 20.04% in the UK, while there are low portions, less than 10%, in most of the European economies. Moreover, comparing the long-run contributions of global liquidity and other variables shock on house price volatility, the contribution of the global liquidity shock ranks the highest or second highest in 21 out of 35 economies, confirming that it played a more important role than other economic variables in explaining house price volatility for most economies.Originality/valueCompared with the related literature, the contributions of this paper are as follows. First, except for Cesa-Bianchi et al. (2015), the existing papers seldom investigated whether the impacts of global liquidity on housing prices display differences between advanced and developing economies. Hence, the study adopted a wider data sample, including 7 developing economies and 28 advanced economies, to examine the differences in the impact of global liquidity on housing prices between advanced and developing economies. Second, most of the relative literature calculated global liquidity by applying a monetary aggregate, such as M2 or M3, while Cesa-Bianchi et al. (2015) argued that global liquidity being measured by the international supply of credit is intuitively connected to housing prices. This paper follows the argument of Cesa-Bianchi et al. (2015) to use the international supply of credit as the measure of global liquidity, and both the long-run effects and the short-run relative strengths of global liquidity on housing prices are analyzed. Hence, this paper uses not only the GIRF to discuss the short-run relative strengths, as with Cesa-Bianchi et al. (2015) but also the panel cointegration of Pedroni (2000, 2004) to identify the long-run linkage between global liquidity and housing prices. Moreover, GVDC was used to estimate the contribution of a global liquidity shock on house price volatility in individual economies, which can confirm that global liquidity innovations are a very important factor in explaining house price volatility in most countries.

The dynamic interaction between investor attention and green security market: an empirical study based on Baidu index

PurposeThis paper aims to explore the interaction between investor attention and green security markets, including green bonds and stocks.Design/methodology/approachThis study takes the Baidu index of “green finance” as the proxy for investor attention and constructs several generalized prediction error variance decomposition models to investigate the interdependence. It further analyzes the dynamic interaction between investor attention and the return and volatility of green security markets using the rolling time window.FindingsThe empirical analysis and robustness test results reveal that the spillovers between investor attention and the return and volatility of the green bond market are relatively stable. In contrast, the spillover level between investor attention and the green stock market displays significant time-varying and asymmetric effects. Moreover, the volatility spillover between investor attention and green securities is vulnerable to major financial events, while the return spillover is extremely sensitive to market performance.Originality/valueThe conclusion further expands the practical application and theoretical framework of behavioral finance in green finance and provides a new reference for investors and regulators. Besides, this study also lays a theoretical basis for investors to focus on the practical application of volatility prediction and risk management in green securities.

Sector Volatility Spillover and Economic Policy Uncertainty: Evidence from China’s Stock Market

Following generalized variance decomposition, we identify the transmission structure of financial shock among ten sectors in China. Then, we examine whether economic policy uncertainty (EPU) affects it through GARCH-MIDAS regression. We find that consumer discretionary, industrials, and materials sectors are systemically important industries during the sample period. Further research of dynamic analysis shows that each sector acts in a time-varying role in this structure. The results of the GARCH-MIDAS regression indicate that none of the selected EPU indexes has a significant long-term impact on the total volatility spillover of the inter-sector stock market in China. However, the EPUs do affect some sectors’ spillover indexes in the long run, and they are significantly heterogeneous. This paper can provide regulatory suggestions for policymakers and reasonable asset allocation and risk avoidance methods for investors.

Liquidity spillover in foreign exchange markets

We use an index of spillover based on the generalized variance decomposition developed by Diebold and Yilmaz (2009, 2012) to measure the spillover in liquidity in currency markets between 2008 and 2015. The results show that the liquidity spillovers across nine major foreign exchange markets increase with global risk and funding constraint. We also find a substantial difference in the propagation pattern of liquidity between the funding currencies and investment currencies markets.

Sector connectedness in the Chinese stock markets

Uncovering the risk-transmitting path within economic sectors in China is crucial for understanding the stability of the Chinese economic system, especially under the current situation of the China–US trade conflicts. In this paper, we aim to uncover the risk spreading channels by means of volatility spillovers within the Chinese sectors using stock market data. By applying the generalized variance decomposition framework based on the VAR model and the rolling window approach, a set of connectedness matrices is obtained to reveal the overall and dynamic spillovers within sectors. We find that 17 sectors (mechanical equipment, electrical equipment, utilities, and so on) are risk transmitters and 11 sectors (national defense, bank, non-bank finance, and so on) are risk takers during the whole period. Under the extreme risk events (i.e., the global financial crisis, the Chinese interbank liquidity crisis, the Chinese stock market crash, and the China–US trade war), the connectedness measures significantly increase and the financial sectors play a buffer role in stabilizing the economic system. Our results are robust to changes of the model parameters. Our study not only uncovers the spillover effects within the Chinese sectors, but also highlights the deep understanding of the risk contagion patterns in the Chinese stock markets.

Investigating the Asymmetry in Trade Balance Response to Oil Price Changes: Evidence from China

Examining the relation between oil price changes and the trade balance is important for oil-importing countries to formulate policy solutions. This study contributes to the literature by examining the asymmetric effects of changes in oil prices on the trade balance of China, the world’s largest crude oil importer. To this end, the Nonlinear Autoregressive Distributed Lag (NARDL) cointegration approach and Pesaran’s Generalized Variance Decompositions technique are adopted. The results reveal that the trade balance responds asymmetrically to oil price changes in the long run, while the responses are symmetric in the short run. The Generalized Variance Decompositions results indicate that the trade balance is more sensitive to the oil price increases than to decreases. These results indicate that China should support energy-efficient systems to reduce its dependency on imported oil.

Jump volatility spillover network based measurement of systemic importance of Chinese financial institutions

AbstractThe identification of systemically important financial institutions (SIFIs) is an important measure to deal with systemic risks. To achieve this goal, we first use generalized variance decomposition method and granger causality test to construct jump volatility spillover networks of Chinese financial institutions based on the 5‐min high‐frequency data. Then, out‐strength and in‐strength are adopted to analyze the SIFI. Finally, we use panel data regression model to investigate the determinant of the SIFIs. The empirical results show that: (a) The network density reaches a peak when the financial system under pressure during the China's stock market disaster of 2015. (b) Large banks and insurances usually display systemic importance, while some small financial institutions are also SIFIs due to their high value of out‐strength and in‐strength. (c) There are obvious differences in the factors that affect the out‐strength and in‐strength based on panel data regression model, but turnover rate, jump volatility, firm size and growth rate of total assets are the common driving factors.

A Weighted and Directed Perspective of Global Stock Market Connectedness: A Variance Decomposition and GERGM Framework

The financial penetration accelerated by economic globalization and financial liberalization has inevitably induced market co-movement and the rising likelihood of cross-market risk contagion. An in-depth analysis concerning the carrier of risk contagion, i.e., market connectedness network, is of great significance for risk management. This study aims to establish a holistic framework to shed light on the topological dynamics and the evolving channels of connectedness network among 24 major stock markets in two aspects; namely, a dynamic perspective juxtaposing crisis and non-crisis periods, and a contrasting perspective between risk absorption and risk spillover. To this end, a methodological framework of the generalized variance decomposition and generalized exponential random graph models (GERGM) is constructed, in which the former method formulates the asymmetric causal relationships of stock return volatility among countries and regions into weighted and directed networks, and the latter method simulates and models the varying attributes of different contagion channels in the formation of tie directions and weights. The results indicate that the global stock market network reflects typical event-driven and time-varying characteristics. Countries and regions that rely heavily on foreign direct investment (FDI) are more likely to absorb risks, especially during the post-crisis recovery period, while countries and regions with higher foreign portfolio holdings are more inclined to risk spillover, especially during the subprime crisis. Geographical proximity and bilateral trade volume amplify risk contagion, whereas foreign exchange reserve holding improves robustness. This holistic framework allows the identification of the direction and intensity of risk contagion and the clarification of priority of risk transmission channels in different stages, thus reducing the uncertainty of risk management and providing insights into the macro-prudential managements toward sustainable economic development.