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.     

The relationship between information technology capability,inventory efficiency,and shareholder wealth: A firm-level empirical analysis

Inventories represent an important strategic resource for firms, with implications for shareholder wealth. As such, firms expend considerable effort in managing their inventories efficiently. Among other factors, information technology (IT) capability can play an important role in enabling inventory efficiency and financial performance. However, insight into the chain-of-effects linking IT capability, inventory efficiency, and stock market returns and risk remains limited. In this paper, we provide a conceptual model outlining the relationships between these constructs. Next, we evaluate the model using secondary information on firms from multiple industries across the 10-year time period of 2000–2009. Our analysis confirms that firms’ IT capability plays a significant role in enhancing their inventory efficiency, which, in turn, is observed to increase stock market returns. Our results also reveal that firms’ IT capability directly reduces their stock market risk and enhances their stock market returns. Taken together, these findings, along with the conceptual model that we advance, have important research and managerial implications.     

Risk spillover analysis of China’s financial sectors based on a new GARCH copula quantile regression model

This study employs a new GARCH copula quantile regression model to estimate the conditional value at risk for systemic risk spillover analysis. To be specific, thirteen copula quantile regression models are derived to capture the asymmetry and nonlinearity of the tail dependence between financial returns. Using Chinese stock market data over the period from January 2007 to October 2020, this paper investigates the risk spillovers from the banking, securities, and insurance sectors to the entire financial system. The empirical results indicate that (i) three financial sectors contribute significantly to the financial system, and the insurance sector displays the largest risk spillover effects on the financial system, followed by the banking sector and subsequently the securities sector; (ii) the time-varying risk spillovers are much larger during the global financial crisis than during the periods of the banking liquidity crisis, the stock market crash and the COVID-19 pandemic. Our results provide important implications for supervisory authorities and portfolio managers who want to maintain the stability of China’s financial system and optimize investment portfolios.     

Topological properties of reconstructed credit networks and banking systemic risk

Financial bipartite networks provide channels for contagion risks and their topological properties determine financial stability. We enrich the bipartite network reconstruction methods proposed by Ramadiah et al. (2020) and extend them to the Chinese banking system. By comparing the reproducibility of the real credit market and the corresponding systemic risk, the impact of topological properties for different reconstructed bipartite networks on financial stability is analyzed. The empirical evidence shows that network reconstruction methods based on maximum entropy ensembles capture more properties in the real credit network. It also highlights that the different systemic risk level is mainly contributed by the topological properties based on common exposures. These analyses for topological properties provide regulatory insights for systemic risk prevention. It shows that reducing credit similarity across banks while increasing credit diversification in different sectors helps to control systemic risk. The results imply the possibility of increasing financial stability through the macro-regulation of the credit market structure.     

Bank systemic risk and CEO overconfidence

This study examines the relationship between CEO overconfidence and banking systemic risk. We employ the CoVaR (Conditional Value-at-Risk) approach to measure a bank's contribution to systemic risk and compute its MES (Marginal Expected Shortfall) and SRISK (Systemic Risk index) to measure the exposure to banking systemic risk. We use a stock options based measure for CEO overconfidence and explore how managerial overconfidence could be associated with banking systemic risk. Using data for U.S. banks from 1995–2014, we find evidence that banks with overconfident CEOs have a higher contribution and exposure to systemic risk than banks with non-overconfident CEOs. We also show that the impact of CEO overconfidence contributed significantly more to systemic risk during the financial crisis of 2008–2009.     

Systemic risk of Chinese financial institutions and asset price bubbles

Although there has not been a large-scale systemic crisis in China, high-risk financial events have occurred continuously in recent years. This research thus creatively analyzes the determinants of systemic risk for Chinese financial institutions from the view of asset price bubbles. First, we identify bubbles in the China stock and real estate markets on the basis of the generalized sup Augmented Dickey-Fuller (GSADF) model and explain the reasons for bubble formations according to the stage of China's economic development and policies implementation. At this stage, considering the differences in economic development levels of different cities, the real estate bubbles in the first, second and third tier cities and the whole country were innovatively identified. Second, on the basis of the DCG-GARCH-CoVaR model to measure the systemic risk of listed financial institutions in China and to classify institutions, the results show that the main source of such risk is the banking sector. Furthermore, by constructing regression models, stock market bubbles and real estate bubbles both positively correlate with systemic risk throughout the sample period. Meanwhile, the impact of bubbles on the systemic risk of different types of financial institutions was taken into account so that regulators prioritized different types of institutions with different characteristics when faced with decisions. Finally, we provide macro-prudential policy advice to regulators in order to weaken the impact of bubbles on financial stability to avoid systemic crises.     

Nowcasting and forecasting global financial sector stress and credit market dislocation

We introduce a new international model for the systematic distress risk of financial institutions from the US, the European Union, and the Asia-Pacific region. Our proposed dynamic factor model can be represented as a nonlinear, non-Gaussian state space model with parameters that we estimate using Monte Carlo maximum likelihood methods. We construct measures of global financial sector risk and of credit market dislocation, where credit market dislocation is defined as a significant and persistent decoupling of the credit risk cycle from macro-financial fundamentals in one or more regions. We show that, in the past, such decoupling has preceded episodes of systemic financial distress. Our new measure provides a risk-based indicator of credit conditions, and as such, complements earlier quantity-based indicators from the literature. In an extensive comparison with such quantity-based systemic risk indicators, we find that the behaviour of the new indicator is competitive with that of the best quantity-based indicators.     

A description of the COVID-19 outbreak role in financial risk forecasting

This study aims to describe the risk of the system composed on the market indexes of the countries that were more affected by COVID-19. Our sample encompasses the thirty-five countries with more cases and/or deaths caused by COVID-19 until November 2020. As a second contribution, we describe the risk of each market index individually. As a general pattern, we note that losses and individual and systemic risks peaked in March 2020. We verify that countries that were epicenters of the COVID-19 pandemic experienced critical levels of risk, which is partially explained by more stringent confinement measures since these are the ones whose labor markets will suffer more in the medium and long run. We perceived a market recovery, arguably due to the low-interest rates and expansive actions taken by central banks. Nonetheless, we also observed that the systemic risk returned to pre-pandemic levels at the end of 2020.     

Systemic risk in European financial and energy sectors: Dynamic factor copula approach

We perform an analysis of systemic risk in financial and energy sectors in Europe using daily time series of CDS spreads. We employ the factor copula model with GAS dynamics from Oh and Patton (2018) for the purpose of estimating dependency structures between market participants. Based on the estimated models, we perform Monte Carlo simulations to obtain future values of CDS spreads, and then measure the probability of systemic events at given time points. We conclude that substantially higher systemic risk is present in the financial sector compared to the energy sector. We also find that the most systemically vulnerable financial and energy companies come from Spain.     

A time-varying copula approach for constructing a daily financial systemic stress index

This paper develops a financial systemic stress index (FSSI) for the US financial market. We propose a time-varying copula method to model the dependence structure among financial sectors in order to build a correlated financial stress model that can signal systemic financial risks. The copula method is preferable to the traditional approach, enabling the modeling of non-linear correlations. Our analyses show that the dependencies across banking, security, and forex markets are best modeled by Archimedian copulas. Finally, we conduct a Markov Switching Autoregressive (MS-AR) model for FSSI and identify high financial stress episodes taking place in 2008–2009, 2011 and 2020.     

In union lies strength: Collaborative competence in new product development and its performance effects

It is widely recognized that new product development (NPD) is a highly interdependent process, yet efforts to empirically model the interdependence and examine its effect on firm performance are scarce. Our study addresses this research gap. We model firms’ abilities to collectively collaborate with suppliers, customers, and internal employee teams in NPD as collaborative competence and examine its impact on project and market performance. Using responses collected from 189 NPD managers, we find empirical evidence for collaborative competence and its differential impact on project and market performance. Specifically, we find that collaborative competence has a direct impact on project performance, but its impact on market performance is indirect, mediated through project performance. The results have significant managerial implications; achieving superior market performance from inter- and intra-organizational involvement is contingent on achieving superior project performance, and companies that fail to achieve desired project performance outcomes will also fail in achieving market performance goals.     

Overlapping portfolios,contagion, and financial stability

We study the problem of interacting channels of contagion in financial networks. The first channel of contagion is counterparty failure risk; this is captured empirically using data for the Austrian interbank network. The second channel of contagion is overlapping portfolio exposures; this is studied using a stylized model. We perform stress tests according to different protocols. For the parameters we study neither channel of contagion results in large effects on its own. In contrast, when both channels are active at once, bankruptcies are much more common and have large systemic effects.     

Tail risk and investors’ concerns: Evidence from Brazil

I estimate tail risk for Brazil from January 2001 to July 2020 and investigate the origins of tail risk variation. The tail risk measure peaks at stock market crashes, financial crises, political shocks and disaster events such as the coronavirus pandemic. Moreover, I find that tail risk is countercyclical, has strong predictive power for market returns and negatively predicts real economic activity. In order to identify the investors’ concerns associated with tail risk, I extract daily news from the largest financial newspaper in Brazil. The co-movement between news and tail risk indicates that tail risk variation is mainly driven by disaster concerns, followed by economic and government uncertainty. While economic uncertainty explains the countercyclical property of tail risk, investors only require compensation for bearing tail risk implied by disaster concerns. Similarly, tail risk negatively impacts real outcomes because of the disaster concerns that it identifies. These findings support recent models explaining asset pricing puzzles with time-varying disaster risk.     

The impact of national culture on systemic risk

We investigate the effects of national culture on systemic risk using a comprehensive dataset from global banks in 58 countries over the period 2003–2016. Our results reveal that systemic risk measures are associated with cultural values. In particular, our results show that individualism and masculinity are the main drivers of banks' contribution to systemic risk. In addition, the impact of cultural variables on the systemic risk measures is nonlinear. This variation may be driven by both information in the national cultural measures and the skewness of the systemic risk measures. The findings have implications for prudential policies: designing uniform prudential and regulatory policies in banking to avoid financial distress for countries with heterogeneous cultures might not have the desire impact; rather, they might be more effective if the type of culture in each individual country is considered.     

Corporate financial determinants of foreign direct investment

Financial market incompleteness and (partial) segmentation of financial markets internationally may endow some firms with a financial advantage which can be exploited through foreign direct investment. We argue that this advantage appears as a distinct cost-of-capital effect on FDI, and identify possible channels for such an effect. Using a sample of European firms’ cross-border acquisitions, and controlling for traditional firm-level determinants of FDI, we find strong support for a cost-of-equity effect, whereas the effect of debt costs is indeterminate. Moreover, financial FDI determinants are more important for firms with high knowledge intensity and for firms resident in relatively less financially developed countries.     

Bank herding and systemic risk

Bank herding behavior is often hypothesized to increase systemic risk, but the actual effect is unclear ex-ante from the theory and unknown ex-post from the data. We expand the literature on this topic in several dimensions – posing alternative hypotheses regarding the effects of herding in asset, liability, and off-balance sheet portfolios; developing a novel set of bank-specific, time-varying measures of herding in these portfolios; and empirically testing the relations between bank herding for all three portfolios and bank systemic risk contributions. We find nuanced empirical results that differ by portfolio, bank size class, and periods before versus after TARP.     

The impact of systemic and illiquidity risk on financing with risky collateral

Repurchase agreements (repos) are one of the most important sources of funding liquidity for many financial investors and intermediaries. In a repo, some assets are given by a borrower as collateral in exchange of funding. The capital given to the borrower is the market value of the collateral, reduced by an amount termed as haircut (or margin). The haircut protects the capital lender from loss of value of the collateral contingent on the borrower׳s default. For this reason, the haircut is typically calculated with a simple Value at Risk estimation of the collateral for the purpose of preventing the risk associated to volatility. However, other risk factors should be included in the haircut and a severe undervaluation of them could result in a significant loss of value of the collateral if the borrower defaults. In this paper we present a stylized model of the financial system, which allows us to compute the haircut incorporating the liquidity risk of the collateral and, most important, possible systemic effects. These are mainly due to the similarity of bank portfolios, excessive leverage of financial institutions, and illiquidity of assets. The model is analytically solvable under some simplifying assumptions and robust to the relaxation of these assumptions, as shown through Monte Carlo simulations. We also show which are the most critical model parameters for the determination of haircuts.     

Buyer perceptions of supply disruption risk: A behavioral view and empirical assessment

As supply chains become more complex, firms face increasing risks of supply disruptions. The process through which buyers make decisions in the face of these risks, however, has not been explored. Despite research highlighting the importance of behavioral approaches to risk, there is limited research that applies these views of risk in the supply chain literature. This paper addresses this gap by drawing on behavioral risk theory to investigate the causal relationships amongst situation, representations of risk, and decision-making within the purchasing domain. We operationalize and explore the relationship between three representations of supply disruption risk: magnitude of supply disruption, probability of supply disruption, and overall supply disruption risk. Additionally, we draw on exchange theories to identify product and market factors that impact buyers’ perceptions of the probability and magnitude of supply disruption. Finally, we look at how representations of risk affect the decision to seek alternative sources of supply. We test our model using data collected from 223 purchasing managers and buyers of direct materials. Our results show that both the probability and the magnitude of supply disruption are important to buyers’ overall perceptions of supply disruption risk. We also find that product and market situational factors impact perceptions of risk, but they are best understood through their impact on perceptions of probability and magnitude. Finally, we find that decisions are based on assessments of overall risk. These findings provide insight into the decision-making process and show that all three representations of risk are necessary for fully understanding risky decision-making with respect to supply disruptions.     

Assessment of time-varying systemic risk in credit default swap indices: Simultaneity and contagiousness

This study assesses systemic risk inherent in credit default swap (CDS) indices using empirical and statistical analyses. We define systemic risk in two perspectives: the possibilities of simultaneous and contagious defaults, and then quantify them separately across benchmark models. To do so, we employ a Marshall-Olkin copula model to measure simultaneous default risk, and an interacting intensity-based model to capture contagious default risk. For an empirical test, we collect daily data for the iTraxx Europe CDS index and its tranche prices in the period from 2005 to 2014, and calibrate model parameters varying across time. In addition, we select forecasting models that have minimal prediction errors for the calibrated time series. Finally, we identify significant changes in each dynamic of systemic risk indicator before and after default and downgrade-related episodes that have occurred in the global financial crisis and European sovereign debt crisis.     

Non-Gaussian models for CoVaR estimation

In this paper we show how to obtain estimates of CoVaR based on models that take into consideration some stylized facts about multivariate financial time series of equity log returns: heavy tails, negative skew, asymmetric dependence, and volatility clustering. While the volatility clustering effect is captured by AR-GARCH dynamics of the Glosten-Jagannathan-Runkle (GJR) type, the other stylized facts are explained by non-Gaussian multivariate models and copula functions. We compare the different models in the period from January 2007 to March 2020. Our empirical study conducted on a sample of listed banks in the euro area confirms that, in measuring CoVaR, it is important to capture the time-varying dynamics of the volatility. Additionally, a correct assessment of the heaviness of the tails and of the dependence structure is needed in the evaluation of this systemic risk measure.