Interconnectedness and systemic risk in the US CDS market

This study assesses systemic risk in the US credit default swap (CDS) market. First, this study estimates the bilateral exposures matrix using aggregate fair value data and theoretically analyze interconnectedness in the US CDS network using various network measures. Second, this study theoretically analyzes the contagious defaults. The default analysis shows the theoretical occurrence of many stand-alone defaults and one contagious default via the CDS network during the global financial crisis. A stress test based on a hypothetical severe stress scenario predicts almost no future contagious defaults. Thus, risk contagion via the CDS network is unlikely.     

Understanding the consequences of diversification on financial stability

In this paper, we study the consequences of diversification on financial stability and social welfare using an agent based model that couples the real economy and a financial system. We validate the model against its ability to reproduce several stylized facts reported in real economies. We find that the risk of an isolated bank failure (i.e. idiosyncratic risk) is decreasing with diversification. In contrast, the probability of joint failures (i.e. systemic risk) is increasing with diversification which results in more downturns in the real sector. Additionally, we find that the system displays a “robust yet fragile” behaviour particularly for low diversification. Moreover, we study the impact of introducing preferential attachment into the lending relationships between banks and firms. Finally, we show that a regulatory policy that promotes bank–firm credit transactions that reduce similarity between banks can improve financial stability whilst permitting diversification.

     

Central bank disclosure as a macroprudential tool for financial stability

The Basel Capital Accord (pillar 3) states that disclosure of information (transparency) is essential to financial stability. This study analyzes, through inflation reports, the disclosure of information from the Central Bank of Brazil concerning the credit market. We consider credit risk and capital buffers as measures of financial stability in this analysis. Furthermore, in order to measure the perception of the monetary authority on the credit market, we built two indices based on the central bank’s communication on credit development. We performed a panel data analysis based on a sample of 125 banks for the period from June 1999 to September 2014 (7000 observations). The findings suggest that central bank communication regarding expectations concerning the credit market contributes to financial stability. Therefore, this kind of communication of central banks (about credit development) may constitute an important macroprudential tool to improve financial stability.     

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.     

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.     

Systemic liquidity contagion in the European interbank market

Systemic liquidity risk, defined by the International Monetary Fund as “the risk of simultaneous liquidity difficulties at multiple financial institutions,” is a key topic in financial stability studies and macroprudential policy-making. In this context, the complex web of interconnections of the interbank market plays the crucial role of allowing funding liquidity shortages to propagate between financial institutions. Here, we introduce a simple yet effective model of the interbank market in which liquidity shortages propagate through an epidemic-like contagion mechanism on the network of interbank loans. The model is defined by using aggregate balance sheet information of European banks, and it exploits country and bank-specific risk features to account for the heterogeneity of financial institutions. Moreover, in order to obtain the European-wide topology of the interbank network, we define a block reconstruction method based on the exchange flows between the various countries. We show that the proposed contagion model is able to estimate systemic liquidity risk across different years and countries. Results suggest that our effective contagion approach can be successfully used as a viable alternative to more realistic but complicated models, which not only require more specific balance sheet variables with high time resolution but also need assumptions on how banks respond to liquidity shocks.

     

Dynamic credit contagion and aggregate loss in networks

We develop a dynamic model to illustrate the credit risk contagion mechanism caused by interaction between firms. Specifically, we formulate the sources of risk into idiosyncratic risk and contagion risk, and introduce recovery ability to model the scenario of a firm changing from default into normal status. Our result shows that there always exists a steady state in a network under some trivial conditions. For quasi-regular networks and bipartite networks, the expected aggregate loss remains unchanged as long as the product of the contagion probability and the partner number is fixed.     

Forecasting systemic impact in financial networks

We propose a methodology for forecasting the systemic impact of financial institutions in interconnected systems. Utilizing a five-year sample including the 2008/9 financial crisis, we demonstrate how the approach can be used for the timely systemic risk monitoring of large European banks and insurance companies. We predict firms’ systemic relevance as the marginal impact of individual downside risks on systemic distress. So-called systemic risk betas account for a company’s position within the network of financial interdependencies, in addition to its balance sheet characteristics and its exposure to general market conditions. Relying only on publicly available daily market data, we determine time-varying systemic risk networks, and forecast the systemic relevance on a quarterly basis. Our empirical findings reveal time-varying risk channels and firms’ specific roles as risk transmitters and/or risk recipients.     

A functional perspective on financial networks

The financial sector is a critical component of any economic system, as it delivers key qualitative asset transformation services in terms of liquidity, maturity and volume. Although these functions could in principle be carried out separately by specialized actors, in the end it is their systemic co-evolution that determines how the aggregate economy performs and withstands disruptions. In this paper we argue that a functional perspective on financial intermediation can be usefully employed to investigate the functioning of financial networks. We do this in two steps. First, we use previously unreleased data to show that focusing on the economic functions performed over time by the different institutions exchanging funds in an interbank market can be informative, even if the underlying topological structure of their relations remains constant. Second, a set of alternative artificial histories are generated and stress-tested by using real data as a calibration base, with the aim of performing counterfactual welfare comparisons among different topological structures.     

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.     

Credit behavior and financial stability in an emerging economy

One relevant issue for the management of financial stability is the monitoring of the credit market. In this sense, Basel III proposed the credit gap as the most appropriate measure to anticipate financial stability issues. However, the adoption of the credit gap has been criticized, especially for emerging markets. Through panel data analysis, this study investigates the effect of the credit gap and the credit growth rate on financial stability in Brazil, which represents a relevant emerging economy. For this purpose, we use a set of financial stability measures traditionally found in the literature: the z-score, regulatory capital and credit risk. The results suggest that the credit gap and credit growth rates are adequate metrics to indicate the sustainability of credit growth in Brazil. However, credit growth rates are more attractive, since they indicate a threshold for credit growth in the Brazilian economy concerning financial stability.     

Does transparency of central banks communication affect credit market? Empirical evidence for advanced and emerging markets

This study examines the effect of the transparency of central banks communication on credit market. In particular, this study investigates how central banks’ effort to provide more detailed information about their objectives regarding the price stability (monetary policy transparency) and financial stability (financial stability transparency) policies are able to mitigate information asymmetry on credit market, through the net interest margin charged by the banks to engage in financial intermediation (credit spread). The findings denote central bank transparency is able to reduce the credit spread. Additionally, the evidence suggests the effect of central bank transparency on the credit spread is greater in emerging markets, where there is less information available on credit market. In brief, transparency in central banks communication is an important tool to mitigate the information asymmetry in the credit market.     

Bagging方法在基于日常交易数据解决中小企业的信用评价问题中的应用

关于信用风险评价问题至今已经做了很多研究,各种信用评价模型与方法也已被开发。但是这些模型与方法几乎都是基于财务数据、股票价格或风险调研机构发表的各种调查结果。因为几乎所有的中小企业的财务数据都是非公开的,至今开发的信用评价模型与方法都不免成为无米之炊。为此,本文提出了一个新的途径,只需要根据销售额、顾客付款额、拖欠款额等日常业务处理数据来评价顾客企业的信用度。本文提出一个应用Sagging方法评价顾客信用的系统,其目的在于解决由于异常顾客数比正常顾客要少很多而带来的问题,提高分辨异常顾客的能力。本文所提出的信用评价系统将应用到一个实际企业的信用评价问题中,借此来验证系统的性能和效果。     

Multiscale features of extreme risk spillover networks among global stock markets

This paper studies the multiscale features of extreme risk spillover among global stock markets over various time–frequency horizons. We propose multiscale risk spillover indexes based on GARCH-EVT-VaR, maximal overlap discrete wavelet transform method, and forecast-error-variance decompositions. We further construct multiscale risk spillover networks to visualize risk spillovers at different scales. Our findings show that the US and the UK are detected as the centers of risk spillovers, while Asian stock markets are mainly at the edge of the risk spillover network. The topological properties are unevenly spread over each time scale. The network tends to be closer not only at the short-term scale but also during the financial crisis. For individual features, the US and the UK are super-spreaders of risk spillover at each time scale, while most developing markets mainly act as absorbers. The role of European stock markets is complex at different scales.     

Banks’ risk taking,financial innovation and macroeconomic risk

This paper shows how financial innovation in combination with the fall of macroeconomic risk can explain the strong growth of the primary and secondary credit markets in the U.S. economy. We document empirically the fall in macroeconomic risk, the expansion of the prime and secondary credit market and we provide evidence that changes in macroeconomic risk are closely related to the evolution of the prime market. In the theoretical part of the paper we study in a simple portfolio optimization framework the effect of financial innovation and macroeconomic risk on banks’ risk taking. The results of the model show that financial innovation increases bank appetite for risky investment both in the prime and secondary markets and that this effect is stronger in environments with low aggregate macroeconomic risk. In addition the banking system becomes less stable because of the portfolio risk of each individual bank increases.     

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.     

Spillover effects in oil-related CDS markets during and after the sub-prime crisis

This paper investigates the return and volatility spillover effects across oil-related credit default swaps (CDSs), the oil market, and financial market risks for the US during and after the subprime crises. The empirical analysis is based on monthly return and realized volatility data from February 2004 to April 2020. We estimate both static and dynamic generalized dynamic spillover measures based on vector autoregressive (VAR) models. Our full sample empirical findings show that the oil market is the primary source of risk transmission for all the oil-related credit default swaps, while the bond market is the highest source of risk transmission to the stock market and vice versa. We also provide evidence that the regulated monopoly US utility sector has the least role in volatility transmission. Furthermore, the bailout program conducted by the US Treasury and Federal Reserve helped stabilize the US financial market through the purchase of toxic assets after the subprime financial crisis. We find strong evidence that the federal funds rate hike cycles lessen total risk transmission throughout the US bond market. Finally, our findings assert that oil price shocks have a significant effect on the oil-related CDSs in some sub-periods via the demand and supply transmission channels.     

基于CCA模型的信用风险定价分析与实证研究

信用风险一直是银行和其他金融机构关心的主要话题。对待信用风险的传统方法是由信用风险部门根据过去的数据进行统计估计。然而,在最近几年,随着金融市场的迅速发展以及金融工具的日益复杂化,这种方法已经显得无法适应了。从最广义的风险说起,把讨论范围逐渐缩小,最后缩小至信用风险的定价问题。通过Merton模型对信用风险定价过程作一般性推导,同时给出一个例子以便掌握运用这种方法。     

The role of banking regulation in an economy under credit risk and liquidity shock

This paper develops a Dynamic Stochastic General Equilibrium model which includes a financial sector to analyze the effects of liquidity shock and credit risk in the Brazilian economy. Banks use equity capital and deposits from agents to finance investments of the productive sector. The sources of financial frictions are default rate and liquidity shock, due to deposits withdrawn in advance. The banking supervisor injects liquidity in the deposit market. Using data for the Brazilian economy in the period from 1995 to 2009, the structural parameters are estimated by Bayesian methods. Impulse response functions are computed to describe the dynamic effects of exogenous shocks. The major results show that credit risk is pro-cyclical and default risk depends on structural features. The banking regulator is able to set up a policy to promote financial stability and efficiently reduce fluctuations in the output.     

Analysis of the impact of Sino-US trade friction on China’s stock market based on complex networks

This paper analyzes the impact of the Sino-US trade friction incident in 2018 on China's stock market by using the complex network methods. Firstly, we divide the Sino-US trade friction incident in 2018 into four research periods. Based on the GARCH-BEKK model and the Planar Maximum Filter Graph (PMFG) algorithm, the volatility spillover network between China's stock market sectors and the stock price correlation network of China's stock market corresponding to the above four research periods are constructed. Next, from the perspective of sectors in stock market, we use various network centrality indicators to build a systematic importance comprehensive evaluation index of industry sectors in the stock market through the principal component analysis method, to explore the impact of the Sino-US trade friction incident on the risk spillover effects of sectors in China's stock market. From the perspective of the overall stock market, we analyze the impact of Sino-US trade friction incident on the overall stability of the stock market through calculating the network topology indicators and conducting simulation experiments. Finally, the main factors affecting the stability mechanism of China's stock market are studied through the probit model. The results show that: (1) The risk spillover effect of various sectors in China's stock market changes significantly in different periods of Sino-US trade friction, and there are obvious cyclical rotation effects among various sectors (2) When some weighted stocks in the stock market abnormally fluctuate or suffer targeted shocks, the China's stock market's ability to maintain stability is weak, and the Sino-US trade friction will reduce the stability of China's stock market, and the higher the intensity of trade friction incident is, the more obvious the impact of the incident is. (3) The important factors that affect the abnormal fluctuations in China's stock market include four types of indicators: the stock market network structure, the fluctuation of important international stock indexes, the fluctuation of commodity prices in the international market, and the domestic macroeconomic indicators. This study provides a reference for China's financial regulatory authorities to conduct macro-prudential management, control systemic risks, and maintain the stability of financial market.