Research on China's financial systemic risk contagion under jump and heavy-tailed risk

To accurately measure the dynamic characteristics of systemic risk contagion under the impact of extreme financial events, we construct a research framework that analyzes the contagion dynamics of systemic risk under extreme risk impact from the perspectives of both time and space. Based on the macro-jump CCA method, this paper extracts the heterogeneous volatility sequence of financial industries considering the thick tail of the distribution of financial assets returns. Then, the dynamic variation of systemic risk in the financial sectors is characterized from the time dimension. The volatility spillover network method is used to examine the spillover contagion of systemic risk among financial system sectors from the spatial dimension. Empirical studies have found that when considering the risk contagion level, the capital market service sector plays a risk‑leading role, followed by the currency service sector and the insurance sector. The measurement indicators that consider the jump risk and the tail risk have good early warning effects on extreme financial events. Seen from the spatial direction of risk spillover, the real estate sector exhibits the most obvious risk spillover effect on other sectors and can be regarded as the source of systemic risk, which suggests differentiated regulation.     

Contagion risk in the Australian banking and property sectors

The Australian banking system emerged from the global crisis virtually unhurt, with most banks still profitable, adequately capitalized, and with AA credit ratings. Are there any risks or vulnerabilities in this success story? This paper analyzes Australia’s systemic banking risk and attempts to determine if this risk increased with the recent global crisis and whether this risk is related to the downturn experienced in the real estate market. We use extreme value theory to measure banks’ and property firms’ univariate Value at Risk, as well as multivariate intra-sector and inter-sector contagion risks. Of the 13 sectors analyzed, we find that the property sector exhibits the highest level of extremal dependence with the banking sector. The credit crisis significantly increased the probability of a bank or property firm crashing. Moreover, contagion risks significantly increased not only within the banking and property sectors, but also between those sectors.     

Reprint of: Market liquidity in the financial crisis: The role of liquidity commonality and flight-to-quality

We examine the dynamics and the drivers of market liquidity during the financial crisis, using a unique volume-weighted spread measure. According to the literature we find that market liquidity is impaired when stock markets decline, implying a positive relation between market and liquidity risk. Moreover, this relationship is the stronger the deeper one digs into the order book. Even more interestingly, this paper sheds further light on so far puzzling features of market liquidity: liquidity commonality and flight-to-quality. We show that liquidity commonality varies over time, increases during market downturns, peaks at major crisis events and becomes weaker the deeper we look into the limit order book. Consistent with recent theoretical models that argue for a spiral effect between the financial sector’s funding liquidity and an asset’s market liquidity, we find that funding liquidity tightness induces an increase in liquidity commonality which then leads to market-wide liquidity dry-ups. Therefore our findings corroborate the view that market liquidity can be a driving force for financial contagion. Finally, we show that there is a positive relationship between credit risk and liquidity risk, i.e., there is a spread between liquidity costs of high and low credit quality stocks, and that in times of increased market uncertainty the impact of credit risk on liquidity risk intensifies. This corroborates the existence of a flight-to-quality or flight-to-liquidity phenomenon also on the stock markets.     

The credit quality channel: Modeling contagion in the interbank market

We propose an algorithm to model contagion in the interbank market via what we term the “credit quality channel”. In existing models on contagion via interbank credit, external shocks to banks often spread to other banks only in case of a default. In contrast, shocks are transmitted also via asset devaluations and deteriorations in the credit quality in our algorithm. First, the probability of default (PD) of those banks directly affected by some shock increases. This increases the expected loss of the credit portfolios of the initially affected banks’ counterparties, thereby reducing the counterparties’ regulatory capital ratio. From a logistic regression we estimate the increase in the counterparties’ PD due to a reduced capital ratio. Their increased PDs in turn affect the counterparties’ counterparties, and so on. This coherent and flexible framework is applied to the bilateral interbank credit exposure of the entire German banking system in order to examine policy questions. For that purpose, we propose to measure the potential cost of contagion of a given shock scenario by the aggregated regulatory capital loss computed in our algorithm.     

Modelling the distribution of credit losses with observable and latent factors

This paper proposes a dynamic model to estimate the credit loss distribution of the aggregate portfolio of loans granted in a banking system. We consider a sectoral approach distinguishing between corporates and households. The evolution of their default frequencies and the size of the loans portfolio are expressed as functions of macroeconomic conditions as well as unobservable credit risk factors, which capture contagion effects between sectors. In addition, we model the distributions of the Exposures at Default and the Losses Given Default. We apply our framework to the Spanish banking system, where we find that sectoral default frequencies are not only affected by economic cycles but also by a persistent latent factor. Finally, we identify the riskier sectors, perform stress tests and compare the relative risk of small and large institutions.     

Firm heterogeneity and credit risk diversification

This paper examines the impact of neglected heterogeneity on credit risk. We show that neglecting heterogeneity in firm returns and/or default thresholds leads to underestimation of expected losses (EL), and its effect on portfolio risk is ambiguous. Once EL is controlled for, the impact of neglecting parameter heterogeneity is complex and depends on the source and degree of heterogeneity. We show that ignoring differences in default thresholds results in overestimation of risk, while ignoring differences in return correlations yields ambiguous results. Our empirical application, designed to be typical and representative, combines both and shows that neglected heterogeneity results in overestimation of risk. Using a portfolio of U.S. firms we illustrate that heterogeneity in the default threshold or probability of default, measured for instance by a credit rating, is of first order importance in affecting the shape of the loss distribution: including ratings heterogeneity alone results in a 20% drop in loss volatility and a 40% drop in 99.9% VaR, the level to which the risk weights of the New Basel Accord are calibrated.     

CDO pricing with nested Archimedean copulas

Companies in the same industry sector are usually more correlated than firms in different sectors, as they are similarly affected by macroeconomic effects, political decisions, and consumer trends. Despite the many stock return models taking this fact into account, there are only a few credit default models that take it into consideration. In this paper we present a default model based on nested Archimedean copulas that is able to capture hierarchical dependence structures among the obligors in a credit portfolio. Nested Archimedean copulas have a surprisingly simple and intuitive interpretation. The dependence among all companies in the same sector is described by an inner copula and the sectors are then coupled via an outer copula. Consequently, our model implies a larger default correlation for companies in the same industry sector than for companies in different sectors. A calibration to CDO tranche spreads of the European iTraxx portfolio is performed to demonstrate the fitting capability of the model. This portfolio consists of CDS on 125 companies from six different industry sectors and is therefore an excellent portfolio for a comparison of our generalized model with a traditional copula model of the same family that does not take different sectors into account.     

Constant proportion portfolio insurance strategies in contagious markets

Constant Proportion Portfolio Insurance (CPPI) strategies are popular as they allow to gear up the upside potential of a stock index while limiting its downside risk. From the issuer’s perspective it is important to adequately assess the risks associated with the CPPI, both for correct ‘gap’ fee charging and for risk management. The literature on CPPI modelling typically assumes diffusive or Lévy-driven dynamics for the risky asset underlying the strategy. In either case the self-contagious nature of asset prices is not taken into account. In order to account for contagion while preserving analytical tractability, we introduce self-exciting jumps in the underlying dynamics via Hawkes processes. Within this framework we derive the loss probability when trading is performed continuously. Moreover, we estimate measures of the risk involved in the practical implementation of discrete-time rebalancing rules governing the CPPI product. When rebalancing is performed on a frequency less than weekly, failing to take contagion into account will significantly underestimate the risks of the CPPI. Finally, in order to mimic a situation with low liquidity, we impose a daily trading cap on the risky asset and find that the Hawkes process driven models give rise to the highest risk measures even under daily rebalancing.     

Hedge Fund Contagion and Liquidity Shocks

Defining contagion as correlation over and above that expected from economic fundamentals, we find strong evidence of worst return contagion across hedge fund styles for 1990 to 2008. Large adverse shocks to asset and hedge fund liquidity strongly increase the probability of contagion. Specifically, large adverse shocks to credit spreads, the TED spread, prime broker and bank stock prices, stock market liquidity, and hedge fund flows are associated with a significant increase in the probability of hedge fund contagion. While shocks to liquidity are important determinants of performance, these shocks are not captured by commonly used models of hedge fund returns.     

Equity Market Comovement and Contagion: A Sectoral Perspective

This paper takes an asset pricing perspective to investigate the equity market comovement and contagion at the sector level during the period 1990-2004 across the regions of Europe, Asia, and Latin America. It examines whether unexpected shocks from a particular market, or group of markets, are propagated to the sectors in other countries. The results confirm the sector heterogeneity of contagion. This implies that there are sectors that can still provide a channel for achieving the benefits of international diversification during crises despite the prevailing contagion at the market level. In addition, the results lend support to the importance of financial links in the propagation of contagion.     

Aggregating sectors in the infectious defaults model

Abstract

How to model the dependence between defaults in a portfolio subject to credit risk is a question of great importance. The infectious default model of Davis and Lo offers a way to model the dependence. Every company defaulting in this model may ‘infect’ another company causing it to default. An unsolved question, however, is how to aggregate independent sectors, since a naive straightforward computation quickly gets cumbersome, even when homogeneous assumptions are made. Here, two algorithms are derived that overcome the computational problem and further make it possible to use different exposures and probabilities of default for each sector. For an ‘outbreak’ of defaults to occur in a sector, at least one company has to default by itself. This fact is used in the derivations of the two algorithms. The first algorithm is derived from the probability generating function of the total credit loss in each sector and the fact that the outbreaks are independent Bernoulli random variables. The second algorithm is an approximation and is based on a Poisson number of outbreaks in each sector. This algorithm is less cumbersome and more numerically stable, but still seems to work well in a realistic setting.     

Foreign Banks and the Dual Effect of Financial Liberalization

In emerging countries, credit market liberalization is often motivated with the financial deepening generated by the entry of foreign financial institutions. However, there is a risk that liberalization may benefit internationally active, export‐oriented businesses at the expense of domestically oriented ones. This paper models a two‐sector economy in which foreign lenders are more efficient than local lenders at extracting value from internationally tradable collateral assets. Under some conditions the entry of foreign lenders eases entrepreneurs’ access to the credit market and raises asset prices and output, but in other circumstances it reduces the depth of the credit market and depresses the price of nontradables and output. Liberalization can have a contractionary impact by inducing a reallocation of credit from the nontradables to the tradables sector.     

An Investigation of the Impact of Interest Rates and Interest Rate Volatility on Australian Financial Sector Stock Return Distributions

Abstract:   This paper extends the existing literature by analysing the dual impact of changes in the interest rate and interest rate volatility on the distribution of Australian financial sector stock returns. In addition, a multivariate GARCH‐M model is used to analyse the impact of deregulation on the financial institutions sector. It was found that there is a consistent inter‐temporal trade off between risk and return over the different regulatory periods. Moreover, finance corporations were found to be highly sensitive to new shocks across the financial sector and deregulation increased the risk faced by finance corporations and small banks – effectively increasing the required rate of return and explaining the continued rationalisation of these sectors. Furthermore, deregulation has changed the fundamental relationship between interest rates and large bank stock excess returns from positive in the pre‐deregulation period to negative in the post‐deregulation period. This reflects the changing institutional environment from one of controlled credit rationing to a more competitive environment.     

How strong are the linkages between real estate and other sectors in China?

International experience points to the critical role of stable property markets in maintaining financial stability. This paper investigates the real and financial linkages between real estate sector and other sectors. The real linkage based on input–output analysis shows that the linkages have strengthened. The financial linkages in terms of credit risk spillovers across sectors are studied by using DAG method and SVAR. We find that that credit risk in the real estate sector has large-scale spillover effects onto other sectors. Consequently, shocks to the property market could have much larger impact on the Chinese economy than suggested by headline figures.     

A Tractable Model to Measure Sector Concentration Risk in Credit Portfolios

We explore a simplified version of the value-at-risk approximation developed by Pykhtin (Risk Magazine, March, 85–90, 2004), which only requires risk parameters on a sector level. We measure the impact of credit concentrations in business sectors on the economic capital of credit portfolios. We base our portfolios’ sector composition on credit information from the German central credit register. Our results show that the approximation formula performs well for fine-grained portfolios that are homogeneous on a sector level in terms of probability of default (PD) and exposure size. We explore the robustness of our results for portfolios which are heterogeneous in terms of these two characteristics. We find that low granularity ceteris paribus causes the approximation formula to underestimate economic capital, whereas heterogeneity in individual PDs causes overestimation. Indicative results imply that in typical credit portfolios of banks, PD heterogeneity will at least compensate for the granularity effect. This result suggests that the approximation estimates economic capital reasonably well and/or errs on the conservative side.     

基于房地产市场的我国系统性金融风险测度与预警研究

本文阐释了基于房地产市场的系统性金融风险形成机制,据此建立了分阶段、跨部门的房地产市场的系统性金融风险网络模型,并运用2006-2017年16家上市银行数据,分析和测度了我国房价大幅下跌所引发的系统性金融风险水平和结构,构建了基于房地产市场的系统性金融风险预警指标并进行测算。研究发现:在房价下跌30%的压力情景下,我国金融体系的潜在总损失总体呈级数式上升,年均增长22.70%;基于房地产市场的系统性金融风险值(SR)呈现先上升后波动下降的总体趋势;系统性金融风险(SR)的脆弱性指标(FLI)整体呈现波浪式振荡变化,且与房地产贷款/权益整体呈反向变动,系统性金融风险(SR)的传染性指标(CTI)在2012-2017年呈持续下降趋势,且与金融市场压力指数、金融机构间资产占总资产比重呈现出高度的一致性变化趋势。最后,基于房地产市场的系统性金融风险预警指标(SRWI)值呈收敛式振荡走势,表明基于房地产市场的系统性金融风险总体可控且呈收敛式下降。     

Carry

We apply the concept of carry, which has been studied almost exclusively in currency markets, to any asset. A security’s expected return is decomposed into its “carry,” an ex-ante and model-free characteristic, and its expected price appreciation. Carry predicts returns cross-sectionally and in time series for a host of different asset classes, including global equities, global bonds, commodities, US Treasuries, credit, and options. Carry is not explained by known predictors of returns from these asset classes, and it captures many of these predictors, providing a unifying framework for return predictability. We reject a generalized version of Uncovered Interest Parity and the Expectations Hypothesis in favor of models with varying risk premia, in which carry strategies are commonly exposed to global recession, liquidity, and volatility risks, though none fully explains carry’s premium.     

失信风险传染会影响债券定价吗？——基于担保网络大数据的实证研究

在债券定价研究中不仅应该考虑企业自身的信用风险,还应该考虑相关网络组织的传染风险。本文基于43万笔非金融企业间的担保数据,构建了企业信用担保网络,发现失信风险作为一种广义的信用风险,在担保网络中具有传染效应,该传染效应能够影响债券的信用利差。企业的失信行为产生了三类传染效应,一是直接传染效应,无论是发债主体的担保人出现失信行为,还是被担保人出现失信行为,都会引起发债主体的信用利差上升;二是局部感染效应,如果局部担保网络中失信主体的占比提升,可能引起投资者对发债主体的“团体处罚”,导致信用利差上升;三是全局扩散效应,失信信息沿担保网络向整个市场扩散,导致债券信用利差上升。从企业所有制来看,民营企业主要受微观的直接传染效应和中观的局部感染效应影响;而国有企业主要受全局扩散效应影响;被担保人的失信风险对两类企业都有显著影响。失信风险传染效应会降低企业的再融资能力,其中局部感染效应导致企业次年的借款融资额下降,全局扩散效应导致企业次年的债券融资额下降。     

Contagion models a la carte: which one to choose?

We propose a copula contagion mixture model for correlated default times. The model includes the well-known factor, copula, and contagion models as its special cases. The key advantage of such a model is that we can study the interaction of different models and their pricing impact. Specifically, we model the default times of the underlying names in a reference portfolio to follow contagion intensity processes with exponential decay coupled with a copula dependence structure. We also model the default time of the counterparty and its dependence structure with the reference portfolio. Numerical tests show that correlation and contagion have an enormous joint impact on the rates of CDO tranches and the corresponding credit value adjustments are extremely high to compensate for the wrong-way risk.     

A simple model of credit contagion

We propose a simple model of credit contagion in which we include macro- and microstructural interdependencies among the debtors within a credit portfolio. The microstructure captures interdependencies between debtors that go beyond their exposure to common factors, e.g., business or legal interdependencies. We show that even for diversified portfolios, moderate microstructural interdependencies have a significant impact on the tails of the loss distribution. This impact increases dramatically for less diversified microstructures.