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The purpose of this paper is to study how the market correlation changes in Chinese stock market and how the market correlation affects stock returns.

The authors first examine the relationship between the market correlation and the market return. Then, the authors run formal multiple regressions to see whether correlation risk is priced in security returns.

The authors find that market correlation increases when the market index falls down. Though market correlation risk is partly influenced by macroeconomic shocks, volatility risk, liquidity risk and higher moment risk, market correlation contains unique information that measures the benefit investors gain from diversification strategies. The market correlation risk is negatively priced. This conclusion remains valid even if the authors have considered the influence of other risk factors and the impact of conditional information.

Subjected to the limited history of the Chinese stock market, the authors cannot use more accurate and specific empirical methodology to fulfill the empirical research. And this renders further study.

This research provides empirical evidence in a new data sample and it sheds lights on correlation strategies for institutional investors in China.

This analysis asks whether regulatory capital requirements capture differences in systematic risk for large firms and micro-, small- and medium-sized enterprises (MSMEs). The authors explore whether bank capital regulations intended to support SMEs’ access to borrowing are effective. The purpose of this paper is to find out whether the regulatory design (particularly the estimate of asset correlations) positively affects the lending process to small and medium enterprises, compared to large corporates.

The authors investigate the appropriateness of bank capital requirements considering default risk of loans to MSMEs and distortions in capital charges between MSMEs and large firms under the Basel III framework. The authors compiled firm-level data to capture the proportions of MSMEs and large firms in Italy during 2000–2014. The data set is drawn from financial reports of 708,041 firms over 15 years. Unlike most empirical studies that correlate assets and defaults, this study assesses a firm’s creditworthiness not by agency ratings or by sampling banks but by a specific model to estimate one-year probabilities of default.

The authors found that asset correlations increase with firms’ size and that large firms face considerably greater systematic risk than MSMEs. However, the empirical values are much lower than regulatory values. Moreover, when the authors focused on the MSME segment, systematic risk is rather stable and varies significantly with turnover. This analysis showed that the regulatory supporting factor represents a valuable attempt to treat MSME loans more fairly with respect to banks’ capital requirements. Basel III-internal ratings-based approach results show that when the supporting factor is applied, the Risk-Weighted-Assets (RWA) differences between MSMEs and large firms increase.

The implications of this research is that banking regulators to make MSMEs support more effective should review asset correlation estimation criteria, refining the fitting with empirical evidence.

The asset correlation parameter stipulated by the Basel framework is invariant with economic cycles, decreases with borrowers’ probability of default and increases with borrowers’ assets. The authors found that those relations do not hold. This way, asset correlations fall below parameters defined by regulatory formula, and SMEs’ credit risk could be overstated, resulting in a capital crunch.

The original contribution of this paper is to demonstrate that the gap between empirical and regulatory capital charge remains high. When the authors examined the Basel III-IRBA, results showed that when the supporting factor is applied, the RWA differences between MSMEs and large firms increase. This is particularly strong for loans to small- and medium-sized companies. Correctly calibrating asset correlations associated with the supporting factor eliminates regulatory distortions, reducing the gap in capital charges between loans to large corporate and MSMEs.

The purpose of this paper is to study the correlation structure of the credit spreads.

The minimal spanning tree is used to find the risk center node and the basic correlation structure of the credit spreads. The dynamic copula and pair copula models are applied to capture the dynamic and non-linear correlation structure.

The authors take the enterprise bond with trading data from January 2013 to December 2013 as the research sample. The empirical study of minimum spanning tree shows that the credit risk of corporate bonds forms a network structure with a center node. Meanwhile, the correlation between credit spreads shows dynamic characteristics. Under the framework of dynamic copula, the lower tail dependence is less than the upper tail dependence, thus, in economic boom period, the dynamic correlation is more significant than in recession period. The authors also find that the centrality of credit risk network is not significant according to the pair copula and Granger causality test. The empirical study shows that the goodness-of-fit of D vine is superior to Canonical vine, and the Granger causality test additionally proves that the center node has influence on few other nodes in the risk network, thus the center node captured by the minimum spanning tree is a weak center node, and this characteristic of credit risk network indicates that the risk network of credit spreads is generated mostly by the external shocks rather than the internal risk contagion.

This paper provides new ideas for investors and researchers to analyze the credit risk correlation or contagion.

Actual costs frequently deviate from the estimated costs in either favorable or adverse direction in construction projects. Conventional cost evaluation methods do not take the uncertainty and correlation effects into account. In this regard, a simulation-based cost risk analysis model, the Correlated Cost Risk Analysis Model, previously has been proposed to evaluate the uncertainty effect on construction costs in case of correlated costs and correlated risk-factors. The purpose of this paper is to introduce the detailed evaluation of the Cost Risk Analysis Model through scenario and sensitivity analyses.

The evaluation process consists of three scenarios with three sensitivity analyses in each and 28 simulations in total. During applications, the model’s important parameter called the mean proportion coefficient is modified and the user-dependent variables like the risk-factor influence degrees are changed to observe the response of the model to these modifications and to examine the indirect, two-sided and qualitative correlation capturing algorithm of the model. Monte Carlo Simulation is also applied on the same data to compare the results.

The findings have shown that the Correlated Cost Risk Analysis Model is capable of capturing the correlation between the costs and between the risk-factors, and operates in accordance with the theoretical expectancies.

Correlated Cost Risk Analysis Model can be preferred as a reliable and practical method by the professionals of the construction sector thanks to its detailed evaluation introduced in this paper.

Estimation of default and asset correlation is crucial for banks to manage and measure portfolio credit risk. The purpose of this paper is to find empirical relationship between the default and asset correlation with default probability, to understand the effect of systematic risk.

The authors have estimated single default and implicit asset correlations for banks and corporates in India and compare it with global scenario. This paper deduces a simple methodology to estimate the default correlations from the variance of temporal default rates. Next, the asset correlations have been estimated analytically by decomposition of variance equation in Merton's one factor risk model following approaches of Gordy and of Bluhm and Overbeck.

The authors empirically find a negative relationship between asset correlation and the probability of default using Moody's global corporate data that support Basel II internal ratings‐based (IRB) correlation prescription. However, they do not find any smooth relationship between the probability of default (PD) and asset correlation for Indian corporate. The magnitude of correlation estimates based on a large bank's internal rating‐wise default rates are much lower than what is prescribed by the Basel committee. Thus, the standardized correlation figures as assumed by the Basel Committee on Banking Supervision need to be properly calibrated by the local regulators before prescribing their banks to calculate IRB risk weighted assets.

These correlation estimates will help the regulators, insurance firms and banks to understand the linkage between counterparty default risks with the systematic factors. The findings of this paper could be used further in estimating portfolio economic capital for large corporate exposures of banks and insurance companies.

This paper investigates the impact of a change in economic policy uncertainty (ΔEPUt) and the absolute value of a change in geopolitical risk (|ΔGPRt|) on the returns of stocks, bonds and gold in the Chinese market.

The paper uses Engle's (2009) dynamic conditional correlation (DCC) model and Chiang's (1988) rolling correlation model to generate correlations of asset returns over time and analyzes their responses to (ΔEPUt) and  |ΔGPRt|.

Evidence shows that stock-bond return correlations are negatively correlated to ΔEPUt, whereas stock-gold return correlations are positively related to the |ΔGPRt|, but negatively correlated with ΔEPUt. This study finds evidence that stock returns are adversely related to the risk/uncertainty measured by downside risk,  ΔEPUt and  |ΔGPRt|, whereas the bond return is positively related to a rise in ΔEPUt; the gold return is positively correlated with a heightened |ΔGPRt|.

The findings are based entirely on the data for China's asset markets; further research may expand this analysis to other emerging markets, depending on the availability of GPR indices.

Evidence suggests that the performance of the Chinese market differs from advanced markets. This study shows that gold is a safe haven and can be viewed as an asset to hedge against policy uncertainty and geopolitical risk in Chinese financial markets.

This study identify the special role for the gold prices in response to the economic policy uncertainty and the geopolitical risk. Evidence shows that stock and bond return correlation is negatively related to the ΔEPU and support the flight-to-quality hypothesis. However, the stock-gold return correlation is positively related to |ΔGPR|, resulting from the income or wealth effect.

The presence of a dynamic correlations between stock-bond and stock-gold relations in response to  ΔEPUt and  |ΔGPRt| has not previously been tested in the literature. Moreover, this study finds evidence that bond-gold correlations are negatively correlated to both ΔEPUt and  |ΔGPRt|.

The implementation of credit risk models has largely relied either on the use of historical default dependence, as proxied by the correlation of equity returns, or on risk neutral equicorrelation, as extracted from CDOs. Contrary to both approaches, the purpose of this paper is to infer risk neutral dependence from CDS data, taking counterparty risk into consideration and avoiding equicorrelation. The impact of risk neutral correlation on the fees of some higher dimensional credit derivatives is also explored.

Copula functions are used in order to capture dependency. An application to market data is provided.

Both in the FtD and CDO cases, using (the correct) risk neutral measure instead of equity dependency has the same effect as the adoption of a copula with tail dependency instead of a Gaussian one. This should be important for those who resort to copulas in credit derivative pricing.

As far as is known, several attempts have been made in order to compare the behavior of different copulas in derivative pricing; however, no attempt has been made in order to extract risk neutral dependence without using the equicorrelation assumption. Therefore no attempt has been made to understand which copula features could proxy for risk neutrality, whenever risk neutral dependency cannot be inferred (for instance because CDS involving that name are not actively traded)

This chapter analyses the ability of some structural models to predict corporate bankruptcy. The study extends the existing empirical work on default risk in two ways. First, it estimates the expected default probabilities (EDPs) for a sample of bankrupt companies in the USA as a function of volatility, debt ratio, and other company variables. Second, it computes default correlations using a copula function and extracts common or latent factors that drive companies’ default correlations using a factor-analytical technique. Idiosyncratic risk is observed to change significantly prior to bankruptcy and its impact on EDPs is found to be more important than that of total volatility. Information-related tests corroborate the results of prediction-orientated tests reported by other studies in the literature; however, only a weak explanatory power is found in the widely used market-to-book assets and book-to-market equity ratio. The results indicate that common factors, which capture the overall state of the economy, explain default correlations quite well.

Given the rising need for measuring and controlling of financial risk as proposed in Basel II and Basel III Capital Adequacy Accords, trading risk assessment under illiquid market conditions plays an increasing role in banking and financial sectors, particularly in emerging financial markets. The purpose of this chapter is to investigate asset liquidity risk and to obtain a Liquidity-Adjusted Value at Risk (L-VaR) estimation for various equity portfolios. The assessment of L-VaR is performed by implementing three different asset liquidity models within a multivariate context along with GARCH-M method (to estimate expected returns and conditional volatility) and by applying meaningful financial and operational constraints. Using more than six years of daily return dataset of emerging Gulf Cooperation Council (GCC) stock markets, we find that under certain trading strategies, such as short selling of stocks, the sensitivity of L-VaR statistics are rather critical to the selected internal liquidity model in addition to the degree of correlation factors among trading assets. As such, the effects of extreme correlations (plus or minus unity) are crucial aspects to consider in selecting the most adequate internal liquidity model for economic capital allocation, especially under crisis condition and/or when correlations tend to switch sings. This chapter bridges the gap in risk management literatures by providing real-world asset allocation tactics that can be used for trading portfolios under adverse markets’ conditions. The approach to computing L-VaR has been arrived at through the application of three distinct liquidity models and the obtained results are used to draw conclusions about the relative liquidity of the diverse equity portfolios.