This study finds that reducing political influence over local courts weakens local government debt capacity. We establish this result by exploiting the staggered roll-out of a judicial centralization reform aimed at alleviating local court capture in China and find reduced judicial favoritism towards local governments post-reform. The majority of local government lawsuits are with contractors over government payment delays. The reform not only increases government lawsuit losses but also exposes their credit risk, as payment delays without court support signal government liquidity constraint. Investors respond by tightening lending and increasing interest rates, which curbs government spending.

This paper creates default-probability-(PD)-implied credit ratings for Chinese firms following the S&P global rating standard. The domestic credit rating agency (DCRA) ratings are higher than the PD-implied ratings by ten notches on average for the identical level of default risk, implying that the domestic ratings are significantly inflated. The PD-implied ratings outperform the DCRA ratings in detecting defaults and complement the latter in predicting yield spreads. The PD-implied ratings draw information from operating efficiency-related variables; in contrast, the DCRA ratings pay attention to scale-based firm characteristics in credit risk assessment.

Over the last decade, China’s P2P lending industry has been seen as an important credit source but it has recently suffered from a wave of bankruptcies. Using 126,090 P2P loan deals from RenRen Dai, one of the biggest online P2P websites in China, this paper attempts to predict credit default probabilities for P2P lending by implementing machine-learning techniques. More specifically, thisstudy proposes a stacking ensemble machine-learning model to assess credit default risk for P2P lending platforms. A Max-Relevance and Min-Redundancy (MRMR) method is used for feature selection and then irrelevant features are eliminated by using k-means clustering method. Finally, the stacking ensemble model is performed to produce accurate and stable predictions in the feature subset. Experimental results show that stacking ensemble model yields high performance, not only in prediction accuracy but also in precision and recall. In comparison to single classifiers, the stacking ensemble machine-learning model has a minimum error rate and provides more accurate credit default risk prediction. The results also confirm the efficiency of the proposed stacking ensemble model through the area under the ROC curve.

We study non-performing loan (NPL) transactions in China using proprietary data from a leading market participant. We find these transactions – driven by tighter financial regulation – are consistent with banks concealing non-performing assets from regulators as (i) transaction prices do not compensate for credit risks; (ii) banks fund the NPL transactions and remain responsible for debt collection; and (iii) 70% of NPL packages are re-sold at inflated prices to bank clients. These results imply NPL transactions do not truly resolve NPLs. Recognizing the hidden NPLs implies the total NPLs in China is two to four times the reported amount.

In this paper, using a set of zero yield curve data of China’s government bonds and credit bonds, along with China’s aggregate credit risk measures, and macroeconomic variables from 2006 to 2013, we document a puzzle of counter-credit-risk corporate yield spreads. We interpret this puzzle as a symptom of the immaturity of China’s credit bond market, which reveals a distorted pricing mechanism latent in the fundamental of this market. As by-products of our analysis, we also find interesting results about relations between corporate yield spreads and interest rates as well as risk premia and the stock index, and these results are somewhat attributed to this puzzle.

Different from the well established markets such as the dollar-Euro market, recent CIP deviations observed in the onshore dollar-RMB forward market were primarily caused by conversion restrictions in the spot market rather than changes in credit risk and/or liquidity constraint. This paper proposes a theoretical framework under which the Chinese authorities impose conversion restrictions in the spot market in an attempt to achieve capital flow balance, but face the tradeoff between achieving such balance and disturbing current account transactions. Consequently, the level of conversion restriction should increase with the amount of capital account transactions and decrease with the amount of current account transactions. Such conversion restriction in turn places a binding constraint on forward traders’ ability to cover their forward positions, resulting in the observed CIP deviation. More particularly, the model predicts that onshore forward rate is equal to a weighted average of CIP-implied forward rate and the market’s expectation of future spot rate, with the weight determined by the level of conversion restriction. As a secondary result, the model also implies that offshore non-deliverable forwards reflect the market’s expectation of future spot rate. Empirical results are consistent with these predictions.

This paper focuses on evidence from stock markets as it investigates the spillovers from the United States to mainland China and Hong Kong SAR during the subprime crisis. Using both univariate and multivariate GARCH models, this paper finds that China's stock market is not immune to the financial crisis, as evidenced by the price and volatility spillovers from the United States. In addition, HK's equity returns have exhibited more significant price and volatility spillovers from the United States than China's returns, and past volatility shocks in the United States have a more persistent effect on future volatility in HK than in China, reflecting HK's role as an international financial center. Moreover, the impact of the volatility from the United States on China's stock markets has been more persistent than that from HK, due mainly to the United States as the origin of the subprime crisis. Finally, as expected, the conditional correlation between China and HK has outweighed their conditional correlations with the United States, echoing increasing financial integration between China and HK.

Great credit risk is a big headache which blocks the development of the banking sector of China. Based on the panel data of the Chinese banking sector from 2002 to 2006, this paper empirically examines the effects of foreign strategic investors’ participation on the risk behavior of Chinese banks. The results show that foreign strategic investors (FSI) had a positive, but limited impact on the credit risk of Chinese banks. Further analysis reveals that the risk management abilities of Chinese banks have improved apparently when the proportion of shareholding of the leading foreign strategic investors exceeds 15 percent, which results in a significant drop of the credit risk. However, due to the ‘minority ownership’ restriction on foreign investors' stock shares, the positive effect of the participation of foreign strategic investors is limited. The visible decline in both non-performing loans (NPLs) and the NPL ratio of Chinese banks mainly reflects the rapid growth of China's economy and benefits a lot from the massive financial restructuring of state-owned banks.

Stationary-leverage-ratio models of modelling credit risk based on constant target leverage ratios cannot generate probabilities of default which replicate empirically observed default rates. This paper presents a structural model to address this problem. The main feature of the model is that a firm’s leverage ratio is mean-reverting to a time-dependent target leverage ratio. The time-dependent target leverage ratio reflects the firm’s intention of moving its initial target ratio toward a long-term target ratio over time. We derive a closed-form solution of the probability of default based on the model as a function of the firm value, liability and short term interest rate. The numerical results calculated from the solution with simple time-dependent functions of the target leverage ratios show that the model is capable of producing term structures of probabilities of default that are consistent with some empirical findings. This model could provide new insight for future research on corporate bond analysis and credit risk measurement.

Although bank loans themselves are somewhat illiquid because of private information, most of their cashflows are not. Recent financial innovations allow commercial loans to be liquefied via credit derivatives and actual and synthetic securitizations. The loan originating bank holds the remaining illiquid tranche containing the concentrated credit risk, private information rent and the “excess spread” that incentivize the bank to continue to monitor and service the loans. Empirically, we find that the average size of the residual tranche is about 3%, which reflects the size of the “market determined capital” necessary to support the liquefaction. The liquefaction of bank loans makes possible a banking system that restricts the guaranteed accounts to be backed by 100% reserves and the non-guaranteed deposits to be backed by liquid securitized loan tranches, while retaining the deposit-lending synergy. Such a system is perfectly safe without deposit insurance and it renders banks bankruptcy-remote without sacrificing a bank’s traditional role as a financial intermediary.