[Show abstract][Hide abstract] ABSTRACT: External oil supply risk is an important aspect of energy security and oil import diversification is viewed as one of the most effective measures to reduce supply risk. By using the diversification index approach, this research attempts to assess external oil supply risks of oil importers with consideration of two key factors associated with oil suppliers, country risk and potential oil exports capacity. The former can be used to reflect the oil supply risk associated with oil suppliers' macro-economic and socio-political stability and the latter is directly related with the physical availability of oil. The empirical research on China, Japan, the US and EU demonstrates that Japan's oil importing strategy is more concerned about potential exports of oil suppliers than country risk; the EU's oil imports have a good balance between country risk and potential exports of oil suppliers; the US has successfully diversified its oil imports to maintain the security of its external oil supply. With the rapid increase in oil demand, China has changed its oil import policy by expanding oil trade partners and switching to suppliers with high potential oil exports. Some further suggestions are presented for China to ensure its oil supply security in the future. (c) 2014 Elsevier Ltd. All rights reserved.
Energy 04/2014; 68:930-938. DOI:10.1016/j.energy.2014.02.091 · 4.84 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: It is generally accepted that the choice of severity distribution in loss distribution approach has a significant effect on the operational risk capital estimation. However, the usually used parametric approaches with predefined distribution assumption might be not able to fit the severity distribution accurately. The objective of this paper is to propose a nonparametric operational risk modeling approach based on Cornish-Fisher expansion. In this approach, the samples of severity are generated by Cornish-Fisher expansion and then used in the Monte Carlo simulation to sketch the annual operational loss distribution. In the experiment, the proposed approach is employed to calculate the operational risk capital charge for the overall Chinese banking. The experiment dataset is the most comprehensive operational risk dataset in China as far as we know. The results show that the proposed approach is able to use the information of high order moments and might be more effective and stable than the usually used parametric approach.
Discrete Dynamics in Nature and Society 03/2014; 2014. DOI:10.1155/2014/839731 · 0.88 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: In loss distribution approach (LDA), the most popular approach in operational risk modeling, frequency dependence and loss distribution dependence across business lines are two dependences which banks should consider. In practice, mainly for simplicity, many banks only model frequency dependence although they think that the impact of frequency dependence is insignificant. In this study, two approaches, respectively, models frequency dependence and loss distribution dependence, are introduced. Both approaches are modeled by copula function, which is capable of capturing nonlinear correlation. Based on the most comprehensive operational risk dataset of Chinese banking as far as we know, the operational risk capital charge of the overall Chinese banking is calculated by the two approaches. The results show that there is an obvious distinction between the capital calculated by modeling frequency dependence and the capital calculated by modeling loss dependence. The approach with very limited attention exactly yields a much larger capital result. So it is advised in this paper that banks should not just rely on the approach to modeling frequency dependence for it is natural and easy to deal with. A safer and more effective way for banks is to comprehensively take the results of the two kinds of approach into consideration.
[Show abstract][Hide abstract] ABSTRACT: This paper presents a factor copula model for the integration of Chinese commercial banks’ credit risk and market risk. By defining the dependence structure through a set of common factors reflecting the macro-economic situation, this model reveals the intrinsic correlation between credit risk and market risk. We derive the integration process with factor copula and generate common factors by performing a principal component analysis on 4 different macro-economic indicators that have impact on bank's profit, namely the GDP growth, M2 growth, benchmark for loan rate, and the ratio of new loans to GDP. In the empirical study, 15 Chinese listed banks are chosen to construct the model. The results are compared with that of elliptical copulas and Archimedean copulas, we find that factor copula gives a more prudential result in risk integration.
[Show abstract][Hide abstract] ABSTRACT: In this paper, a change point detection approach based on copula with two notable advantages is put forward. One is that the approach can deal with the common but special unbalanced panel data. The other is that it can detect multiple change points. Firstly, a proper copula that most accurately describes the dependence structure of the data is chosen. Then, the chosen copula is fitted to the data dynamically by adding new data. Finally, the change points are located by analyzing the trends o f fitted parameters of the copula. Based on the quarterly financial data of 16 listed Chinese commercial banks, we empirically use the proposed approach to detect the subprime crisis contagion period in Chinese banking. The results show that the contagion starts in 2007Q2 and ends in 2009Q1, which is reasonable according to relevant researches.
[Show abstract][Hide abstract] ABSTRACT: The severity loss distribution is the main topic in operational risk estimation. In this paper, we propose a novel model for quantifying operational risk in the framework of the loss distribution approach (LDA) as suggested by the Basel II. We use Cornish¡VFisher Expansion, which is non-parameter method, to fit operational risk loss severity, and then we use simulation technique to measure the operational risk in the framework of LDA. We use this approach to measure the operational risk of Chinese commercial banking. Empirical analysis shows that this approach allows the allocation of capital in an efficient way.
[Show abstract][Hide abstract] ABSTRACT: In the Basel II Accord, banks are encouraged to use the Advanced Measurement Approach (AMA), which is suitable for banks to assess operational risk capital, but banks are required to demonstrate their ability to capture severe tail loss events. In this paper, based on the 860 operational risk loss data of Chinese commercial banks collected from public reports from 1995 to 2006, we found that the sample data set is characterized as having heavier tail than normal distribution. Then we use loss distribution approach (LDA) to measure the operational risk and operational risk capital of Chinese commercial banks. Next, we compare operational risk economic capital of Chinese commercial banks with operational risk economic capital (EC) of other major banks. We discover that the operational risk of Chinese commercial banks is larger than that of some foreign major commercial banks.
Grey Systems and Intelligent Services, 2009. GSIS 2009. IEEE International Conference on; 12/2009
[Show abstract][Hide abstract] ABSTRACT: Following the Basel II Accord, with the increased focus on operational risk as an aspect distinct from credit and market risk, quantification of operational risk has been a major challenge for banks. This paper analyzes implications of the advanced measurement approach to estimate the operational risk. When modeling the severity of losses in a realistic manner, our preliminary tests indicate that classic distributions are unable to fit the entire range of operational risk data samples (collected from public information sources) well. Then, we propose a piecewise-defined severity distribution (PSD) that combines a parameter form for ordinary losses and a generalized Pareto distribution (GPD) for large losses, and estimate operational risk by the loss distribution approach (LDA) with Monte Carlo simulation. We compare the operational risk measured with piecewise-defined severity distribution based LDA (PSD-LDA) with those obtained from the basic indicator approach (BIA), and the ratios of operational risk regulatory capital of some major international banks with those of Chinese commercial banks. The empirical results reveal the rationality and promise of application of the PSD-LDA for Chinese national commercial banks.
International Journal of Information Technology and Decision Making 12/2009; 08(04):727-747. DOI:10.1142/S0219622009003727 · 1.41 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Operational risk is one of the most important risks for Chinese commercial banks, and brings huge losses to Chinese commercial
banks recent years. Using the public reported operational loss data from 1997 to 2005 of Chinese commercial banks, we simulate
the operational loss distribution, find that loss frequency can be seen as Poisson distribution and the logarithm of loss
is normal distribution. In accordance with the confidence level required by Basel II, aggregated loss distributions and operational
Value-at-Risks (OpVaR) are calculated by Monte Carlo Simulation. Comparing with the real loss, this result is credible. We
also calculate the economic capital by the VaR
99.9, and it maybe help the banks to allocate appropriate their economic capital.
Computational Science - ICCS 2006, 6th International Conference, Reading, UK, May 28-31, 2006, Proceedings, Part IV; 01/2006