Recent Developments on the Stability and Control of Stochastic Systems
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Multivariate TimeVarying Copula GARCH Model and Its Application in the Financial Market Risk Measurement
Abstract
Taking full advantage of the strengths of  distribution, Copula function, and GARCH model in depicting the return distribution of financial asset, we construct the multivariate timevarying  Copula GARCH model which can comprehensively describe “asymmetric, leptokurtic, and heavytail” characteristics, the timevarying volatility characteristics, and the extremetail dependence characteristics of financial asset return. Based on the conditional maximum likelihood estimator and IFM method, we propose the estimation algorithm of model parameters. Using the quantile function and simulation method, we propose the calculation algorithm of VaR on the basis of this model. To apply this model on studying a real financial market risk, we select the SSCI (China), HSI (Hong Kong, China), TAIEX (Taiwan, China), and SP500 (USA) from January 3, 2000, to June 18, 2010, as the samples to estimate the model parameters and to measure the VaRs of various index risk portfolios under different confidence levels empirically. The results of the application example are in line with the actual situation and the risk diversification theory of portfolio. To a certain extent, these results also justify the feasibility and effectiveness of the multivariate timevarying  Copula GARCH model in depicting the return distribution of financial assets.
1. Introduction
Financial market risk has always been one of the hottest topics in the field of financial investment, and many financial researchers put forward many different financial market risk measurement methods. Among them, ValueatRisk (VaR) management technology is an assessment and measurement method of financial risk that has risen in recent years, playing an increasingly important role in the risk management and investment decision. It has been widely adopted by the major banks, nonbank financial intermediaries, corporations, and financial regulators in the world and has become the standard of risk measurement and risk management in financial industry. Accurate calculation of VaR is one of the keys to estimate the probability distribution of future return on financial assets. Usually, it is assumed that financial asset returns are independent of each other and obey the normal distribution in the calculation of VaR, but the movement of financial asset return in the financial market is extremely complex. The return of all kinds of financial assets usually does not satisfy the normal distribution hypothesis. However, it often shows “asymmetric, leptokurtic, and heavytail” characteristics [1–4]. At the same time, various financial asset returns do not satisfy the multivariate normal distribution hypothesis and present the extremetail dependence. On this occasion, a large error would be made by using normal distribution to fit financial asset return, and the estimation of the VaR may be overestimated or underestimated. To solve this problem, many scholars have proposed a lot of leptokurtic and heavytail distributions in recent years, such as the logistic distribution, Student’s distribution, and the distribution. The logistic distribution and Student’s distribution can comprehensively describe the leptokurtic characteristics of financial asset return series, but they could not make a good explanation for the heavytail characteristics of financial asset return series [5]. The  distribution can comprehensively describe the asymmetric, leptokurtic, and heavytail characteristics of financial asset return series and it has a good fitting of the univariate unconditional return distribution of some financial assets; however, it could not reflect the timevarying volatility characteristics of financial asset return and the extremetail dependence characteristics of various financial assets return [6, 7]. Meanwhile, Copula function can connect the joint distribution and the marginal distribution of multiple random variables to construct flexible multivariate distribution functions, which can be used to measure the extremetail dependence of multiple financial assets return. GARCH model can comprehensively describe the timevarying volatility characteristics of financial asset return. Therefore, building the multivariate timevarying  Copula GARCH Model by combining the  distribution with Copula function and GARCH model can not only comprehensively describe the “asymmetric, leptokurtic, and heavytail” characteristics, the timevarying volatility, and extremetail dependence characteristics of the financial asset return and make measurement of VaR more accurately, but also enrich and expand the risk measurement theory and method of financial market theoretically and improve the risk control ability of investors, corporations, financial institutions, and policy authorities and reduce their unnecessary losses in practice.
The  distribution, Copula function, and GARCH model, as well as the financial risk measurement model based on them, have been researched in the existing relevant literatures. A lot of innovative research results with reference value have been brought out. Zhu and Pan [8] proposed three kinds of  VaR methods based on the portfolio gains, losses, and extreme losses according to the statistical characteristics of  distribution. Their empirical results showed that this method is superior to the commonly used deltanormal method. Kuester et al. [9] believed that the  distribution can describe skewness and kurtosis of the financial asset return simultaneously and it plays a very important role of VaR measurement of financial asset return. Degen et al. [10] discussed the application of  distribution in operational risk measurement. Jondeau and Rockinger [11], Rodriguez [12], Fischer et al. [13], and Sun et al. [14] combined time series model with various Copulas functions by using the Sklar theorem to build a lot of highly flexible multivariate timevarying models for risk measurement of portfolios. Liu et al. [15] proposed a GARCH model with a timevarying coefficient of the risk premium. Their study indicated that the coefficient of the risk premium varies with the time, and even in a mature market the conditional skewness in the return distribution is negatively correlated with the timevarying coefficient of the risk premium. Wen et al. [16] built a GARCH model by separating investors’ return into gains and losses on the basis of the characteristics of investors’ risk preference. They found that investors become risk averse when they gain and riskseeking when they lose, which effectively explains the inconsistent riskreturn relationship. And the degrees of investors’ risk aversion and riskseeking are both in direct proportion to the value of gains and losses, respectively. Wen et al. [17] adopted aggregative indices of 14 representative stocks around the world as samples and established a TVRAGARCH model to investigate the influence of prior gains and losses on current risk attitude. The empirical results indicated that the prior gains increase people’s current willingness to take risk asset at the whole market level. Huang et al. [18] combined Student’s marginal distribution with Archimedean Copula functions to build the conditional Copula GARCH model. They used this model to estimate the VaR of portfolios. Ghorbel and Trabelsi [19] built the conditional extremum Copula GARCH model by using extreme value theory (EVT) and measured the risk of financial asset according to this model. Chollete et al. [20] used multivariate regimeswitching Copula function to build international financial asset return model and accordingly put forward the VaR calculation method. Huggenberger and Klett [21] proposed a measurement model of multivariate risk asset return VaR based on  distribution and Copula function. They used DAX30 (Germany), FTSE100 (UK), and CAC40 (French) from January 2000 to May 2010 as samples to test empirically. Wang et al. [22] applied the Gumbel Copula function in multivariate Archimedean Copula functions family to construct the joint distribution function which can describe the actual distribution and the correlation of various financial asset returns. They also used the Monte Carlo simulation technology to analyze the portfolios VaR and its composition under different confidence levels. The result showed that using the multidimensional Gumbel Copula function to construct the risk measurement model of financial asset can make the assets chosen by investors more robust, and it can also help investors to diversify and control the overall risk of the portfolios. Dai and Wen [23] proposed a computationally tractable robust optimization method for minimizing the CVaR of a portfolio under a general affine data perturbation uncertainty set. And they presented some numerical experiments with real market data to illustrate the behavior of robust optimization model. Liu et al. [24] proposed a pricing model for convertible bonds based on the utilityindifference method and got access to the empirical results by use of Information Technology. Furthermore, using the proposed theoretical model, they presented an empirical pricing study of China's market. They found that the theoretical prices are higher than the actual market prices 0.24–4.58% and the utilityindifference prices are better than the BlackScholes (BS) prices.
Based on the aforementioned analyses, the VaR is still the mainstream measurement method of financial market risk. In order to achieve the purpose of measuring VaR more precisely, it has been the hot issue of existing research literatures to construct the distribution functions as comprehensive as possible to describe the “asymmetric, leptokurtic, and heavytail” characteristics, the timevarying volatility characteristics, and the extremetail dependence characteristics of financial asset return through a variety of mathematical methods. However, in the process of constructing the return distribution model and measuring VaR of financial asset, existing results only grasp some characteristics of financial asset return distribution. They are not able to comprehensively describe the “asymmetric, leptokurtic, and heavytail” characteristics, the timevarying volatility characteristics, and the extremetail dependence characteristics of the financial asset return. The rationality and accuracy of VaR calculated based on the existing distribution models have a large space for further improvement.
In this paper, we would take full advantage of the strengths of  distribution, Copula function, and GARCH model in depicting the return distribution of financial asset to build multivariate timevarying  Copula GARCH model which can simultaneously describe “asymmetric, leptokurtic, and heavytail” characteristics, the timevarying volatility characteristics, and the extremetail dependence characteristics of financial asset return and propose the estimation method of model parameters and the calculation algorithm of VaR. Then, this paper selects the SSCI (China), HSI (Hong Kong, China), TAIEX (Taiwan, China), and SP500 (USA) from January 3, 2000, to June 18, 2010, as samples to estimate the parameters and calculate the VaRs of various index portfolios under different confidence levels.
2. Distribution, Copula Function, and Its Tail Dependence Index
2.1. Distribution
2.1.1. Distribution
Assuming that random variable obeys the standard normal distribution and is a real number, then the random variable obeys distribution. Considerwhere controls the skewness of distribution. When , and distribution tends to be symmetric. With the increase of the absolute value of , the degree of asymmetry increases. Changing the sign of can change the asymmetric direction of distribution, but it does not change its degree of asymmetry.
2.1.2. Distribution
Assuming that random variable obeys the standard normal distribution and is a real number, then the random variable obeys distribution. Consider
distribution stretches the tail of the standard normal distribution. controls the tail heaviness of distribution. The larger the is, the heavier the tail is. Because is an even function, distribution is symmetric. But the heaviness of its tail changes compared to the standard normal distribution.
2.1.3.  Distribution
The random variable can be obtained by introducing both functions and to revise standard normal random variable . Consider Then, can be obtained through linear transformation of . Consider
The distribution of the random variable obeys the  distribution. , , , and are real numbers. describes the asymmetry of  distribution, and describes the heavytail characteristics of  distribution. Obviously, (3) is a special form of (4). The random variable in (3) is the random variable of  distribution after central standardization.
2.2. Copula Function and Its Tail Dependence Index
Assuming that marginal distribution of random vector () obeys uniform distribution , according to the Sklar theorem, the joint distribution function of dimensional random vectors can be represented as the following formula:where is the Copula function of , which is a hypercube multivariate density function defined on dimensional space . If the marginal distribution is continuous, there is a unique Copula function . Then
On the contrary, given dimensional Copula function and its marginal distribution function , the density function of dimensional joint distribution function is
If is the edge density, denotes Copula density derived from (6). Thus,
Since the joint distribution function of random variables defines the correlation among its components, Copula function determines the dependent structure among random variables uniquely. The upper tail index and lower tail index of tail dependence indicators can be defined as follows:
According to Nelsen [25], Gauss Copula function generated by multivariate normal distribution function whose correlation matrix is can be represented as follows:where and is the inverse function of single normal distribution. Because the Gauss Copula function does not have the characteristics of tail dependence, we often use the Copula function whose degree of freedom is and correlation matrix is to measure tail dependence structure of risk asset in empirical analysis; that is, where is the inverse function of simple standard Student’s distribution whose degree of freedom is . When , Copula function degenerates to Gauss Copula function. Its tail index ; that is, the tail is independent. The tail index of Copula function is where is simple standard Student’s distribution whose degree of freedom is . Considering that the innovation impacts on the price of risk asset in varying degrees at different times, and should have timevarying characteristics. For this reason, tail index also has the same characteristics.
3. Multivariate TimeVarying  Copula GARCH Model
Let denote return time series of risk assets. The prior information set before time iswhere . is conditional volatility of about single asset prior information set . Let denote dimensional conditional Copula function and be the conditional distribution of the th component. According to Sklar theorem, the conditional joint distribution of risk assets return is
Numerous empirical studies show that the risk asset return series obey GARCH model. Based on this, assuming that satisfies the GARCH model, we can get the following  Copula GARCH model which describes the timevarying dependence structure of risk assets return after filtering the timevarying characteristics of single series:where the parameters satisfy the conditions and . These parameters can ensure the stability of conditional volatility series. The innovation series obey  distribution whose parameter is in (4). But in order to simplify the analysis, we only consider  distribution after central standardization given by (3) and its density function is written as . The Copula function is given by (11) and its density can be represented as the following timevarying Copula function whose degree of freedom is : where denotes the multivariate Student’s distribution whose degree of freedom is and timevarying correlation matrix is , and
The joint density function of risk assets return is where and . Then the likelihood function of overall samples is where and . The value of correlation matrix is similar to the timevarying correlation matrix of multivariate Copula GARCH model proposed by Jondeau and Rockinger [11]. That is, satisfies the following evolution equationwhere , , . is a positive definite matrix whose main diagonal elements are 1 and other elements are static correlation coefficients. is a matrix, in which every elementdenotes the correlation coefficients of risk asset returns, , . Each element of satisfies .
4. Parameter Estimation Algorithm of the Multivariate TimeVarying  Copula GARCH Model
On the basis of Huggenberger and Klett [21], this section will use dynamic correlation matrix instead of static correlation matrix in the multidimensional discretetime stochastic process to estimate the parameters of multivariate timevarying  Copula GARCH model established in Section 3. Assuming that denotes the parameter space defined by the model and denotes the log return samples of dimensional risk asset which is generated by multivariate conditional density function , where , is algebra of time and before, the maximum likelihood estimation of parameter vector can be calculated by the following equation: where can be obtained by calculating the derivative of (5). Let denote Copula density function. Thus
The probability density function and distribution function can be obtained in the process of model built in Section 3. Using the IFM method proposed by Joe [26], we can convert (22) into an optimization problem. Therefore, we need to divide the parameter vector into two subparameter vectors and : that is , where , is the parameter vector of th marginal distribution, and is the parameter vector of Copula function. Because IFM method is a twostep likelihood estimation method, the model parameters should be estimated through the following two steps.
Step 1. Solving the maximum likelihood estimator of the parameter vector of each risk asset return,
This means that we need to estimate parameters vector of distributions continuously.
Step 2. Taking each into the likelihood equation (22), we can obtain the parameter vector of Copula function and its maximum likelihood estimator . Consider
In the maximum likelihood estimation, we need to use the derivative function of the density function of  marginal distribution with respect to the component of parameter vector. Because the density function of  marginal distribution is very complex, this paper uses the implicit function differentiation rule to take its derivative. The estimator of parameter vector obtained by the abovementioned twostep method obeys normal distribution consistently and asymptotically under the standard regularity conditions proposed in Huggenberger and Klett [21], Joe [26], and Patton [27]; that is, where
Because the matrixes and can be estimated by the estimated parameter vector consistently,
Thus (26) can be used to calculate the standard deviation of the estimator .
5. VaR Algorithm Based on the Multivariate TimeVarying  Copula GARCH Model
After estimating the parameters of multivariate timevarying  Copula GARCH model, VaR of the risk portfolio can be measured. VaR of risk portfolio indicates the expected maximum losses of risk portfolio held by investors within a given confidence level and a certain period of time. Assuming that () are the return samples of risk assets which satisfy the multivariate timevarying  Copula GARCH model in Section 3 and is the portfolio of risk assets in which the weight of the risk asset is () that can be less than 0 because of permitting shortpurchasing and shortselling the risk assets and meet , the VaR of risk portfolio under confidence level at time should satisfy . The confidence level can reflect the different risk preferences of investors or financial institutions to a certain extent. Choosing a larger confidence level means that investors or financial institutions have greater risk aversion, and they hope to get a forecast result with larger probability.
Although the conditional distributions of can be calculated through the known marginal distributions, it is very difficult to calculate quantile from timevarying Copula density function, and it is adverse to measure and calculate the VaR of risk portfolio. Therefore, this paper measures the dynamic risk of portfolio and its estimation value approximately through simulating  Copula GARCH model. Based on the parameters of the sample, the return series of risk assets and the onestep measurement and estimation values of VaR of their portfolios can be obtained through estimating according to the volatility equation of  Copula GARCH model and calculating by using Copula dynamic evolution equation and then repeating the following algorithm for times ().
Step 1. It is simulating groups of random vectors according to the multivariate Copula density function whose degree of freedom is and correlation matrix is .
Step 2. Calculating , .
Step 3. Firstly, one calculates the return rate of risk portfolio that is equal to , . Secondly, one evaluates its quantile . Thirdly, one measures the VaR of the risk portfolio by .
6. Application of the Multivariate TimeVarying  Copula GARCH Model
6.1. Date Sample and Moment Estimation
USA and China, as the most developed capitalism country and the largest developing country in the world, respectively, rank top two of the world economy. Their stock markets should have strong representation in the world. At the same time, due to the historical reasons, there exist several regions with different political systems such as Mainland China, Hong Kong, Taiwan, and Macau in Greater China. Macau is similar to Hong Kong on the whole. For the abovementioned reasons, this paper selects the SSCI (China), HSI (Hong Kong, China), TAIEX (Taiwan, China), and SP500 (USA) from January 3, 2000, to June 18, 2010, as data samples to estimate the VaR of various index portfolios under different confidence levels by using the multivariate timevarying Copula GARCH model. The data comes from Yahoo Finance website: http://finance.yahoo.com/.
The moment estimation results of the daily log return of SSCI, HSI, TAIEX, and SP500 are shown in Table 1.

Table 1 shows that the skewness of daily log returns of SSCI, HSI, TAIEX, and SP500 is less than 0 and their kurtosis is much larger than that of standard normal distribution which is equal to 3. These results demonstrate that the daily log returns of these indices have the right skew and leptokurtic characteristics. Therefore, it is appropriate to fit the daily log return of SSCI, HSI, TAIEX, and SP500 by applying  distribution which has leptokurtic, heavytail characteristics, and it is reasonable to apply the multivariate timevarying  Copula GARCH model to measure their VaR.
6.2. Parameter Estimates of the Multivariate TimeVarying  Copula GARCH Model
Based on the parameter estimation algorithm proposed in Section 4, the parameters of the multivariate timevarying  Copula GARCH model with SSCI, HSI, TAIEX, and SP500 can be estimated. The parameter estimation results are shown in Tables 2, 3, 4, 5, and 6.
 
Note: in the table denotes that the parameter is significant at 1% level. 
 
Note: in the table denotes that the parameter is significant at 1% level. 
 
Note: in the table denotes that the parameter is significant at 1% level. 
 
Note: in the table denotes that the parameter is significant at 1% level. 
 
Note: and in the table denote that the parameter is significant at 1% and 5% level, respectively. 
From Tables 2 to 6, the following results can be obtained:(1)Consider , , , and . This shows that the volatility persistence of Shanghai stock market is the strongest, Taiwan and Hong Kong stock market rank second and third, and the volatility persistence of USA stock market is minimum. It indicates that the investors’ expectation of risk compensation in the emerging markets represented by China’s stock market is stronger than that in the mature markets represented by the USA’s stock market and the price discovery efficiency of innovation in the emerging markets represented by China’s stock market is lower than that in the mature markets represented by USA’s stock market. In addition, the sum of the coefficients and is very close to 1, which indicates that the impact and shock of innovation on the index volatility of each stock market has a long memory.(2)The degree of freedom and the correlation coefficients of Copula in Table 6 show that there exists the strongest correlation between Hong Kong stock market and Taiwan stock market, and the correlation between Shanghai stock market and Hong Kong stock market is also relatively large. The abovementioned facts indicate that the extreme events probably result in the phenomena that Hong Kong stock market and Taiwan stock market are up and down synchronously, and there exist comoving behaviors between Shanghai stock market and Hong Kong stock market.(3)The timevarying coefficient indicates that the timevarying correlation coefficient of  Copula GARCH model has a long memory; that is, the impact of historical values of each other’s correlation coefficient among SSCI, HSI, TAIEX, and SP500 on the expected correlation is relatively large.
6.3. VaR Measurement Based on the Multivariate TimeVarying  Copula GARCH Model
Based on the multivariate timevarying  Copula GARCH model with SSCI, HSI, TAIEX, and SP500 whose parameters have been estimated, the VaRs of various index portfolios under different confidence levels can be measured. The measurement results are shown in Table 7.
 
Note: the ratio of index portfolio is ranked by the sequence of SSCI : HSI : TAIEX : SP500 in Table 7. 
From Table 7, the following results can be obtained:(1)The inequalities VaR (SSCI) < VaR (HSI) < VaR (SP500) < VaR (TAIEX) can be satisfied for any confidence level. It shows that the risk of extreme losses in Shanghai stock market is higher than that in Hong Kong stock market, Taiwan stock market, and USA stock market. This measurement result is in line with the actual situation that the maturity of Shanghai stock market is far lower than that of Hong Kong stock market, Taiwan stock market, and USA stock market.(2)For any confidence level, the extreme losses risk of the investors who equally allocate their total assets among SSCI, HSI, TAIEX, and SP500 is lower than that of the investors who put their total assets into one index asset. The extreme losses risk of the investors increases with the concentration of risk asset in the index portfolios. This measurement result is consistent with the risk diversification theory of portfolio.
7. Conclusion
Considering the “asymmetric, leptokurtic, and heavytail” characteristics, the timevarying volatility characteristics, and extremetail dependence characteristics of financial asset return, this paper combined the  distribution, Copula function, and GARCH model to construct a multivariate timevarying  Copula GARCH model which can comprehensively describe the “asymmetric, leptokurtic, and heavytail” characteristics, the timevarying volatility characteristics, and extremetail dependence characteristics of financial asset return. It proposed the parameter estimation algorithm of the multivariate timevarying  Copula GARCH model by using condition maximum likelihood method and IFM twostep method. An algorithm was constructed to calculate VaR by using the quantile function and the simulation method based on  Copula GARCH model. In addition, this paper selected the daily log return of SSCI (China), HSI (Hong Kong, China), TAIEX (Taiwan, China), and SP500 (USA) from January 3, 2000, to June 18, 2010, as samples to estimate the parameters of the multivariate timevarying  Copula GARCH model, and it also estimated the VaR for various index risk asset portfolios under different confidence levels. The research results showed that the multivariate timevarying  Copula GARCH model constructed in this paper could reasonably estimate and measure the extreme losses of risk portfolios in financial market, and the measurement results were in line with the actual situation of stock market and the risk diversification theory of portfolio. The achievement of this paper provided a practical and effective method for measuring the extreme losses of financial market.
Conflict of Interests
The authors declare that they have no conflict of interests regarding the publication of this paper.
Acknowledgments
The authors would like to thank the referees for all helpful comments and suggestions. This work is supported by Natural Science Foundation Project of CQ CSTC (Grant no. CSTC, 2011BB2088) and Key Project of National Natural Science Foundation (Grant no. 71232004).
References
 S. J. Kon, “Models of stock returns—a comparison,” The Journal of Finance, vol. 39, no. 1, pp. 147–165, 1984. View at: Publisher Site  Google Scholar
 J. B. Gray and D. W. French, “Empirical comparisons of distributional models for stock index returns,” Journal of Business Finance & Accounting, vol. 17, no. 3, pp. 451–459, 1990. View at: Publisher Site  Google Scholar
 E. Jondeau and M. Rockinger, “Testing for differences in the tails of stockmarket returns,” Journal of Empirical Finance, vol. 10, no. 5, pp. 559–581, 2003. View at: Publisher Site  Google Scholar
 C. Jiang, S. Li, and S. Liang, “Empirical investigation of distribution feature of return in china stock market,” Application of Statistics and Management, vol. 26, no. 4, pp. 710–717, 2007. View at: Google Scholar
 D. Dong and W. Jin, “A subjective model of the distribution of returns and empirical analysis,” Chinese Journal of Management Science, vol. 15, no. 1, pp. 112–120, 2007. View at: Google Scholar
 J. Martinez and B. Iglewicz, “Some properties of the tukey g and h family of distributions,” Communications in Statistics—Theory and Methods, vol. 13, no. 3, pp. 353–369, 1984. View at: Publisher Site  Google Scholar  MathSciNet
 T. C. Mills, “Modelling skewness and kurtosis in the London stock exchange FTSE index return distributions,” The Statistician, vol. 44, no. 3, pp. 323–332, 1995. View at: Publisher Site  Google Scholar
 H. Zhu and Z. Pan, “A study of portfolios VaR method based on gh distribution,” Chinese Journal of Management Science, vol. 13, no. 4, pp. 7–12, 2005. View at: Google Scholar
 K. Kuester, S. Mittnik, and M. S. Paolella, “Valueatrisk prediction: a comparison of alternative strategies,” Journal of Financial Econometrics, vol. 4, no. 1, pp. 53–89, 2006. View at: Publisher Site  Google Scholar
 M. Degen, P. Embrechts, and D. Lambrigger, “The quantitative modeling of operational risk: between gandh and EVT,” Astin Bulletin, vol. 37, no. 2, pp. 265–291, 2007. View at: Publisher Site  Google Scholar  MathSciNet
 E. Jondeau and M. Rockinger, “The CopulaGARCH model of conditional dependencies: an international stock market application,” Journal of International Money and Finance, vol. 25, no. 5, pp. 827–853, 2006. View at: Publisher Site  Google Scholar
 J. C. Rodriguez, “Measuring financial contagion: a Copula approach,” Journal of Empirical Finance, vol. 14, no. 3, pp. 401–423, 2007. View at: Publisher Site  Google Scholar
 M. Fischer, C. Kock, S. Schluter, and F. Weigert, “An empirical analysis of multivariate copula models,” Quantitative Finance, vol. 9, no. 7, pp. 839–854, 2009. View at: Publisher Site  Google Scholar  MathSciNet
 W. Sun, S. Rachev, F. J. Fabozzi, and S. S. Kalev, “A new approach to modeling comovement of international equity markets: evidence of unconditional copulabased simulation of tail dependence,” Empirical Economics, vol. 36, no. 1, pp. 201–229, 2009. View at: Publisher Site  Google Scholar
 Z. Liu, T. Zhang, and F. Wen, “Timevarying risk attitude and conditional skewness,” Abstract and Applied Analysis, vol. 2014, Article ID 174848, 11 pages, 2014. View at: Publisher Site  Google Scholar
 F. Wen, Z. He, Z. Dai, and X. Yang, “Characteristics of investors' risk preference for stock markets,” Economic Computation and Economic Cybernetics Studies and Research, vol. 3, no. 48, pp. 235–254, 2014. View at: Google Scholar
 F. Wen, X. Gong, Y. Chao, and X. Chen, “The effects of prior outcomes on risky choice: evidence from the stock market,” Mathematical Problems in Engineering, vol. 2014, Article ID 272518, 8 pages, 2014. View at: Publisher Site  Google Scholar  MathSciNet
 J.J. Huang, K.J. Lee, H. Liang, and W.F. Lin, “Estimating value at risk of portfolio by conditional copulaGARCH method,” Insurance: Mathematics and Economics, vol. 45, no. 3, pp. 315–324, 2009. View at: Publisher Site  Google Scholar  MathSciNet
 A. Ghorbel and A. Trabelsi, “Measure of financial risk using conditional extreme value copulas with EVT margins,” Journal of Risk, vol. 11, no. 4, pp. 51–85, 2009. View at: Google Scholar
 L. Chollete, A. Heinen, and A. Valdesogo, “Modeling international financial returns with a multivariate regimeswitching copula,” Journal of Financial Econometrics, vol. 7, no. 4, pp. 437–480, 2009. View at: Publisher Site  Google Scholar
 M. Huggenberger and T. Klett, “A gandh copula approach to risk measurement in multivariate financial models,” Working Paper, 2010, http://ssrn.com/abstract=1677431. View at: Google Scholar
 J. Wang, W. Bao, and J. Hu, “Portfolios VaR analyses based on the multidimensional gumbel copula function,” Journal of Applied Statistics and Management, vol. 29, no. 1, pp. 137–143, 2010. View at: Google Scholar
 Z. Dai and F. Wen, “Robust CVaRbased portfolio optimization under a genal affine data perturbation uncertainty set,” Journal of Computational Analysis and Applications, vol. 16, no. 1, pp. 93–103, 2014. View at: Google Scholar  MathSciNet
 J. Liu, M. Tao, C. Ma, and F. Wen, “Utility indifference pricing of convertible bonds,” International Journal of Information Technology and Decision Making, vol. 13, no. 2, pp. 429–444, 2014. View at: Publisher Site  Google Scholar
 R. B. Nelsen, An Introduction to Copulas, Springer Science & Business Media, New York, NY, USA, 2007.
 H. Joe, Multivariate Models and Dependence Concepts, vol. 73 of Monographs on Statistics and Applied Probability, Chapman & Hall, London, UK, 1997. View at: Publisher Site  MathSciNet
 A. J. Patton, “Estimation of multivariate models for time series of possibly different lengths,” Journal of Applied Econometrics, vol. 21, no. 2, pp. 147–173, 2006. View at: Publisher Site  Google Scholar  MathSciNet
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Copyright © 2015 Qian Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.