ABSTRACTAfter the exchange rate ref

ABSTRACT
After the exchange rate reforms in 2005, China has transformed the fixed exchange rate system into a floating exchange rate system dominated by market supply and demand. Commercial banks will face with greater exchange rate risk. Therefore, how to estimate exchange rate risk and keep the optimal portfolio of foreign exchange is an important research subject. This article chooses the date of the RMB exchange rate against the dollar and the yen from the January 1, 2008 to May 21, 2012 as samples, describes the joint distribution of the two assets using Copula-Garch model, thus eventually works out the optimal holding ratio of the two foreign currency assets under minimal risk situations.
Keywords:Exchange Rate Risk, Copula Function, VaR

1. Introduction
With the establishment of the floating exchange rate system in China, the number of foreign assets held by commercial banks is increasing, as well as frequent financial crises in the international market, exchange rate risk has become an important source of financial risk of commercial banks.
Many domestic and foreign scholars have done the analysis of exchange rate risk, Galai Dan and Michel Crouhy believe that the reason for the existence of the exchange rate risk is foreign currency exposure, which cannot be eliminated through the financial derivatives. Huali Huang thinks that the value of the foreign exchange positions held by commercial banks as the foreign exchange market exchange rate volatility is the direct cause of commercial bank exchange rate risk, and changes in the exchange rate are mainly influenced by the foreign exchange market supply and demand [1] . According to the comprehensive research of scholars both at home and abroad, the author thinks that the fundamental cause of the exchange rate changes is the change of market supply and demand of foreign exchange, while changes of the relationship between supply and demand in the foreign exchange market are determined by each country’s domestic political and economic factors. The specific reasons are as follows: first, in economic transactions, there exists a foreign exchange exposure in the gap between financial assets and financial liabilities. Second, foreign exchange rate risks arise from international currency trading behavior of economic subject in the market. Third, the exchange rate risk and time factors are closely linked. From the intention of a deal to the end of the last transaction settlement, there are certain time intervals. Finally, exchange rate risk is derived from the multinational investment behavior of economic agents. Because of commercial banks for transnational investment, exchange rate changes will cause the bank value change, affect the company’s cash flow, as well as impact on the profitability of banks [2] -[4] .
The key of prevention and control of the exchange rate risk is the effective measure of exchange rate risk. The VaR method is widely used at present. In the 90’s, foreign scholars began to research the VaR method, their research mainly includes the selection of VaR model, the scope of application of VaR, and the re-innovation of VaR model. In the measurement of VaR, assuming that yields obey a certain distribution is one of the earliest calculation methods of VaR parameter. But later it was found that most of the time series of financial assets showed a fat tail nature, not simply showing a normal distribution, and often extreme cases occur together, so the assumption is inconsistent with the earlier objective reality. Until 1982, Engle proposed autoregressive conditional heteroskedasticity model, referred to as the ARCH model, the model believes that the variance and conditional variances are not the same; the conditional variances are related to the error function before; this model can effectively solve the spike thick tail of financial assets time sequence model; this feature provides a new way to solve the heteroscedasticity. Robert F. Engle (1982) introduced the hypothesis and principle of each method that VaR based on, in the end, he used the monte carlo simulation method to calculate the end result based on different VaR method, and compared the result of the calculation. By comparison, the conclusion is as follows: for the present fat-tailed data, CVaR model is the best. Eric Bouye systematically introduces the application of copulas connect function in the financial sector, in addition to the common application of portfolio risk correlation, will also extends to value at risk (VaR) calculations, using copulas connect function further to analyze the credit risk and market risk [5] . Romnano using copulas connect function to calculate risk portfolio risk, he thinks that risks can be divided into individual risk and its structure risk associated with individual external structure though copulas connect function, the article also uses multivariate extremum function through monte carlo simulation to calculate market risk. Rosenberg and Schuermann used the VaR method to measure risk, and they compared the accuracy of several models to calculate the VaR values. The results showed that copula model was the best way of VaR calculation [6] .
Min Chen, a domestic scholar, used the main calculation method of VaR model. She contrasts the pros and cons of various models by later accuracy test. The main calculation method she used includes the historical simulation method, the delta-normal method, etc. The adopted model includes the ARCH model and GARCH model. The final conclusion is that GARCH (1, 1) model based on the t distribution has a better fitting effect in commercial bank exchange rate risk accuracy measurement. Qing Ye uses semi-parametric model based on GARCH model, which is a good measure of risk in China’s mainland stock market fluctuation, and the semiparametric model based on GARCH model in the measurement of China’s stock market risk is more accurate by comparing the different models. Lujie Sun and Manying Bo combined copula function and the VaR method, and compared the traditional VaR model and the new model. Through the empirical study of the euro and the dollar portfolio, results showed that the VaR model based on copula function can more accurately measure the risk of exchange rate [7] [8] .
2. Exchange Rate Risk Measurement Model Selection and Testing Steps
Risk value method is the internationally accepted measure method of foreign exchange rate risk. In a certain period, determine a good confidence level in advance, the commercial bank asset portfolio value at risk showed the positions of the maximum possible losses. Holding period, the confidence level and risk factors is three factors the method must consider. The managers of commercial banks based on their risk tolerance, choose suitable holding period and the confidence level, and take factors that affect the risk of exchange rate volatility into the appropriate model, then get a possible maximum loss amount, which is the value at risk in the future.
VaR method does not apply in financial markets when extreme abnormal fluctuations occurs such as political, military, culture, and some the long-term factors. The VaR approach as a short-term analysis tool is appropriate when financial market are stable.
VaR English full name is the Value at Risk is often translated as “Value at Risk”. The statistical formula can be defined as:



(1)
In Equation (1), “Prob” represents probability level, “ΔP” represents actual loss of foreign exchange assets, “VaR” represents the maximum loss of financial institutions in the foreign exchange, confidence level. represents there’s a percent chance that the actual losses on foreign exchange assets is lower than the maximum loss. Normally, rule out other abnormal condition, the influence of a financial asset or portfolio, given a certain holding period in advance and the confidence interval of the condition, the calculation of VaR value are the biggest losses of the portfolio likely to happen. In other words, if the event is under the condition of the probability of , held at a given period, the biggest loss suffered by a financial asset or portfolio may be is not greater than calculated value of the VaR. VaR model is based on related assumptions, we should first inspect the premise before using the VaR model of VaR and test steps are as follows:
1) Must make sure the time series of financial assets is a random walk, which is the guarantee of the efficient operation of VaR calculation principle, or premise of VaR will not exist. The test for a random walk of the main financial assets time series methods including autocorrelation test, single root ADF test.
2) Test whether the inspection time sequence distribution of financial assets is normal distribution. Usually time series of financial assets distribution show rush fat-tailed, mostly distribution does not conform to the normal distribution assumption, in this case, people usually use GARCH model revise, in order to improve the accuracy of tail fitting and effect.
3) The copulas connect joint distribution test. Copulas connect function to connect the marginal distribution of financial assets of a multidimensional joint distribution of a variable joint cumulative distribution function with variable edge connects the cumulative distribution function function, describe the relationship between marginal distribution of financial assets [9] .
4) Calculate the maximum loss of financial assets VaR. On the basis of financial assets distribution GARCH model and copulas connect joint distribution function. We also need to test the model fitting degree, usually uses K-S inspection. Due to model integration is difficult, we usually adopt the monte carlo simulation method to calculate the final VaR value at risk. In this paper, matlab software simulate 10,000 times and 10,000 groups of yield can be calculated, and then put different proportion of foreign exchange assets in to calculate the risk va

Kết quả (Việt) 1:

TÓM TẮTSau khi tỷ giá hối đoái cải cách vào năm 2005, Trung Quốc đã chuyển đổi hệ thống tỷ giá hối đoái cố định vào một hệ thống tỷ giá hối đoái nổi bị chi phối bởi thị trường cung và cầu. Ngân hàng thương mại sẽ phải đối mặt với nguy cơ tỷ giá hối đoái. Vì vậy, làm thế nào để ước tính rủi ro tỷ giá hối đoái và giữ cho danh mục đầu tư tối ưu của ngoại hối là một chủ đề nghiên cứu quan trọng. Bài viết này chọn ngày của tỷ giá hối đoái RMB đối với đồng đô la và yên từ 1, tháng 1 năm 2008 đến ngày 21 tháng 1 năm 2012 như mẫu, mô tả sự phân bố chung của hai tài sản bằng cách sử dụng mô hình desu-Garch, do đó cuối cùng làm việc ra tỷ lệ tối ưu đang nắm giữ các tài sản nước ngoài thu hai tình huống nguy cơ tối thiểu.Từ khóa: trao đổi tỷ lệ rủi ro, chức năng desu, VaR 1. giới thiệuVới việc thành lập hệ thống tỷ giá hối đoái nổi tại Trung Quốc, số lượng nước ngoài tài sản được tổ chức bởi ngân hàng thương mại đang gia tăng, cũng như thường xuyên các cuộc khủng hoảng tài chính trên thị trường quốc tế, tỷ giá hối đoái rủi ro đã trở thành một nguồn quan trọng của các rủi ro tài chính của ngân hàng thương mại.Many domestic and foreign scholars have done the analysis of exchange rate risk, Galai Dan and Michel Crouhy believe that the reason for the existence of the exchange rate risk is foreign currency exposure, which cannot be eliminated through the financial derivatives. Huali Huang thinks that the value of the foreign exchange positions held by commercial banks as the foreign exchange market exchange rate volatility is the direct cause of commercial bank exchange rate risk, and changes in the exchange rate are mainly influenced by the foreign exchange market supply and demand [1] . According to the comprehensive research of scholars both at home and abroad, the author thinks that the fundamental cause of the exchange rate changes is the change of market supply and demand of foreign exchange, while changes of the relationship between supply and demand in the foreign exchange market are determined by each country’s domestic political and economic factors. The specific reasons are as follows: first, in economic transactions, there exists a foreign exchange exposure in the gap between financial assets and financial liabilities. Second, foreign exchange rate risks arise from international currency trading behavior of economic subject in the market. Third, the exchange rate risk and time factors are closely linked. From the intention of a deal to the end of the last transaction settlement, there are certain time intervals. Finally, exchange rate risk is derived from the multinational investment behavior of economic agents. Because of commercial banks for transnational investment, exchange rate changes will cause the bank value change, affect the company’s cash flow, as well as impact on the profitability of banks [2] -[4] .The key of prevention and control of the exchange rate risk is the effective measure of exchange rate risk. The VaR method is widely used at present. In the 90’s, foreign scholars began to research the VaR method, their research mainly includes the selection of VaR model, the scope of application of VaR, and the re-innovation of VaR model. In the measurement of VaR, assuming that yields obey a certain distribution is one of the earliest calculation methods of VaR parameter. But later it was found that most of the time series of financial assets showed a fat tail nature, not simply showing a normal distribution, and often extreme cases occur together, so the assumption is inconsistent with the earlier objective reality. Until 1982, Engle proposed autoregressive conditional heteroskedasticity model, referred to as the ARCH model, the model believes that the variance and conditional variances are not the same; the conditional variances are related to the error function before; this model can effectively solve the spike thick tail of financial assets time sequence model; this feature provides a new way to solve the heteroscedasticity. Robert F. Engle (1982) introduced the hypothesis and principle of each method that VaR based on, in the end, he used the monte carlo simulation method to calculate the end result based on different VaR method, and compared the result of the calculation. By comparison, the conclusion is as follows: for the present fat-tailed data, CVaR model is the best. Eric Bouye systematically introduces the application of copulas connect function in the financial sector, in addition to the common application of portfolio risk correlation, will also extends to value at risk (VaR) calculations, using copulas connect function further to analyze the credit risk and market risk [5] . Romnano using copulas connect function to calculate risk portfolio risk, he thinks that risks can be divided into individual risk and its structure risk associated with individual external structure though copulas connect function, the article also uses multivariate extremum function through monte carlo simulation to calculate market risk. Rosenberg and Schuermann used the VaR method to measure risk, and they compared the accuracy of several models to calculate the VaR values. The results showed that copula model was the best way of VaR calculation [6] .Min Chen, a domestic scholar, used the main calculation method of VaR model. She contrasts the pros and cons of various models by later accuracy test. The main calculation method she used includes the historical simulation method, the delta-normal method, etc. The adopted model includes the ARCH model and GARCH model. The final conclusion is that GARCH (1, 1) model based on the t distribution has a better fitting effect in commercial bank exchange rate risk accuracy measurement. Qing Ye uses semi-parametric model based on GARCH model, which is a good measure of risk in China’s mainland stock market fluctuation, and the semiparametric model based on GARCH model in the measurement of China’s stock market risk is more accurate by comparing the different models. Lujie Sun and Manying Bo combined copula function and the VaR method, and compared the traditional VaR model and the new model. Through the empirical study of the euro and the dollar portfolio, results showed that the VaR model based on copula function can more accurately measure the risk of exchange rate [7] [8] .2. Exchange Rate Risk Measurement Model Selection and Testing StepsRisk value method is the internationally accepted measure method of foreign exchange rate risk. In a certain period, determine a good confidence level in advance, the commercial bank asset portfolio value at risk showed the positions of the maximum possible losses. Holding period, the confidence level and risk factors is three factors the method must consider. The managers of commercial banks based on their risk tolerance, choose suitable holding period and the confidence level, and take factors that affect the risk of exchange rate volatility into the appropriate model, then get a possible maximum loss amount, which is the value at risk in the future.VaR method does not apply in financial markets when extreme abnormal fluctuations occurs such as political, military, culture, and some the long-term factors. The VaR approach as a short-term analysis tool is appropriate when financial market are stable.VaR English full name is the Value at Risk is often translated as “Value at Risk”. The statistical formula can be defined as: (1)In Equation (1), “Prob” represents probability level, “ΔP” represents actual loss of foreign exchange assets, “VaR” represents the maximum loss of financial institutions in the foreign exchange, confidence level. represents there’s a percent chance that the actual losses on foreign exchange assets is lower than the maximum loss. Normally, rule out other abnormal condition, the influence of a financial asset or portfolio, given a certain holding period in advance and the confidence interval of the condition, the calculation of VaR value are the biggest losses of the portfolio likely to happen. In other words, if the event is under the condition of the probability of , held at a given period, the biggest loss suffered by a financial asset or portfolio may be is not greater than calculated value of the VaR. VaR model is based on related assumptions, we should first inspect the premise before using the VaR model of VaR and test steps are as follows:1) Must make sure the time series of financial assets is a random walk, which is the guarantee of the efficient operation of VaR calculation principle, or premise of VaR will not exist. The test for a random walk of the main financial assets time series methods including autocorrelation test, single root ADF test.2) Test whether the inspection time sequence distribution of financial assets is normal distribution. Usually time series of financial assets distribution show rush fat-tailed, mostly distribution does not conform to the normal distribution assumption, in this case, people usually use GARCH model revise, in order to improve the accuracy of tail fitting and effect.3) The copulas connect joint distribution test. Copulas connect function to connect the marginal distribution of financial assets of a multidimensional joint distribution of a variable joint cumulative distribution function with variable edge connects the cumulative distribution function function, describe the relationship between marginal distribution of financial assets [9] .4) Calculate the maximum loss of financial assets VaR. On the basis of financial assets distribution GARCH model and copulas connect joint distribution function. We also need to test the model fitting degree, usually uses K-S inspection. Due to model integration is difficult, we usually adopt the monte carlo simulation method to calculate the final VaR value at risk. In this paper, matlab software simulate 10,000 times and 10,000 groups of yield can be calculated, and then put different proportion of foreign exchange assets in to calculate the risk va