Empirical Analysis Of VaR And CTE Risk Measurement Theory

In the context of economic globalization, as the economy development rapidly, more and more financial products are increasingly take the worldwide attention. Then researching the risk measurement methods and combinatorial optimization problems of securities portfolio can effectively guard against financial risks and also play an extremely important role in the stability of the financial order. Now, the methods of financial risk measurement are the most comprehensive market risk measurement techniques. The risk measurement methods of has been widely applied in asset portfolio and the measure of market risk. But there are certain deficiencies. In VaR method, to make up for the defects of VaR, the scholars at home and abroad proposed CTE (Conditional Tail Expectation) model.CTE has been expanded complement the value-at-risk (VaR), and it also better description that risk expectation over VaR Method portion is effective. This paper mainly studies the risk VaR and tail conditions expectations CTE which use the method of TARCH-M, and also has been empirical analysis the constrained portfolio model of VaR and CTE.This article discusses the link between VaR and CTE from a whole new angle, and also analyzes the portfolio model based on VaR and CTE combined with the graphical. Firstly, We select the closing price of SSE180Index from July1,2002to April27-2012, then taking the logarithm. After taking logarithms and first-order differential treatment we use TARCH-M model to measure the risk of VaR and CTE. Combine the Monte Carlo simulation method to conclude concrete values of VaR and CTE from November25,2011to April27,2012, thus the stock risk of this period is more specific. It also shows that CTE method is more accurate than VaR when measure risk. This article mainly includes following aspects:The first chapter is the introduction. This chapter propose the the background and significance.and it summary the development process and research results of VaR and CTE method. Then, it makes a simple overview of the development of VaR and CTE in domestic and foreign. Finally, We introduce the main research content, research methods, innovation and shortcomings of this paper. The second chapter is theoretical knowledge of VaR.The chapter described in detail the definition of the VaR calculation principles and formulas. And we make a simple introduction of the three mainstream VaR methods. Finally we pointe out the advantages and disadvantages of VaR.The third chapter is the theoretical knowledge of the CTE. Firstly, we introduce the definition、calculation principles and calculation methods of CTE. Secondly, we make a comparative of the strengths and weaknesses the VaR Method and CTE. Finally, we point out the advantages and disadvantages of the CTE method.The fourth chapter Introduce the relationship between TARCH-M model of VaR and CTE, and to make risk management become a strong guarantee for our future financial investment.The role ofthis section isconnecting between the preceding and the following. On the bases of understanding the TARCH-M model, we introduce the relationship among TARCH-M、VaR and the CTE to facilitate the application of the empirical part.The fifth chapter is empirical analysis. This section will select the SSE180Index and use the model of TARCH-M to calculator and predict the VaR and CTE Of stock index day volatility.then, we can predict the size of its risk. We also calculate the values of VaR and CTE by the method of Monte Carlo simulation.At last, the paper came to the conclusion that the CTE is more effective than the VaR method to predict the risk.The sixth chapter is the summary and outlook for the full text. This chapter introduces the problems that may exist in the empirical study of the full text of the risk management, VaR and CTE. Further, we should find the appropriate improvement direction. The1outlook part points that we should grasp the full range of risk.This paper use the knowledge of statistics, finance and econometrics to analysis the contents that we researched, and we can reach a better expression of the article content.