Study On Financial Market Risk Measurement And Hedging Based On Copula Function-asymmetric Laplace Distribution

With the development of financial globalization and the increasing complexity offinancial markets, and to prevent financial risk had become the consensus of the wholesociety. To strengthen the risk prevention and management capabilities of the financialsystem and to improve the ability of market transfer, digestion and absorption of risk, wereimportant guarantee for the healthy growth and development of our financial market. Withthe continuing changing of financial operating mechanism and environment, the financialrisk’s generation, dissemination, control and management have become increasinglycomplex. And the study on financial market risk measurement and management hasbecome more important and complex. Market risk was the most common and the mainrisk faced by financial institutions. However, traditional research methods based on themodel of normality, linearity or symmetry of volatility were no longer applicable. Becausethese were difficult to fully capture the market risk information, which needed moreconstantly researches, and given more theoretical and empirical researches to adaptmorden risk management requirement.This paper mainly studied the financial market risk measurement and management.Based on analysing the modern theory of financial risk management, it summarized theresearch of market risk measurement and futures hedging, and pointed out the lack ofexisting research. For the complexity of financial market risk, it established riskmeasurement models and hedging strategies models which were based on the non-normaldistribution method and non-linear correlation model, then studied the method of financialmarket risk measurement and hedging. The main parts of the research carried out mainlyfrom the following four aspects:(1) In this paper, Asymmetric Laplace distribution was used to fit the data of assetreturns and described the features of market risk. Then, it provided AL parametric method and AL-MC method of measuring VaR and CVaR. Selected the Shanghai Composite Index,Nikkei225Stock Index and S&P500Index, it given the calculation of VaR and CVaRconsidering the actual stocks risk features, and also given the back testing and accuracyassessment of risk. The results showed that the risk measurement model based onAsymmetric Laplace distribution was reasonable and applicable, and can effectivelyestimated the market risk.(2) In this paper, the ARMA-GJR-AL model was established to describe the featuresof market risk considering the correlation, volatility and innovation distribution. Based onthe financial risk measurement toll VaR/CVaR and the theories of mathematical statistics,it studied the dynamic VaR and CVaR of market risk under Asymmetric Laplacedistribution and given the tests of accurate measurement. Selected the ShanghaiComposite Index and New York Composite Index from the year of2005to2009asobserved samples, it established ARMA (1,1)-GJR (1,1)-AL and ARMA (1,1)-GJR(1,1)-N model to capture the markets’ risk characteristics, got the model parametersestimation by using Matlab software program and given the prediction and test of dailyVaR and CVaR for the year of2010. The results showed that the dynamic riskmeasurement model based on AL distribution was more reasonable and applicable, andcan effectively predicted risk. Finally, it further analyzed the stock market risk.(3) This paper used AL distribution to describe the marginal distributions’ features,combined with Copula function technique to describe the relationship between assets andstudied the VaR and CVaR of market portfolio and their allocation. At the same time, itgiven the comparative study on commonly used measurement method based onmultivariate statistical distribution and risk allocation method based on OLS model. Theauthor calculated the portfolio risk and their allocation with portfolio of ShanghaiComposite Index and Shenzhen Component Index. The results showed that the methods ofVaR and CVaR which based on t-Copula-AL model are more simple and precise, and itcould easily calculate risk allocation. (4) Used parametric and non-parametric distribution to describe the marginaldistributions’ features and combined Copula function technique to describe the correlationbetween them, this paper took CVaR risk minimization as the objective function andestablished an optimal hedging ratio model based on constant and dynamic Copula-CVaR.Selected the recent spot and futures of IS300as samples，it established constant anddynamic Copula-CVaR and OLS model, then analyzed the hedging cost and givencomparative analysis of the amendment-cost-hedging-efficiency for each model in acertain hedging term. When considering the hedging cost, the results showed that investorsshould choose a simple static hedging strategy and should select the optimal hedgingstrategy based on their actual cost conditions even under the same market conditions.This paper had great theoretical significance and practical value. It promoted theresearch of financial market risk measurement, futures hedging, AL distribution andCopula function theory and so on. At the same time, it would play great help and referencein practice activities, such as the investment decision-making, economic capitalmanagement and risk management and so on.