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 of financial markets, and to prevent financial risk had become the consensus of the whole society. To strengthen the risk prevention and management capabilities of the financial system and to improve the ability of market transfer, digestion and absorption of risk, were important guarantee for the healthy growth and development of our financial market. With the continuing changing of financial operating mechanism and environment, the financial risk’s generation, dissemination, control and management have become increasingly complex. And the study on financial market risk measurement and management has become more important and complex. Market risk was the most common and the main risk faced by financial institutions. However, traditional research methods based on the model of normality, linearity or symmetry of volatility were no longer applicable. Because these were difficult to fully capture the market risk information, which needed more constantly researches, and given more theoretical and empirical researches to adapt morden 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 the research of market risk measurement and futures hedging, and pointed out the lack of existing research. For the complexity of financial market risk, it established risk measurement models and hedging strategies models which were based on the non-normal distribution method and non-linear correlation model, then studied the method of financial market risk measurement and hedging. The main parts of the research carried out mainly from the following four aspects:(1) In this paper, Asymmetric Laplace distribution was used to fit the data of asset returns 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 CVaR considering the actual stocks risk features, and also given the back testing and accuracy assessment of risk. The results showed that the risk measurement model based on Asymmetric Laplace distribution was reasonable and applicable, and can effectively estimated the market risk.(2) In this paper, the ARMA-GJR-AL model was established to describe the features of market risk considering the correlation, volatility and innovation distribution. Based on the financial risk measurement toll VaR/CVaR and the theories of mathematical statistics, it studied the dynamic VaR and CVaR of market risk under Asymmetric Laplace distribution and given the tests of accurate measurement. Selected the Shanghai Composite Index and New York Composite Index from the year of2005to2009as observed 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 parameters estimation by using Matlab software program and given the prediction and test of daily VaR and CVaR for the year of2010. The results showed that the dynamic risk measurement model based on AL distribution was more reasonable and applicable, and can 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 and studied the VaR and CVaR of market portfolio and their allocation. At the same time, it given the comparative study on commonly used measurement method based on multivariate statistical distribution and risk allocation method based on OLS model. The author calculated the portfolio risk and their allocation with portfolio of Shanghai Composite Index and Shenzhen Component Index. The results showed that the methods of VaR and CVaR which based on t-Copula-AL model are more simple and precise, and it could easily calculate risk allocation. (4) Used parametric and non-parametric distribution to describe the marginal distributions’ features and combined Copula function technique to describe the correlation between them, this paper took CVaR risk minimization as the objective function and established an optimal hedging ratio model based on constant and dynamic Copula-CVaR. Selected the recent spot and futures of IS300as samples, it established constant and dynamic Copula-CVaR and OLS model, then analyzed the hedging cost and given comparative analysis of the amendment-cost-hedging-efficiency for each model in a certain hedging term. When considering the hedging cost, the results showed that investors should choose a simple static hedging strategy and should select the optimal hedging strategy based on their actual cost conditions even under the same market conditions.This paper had great theoretical significance and practical value. It promoted the research of financial market risk measurement, futures hedging, AL distribution and Copula function theory and so on. At the same time, it would play great help and reference in practice activities, such as the investment decision-making, economic capital management and risk management and so on.